Tomorrow is my PSM practica exam. Examiner will surely ask from this pdf and i dont know anything about them and i have no concept of this . please help me to understand this topic in very easy language and remember all of them so i will secure better marks

Reading File

Running Command

python -m markitdown "/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Pre-testing, Validity, and Reliability _BPH.pptx" 2>&1 | head -300

Running Command

python3 -c " import pdfplumber files = [ '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf', '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Choosing_Appropriate_Statistical_Methods_BPH1_eb2b51ba_a163_4467.pdf', '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Citation_and_References_updated_e1fa4c04_e418_4020_bfec_8014f8eede80.pdf' ] for f in files: print('='*80) print('FILE:', f.split('/')[-1]) print('='*80) with pdfplumber.open(f) as pdf: for i, page in enumerate(pdf.pages): text = page.extract_text() if text: print(f'--- Page {i+1} ---') print(text[:3000]) if i >= 15: print('... (truncated at page 16)') break " 2>&1

Running Command

python3 -c " from pptx import Presentation pptx_path = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Pre-testing, Validity, and Reliability _BPH.pptx' prs = Presentation(pptx_path) for i, slide in enumerate(prs.slides): print(f'=== SLIDE {i+1} ===') for shape in slide.shapes: if hasattr(shape, 'text') and shape.text.strip(): print(shape.text) print() " 2>&1

Running Command

python3 -c " import pdfplumber # Get remaining pages of all PDFs files = [ ('/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf', 'LIT_REVIEW'), ('/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Choosing_Appropriate_Statistical_Methods_BPH1_eb2b51ba_a163_4467.pdf', 'STATS'), ('/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Citation_and_References_updated_e1fa4c04_e418_4020_bfec_8014f8eede80.pdf', 'CITATION'), ] for fpath, label in files: with pdfplumber.open(fpath) as pdf: total = len(pdf.pages) print(f'{label}: {total} pages total') # Get pages 16 onwards for i in range(16, min(total, 35)): page = pdf.pages[i] text = page.extract_text() if text and text.strip(): print(f'--- {label} Page {i+1} ---') print(text[:2000]) " 2>&1

Running Command

# Extract PPTX using python-pptx installed differently pip3 show python-pptx 2>&1 | head -5 ls /usr/local/lib/python3*/dist-packages/ 2>/dev/null | grep pptx python3 -c "import importlib; print([m for m in dir(importlib) if 'find' in m.lower()])" find /usr/local/lib -name "*.dist-info" -type d 2>/dev/null | xargs -I{} basename {} | grep -i pptx 2>/dev/null | head find /home -name "*.egg-info" 2>/dev/null | head

Running Command

python3 -c " import pdfplumber # Get ALL lit review pages fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf' with pdfplumber.open(fpath) as pdf: total = len(pdf.pages) print(f'Total pages: {total}') for i in range(total): page = pdf.pages[i] text = page.extract_text() if text and text.strip(): print(f'--- Page {i+1} ---') print(text[:2500]) " 2>&1

Running Command

# Try extracting PPTX as ZIP python3 -c " import zipfile, os pptx_path = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Pre-testing, Validity, and Reliability _BPH.pptx' with zipfile.ZipFile(pptx_path, 'r') as z: slide_files = sorted([f for f in z.namelist() if f.startswith('ppt/slides/slide') and not 'rels' in f]) print('Slides found:', len(slide_files)) for sf in slide_files[:30]: content = z.read(sf).decode('utf-8', errors='ignore') # Extract text between <a:t> tags import re texts = re.findall(r'<a:t[^>]*>([^<]+)</a:t>', content) slide_num = sf.split('slide')[-1].split('.')[0] combined = ' | '.join([t.strip() for t in texts if t.strip()]) if combined: print(f'SLIDE {slide_num}: {combined}') " 2>&1

Running Command

python3 -c " import zipfile, re pptx_path = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Pre-testing, Validity, and Reliability _BPH.pptx' with zipfile.ZipFile(pptx_path, 'r') as z: slide_files = sorted([f for f in z.namelist() if f.startswith('ppt/slides/slide') and not 'rels' in f]) for sf in slide_files: content = z.read(sf).decode('utf-8', errors='ignore') texts = re.findall(r'<a:t[^>]*>([^<]+)</a:t>', content) slide_num = re.search(r'slide(\d+)\.xml', sf).group(1) combined = ' | '.join([t.strip() for t in texts if t.strip()]) if combined: print(f'=== SLIDE {slide_num} ===') print(combined) print() " 2>&1

Running Command

python3 -c " import pdfplumber # Get ALL lit review text fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf' fpath2 = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf' with pdfplumber.open(fpath2) as pdf: for i, page in enumerate(pdf.pages): text = page.extract_text() if text and text.strip(): print(f'--- Page {i+1} ---') print(text) " 2>&1

Done! I've read all 4 of your files and made complete, easy notes. Here's a summary of what I covered:

What's in your notes above:

📁 File 1 - Pre-testing, Validity & Reliability (PPTX)

Pre-testing: what it is, 2 methods (Focus Groups vs Respondent Interviews), pilot study
4 Types of Validity: Face → Content → Construct (Convergent + Discriminant) → Criterion (Concurrent + Predictive) - all with examples from your slides
5 Methods of Reliability: Test-Retest, Inter-rater (Kappa), Internal Consistency (Cronbach's α), Split-Half, Parallel Forms
The classic Validity vs Reliability bullseye diagram explained in words

📁 File 2 - Choosing Statistical Methods

Variable types, parametric vs non-parametric
Normality tests (Shapiro-Wilk vs Kolmogorov-Smirnov)
The full statistical test selection table (t-test, ANOVA, Mann-Whitney, Chi-square, Fisher's, etc.)
Chi-square vs Fisher's exact criteria
Confounding, interaction effects, p-hacking, clinical vs statistical significance

📁 File 3 - Citation & References

Vancouver style (most important for MBBS/PSM) with format and examples
APA style format and examples
Bibliography vs References
Citation software

📁 File 4 - Literature Review

Purpose, table template format, the assignment

The mnemonics at the end will help you remember everything quickly. Best of luck tomorrow - you've got this! 💪

Along with all also includes methods/ steps of literature review

Running Command

python3 -c " import pdfplumber fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf' with pdfplumber.open(fpath) as pdf: total = len(pdf.pages) print(f'Total pages: {total}') for i in range(total): page = pdf.pages[i] # Try extracting all text including tables text = page.extract_text(layout=True) tables = page.extract_tables() print(f'=== PAGE {i+1} ===') if text and text.strip(): print('TEXT:', text[:3000]) if tables: print('TABLES:') for t in tables: for row in t: print(row) print() " 2>&1

Running Command

python3 -c " import pdfplumber from PIL import Image import io, os fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Literature_Review_2025_MBBS_Revised_5194a3a4_829a_4a86_9275_0ec0d690273d.pdf' outdir = '/home/daytona/workspace/litreview_pages' os.makedirs(outdir, exist_ok=True) with pdfplumber.open(fpath) as pdf: for i, page in enumerate(pdf.pages): img = page.to_image(resolution=150) img.save(f'{outdir}/page_{i+1:02d}.png') print(f'Saved page {i+1}') print('Done') " 2>&1

Reading File

I now have all the content. Here is the complete Literature Review section - fully extracted from your PDF slides, in easy language:

📖 LITERATURE REVIEW - COMPLETE NOTES

(From your Literature Review PDF - every slide read!)

🔍 What is a Literature Review?

"A literature review is a survey of scholarly sources on a specific topic."

It provides an overview of current knowledge, allowing you to:

Identify relevant theories
Identify methods already used
Find gaps in existing research
Apply findings to your own research or thesis

🎯 Purpose of Literature Review (5 purposes)

#	Purpose	Plain meaning
1	Demonstrate your familiarity with the topic	Show you know what has been done
2	Develop a theoretical framework and methodology	Build the base for your research
3	Position your work in relation to other researchers	Show where YOUR study fits
4	Show how your research addresses a gap or contributes to a debate	Justify why your study is needed
5	Evaluate the current state of research and demonstrate knowledge of scholarly debates	Show you understand what is argued and what is still unknown

📋 5 KEY STEPS to Writing a Literature Review

Your slide says exactly these 5 steps (this is a very likely exam question!):

Step 1: Search for relevant literature
Step 2: Evaluate sources
Step 3: Identify themes, debates, and gaps
Step 4: Outline the structure
Step 5: Write your literature review

Mnemonic: "S-E-I-O-W" → "Some Elephants In Our World"

STEP 1: Search for Relevant Literature

Brainstorming before searching - ask yourself:

What to search?
Where to search?
How to search?
Who will help?
Who is the expert in your department, hospital, or locally?
What basic knowledge is needed?

Where to search (Search Engines):

Engine	Type
PubMed	Best for medical/biomedical
Google Scholar	Broad academic search
Scopus	International peer-reviewed journals

Sources to search:

Online OR Offline
From: Journal articles, Book chapters, Conference papers, Reports, Reviews

🔑 Boolean Search (Very Important!)

Boolean operators help you refine your search. Your slides have 5 operators:

Operator	Effect	Example	Result
AND	Narrows results (both words must be present)	Fever AND Joint Pain	Only articles with BOTH topics
OR	Broadens results (either word)	Fever OR Joint Pain	Articles with ANY of the topics
NOT	Narrows/excludes	Typhoid fever NOT children	Typhoid fever - but NOT in children
( )	Groups terms	(Fever AND joint pain) OR chills	Controls order of operations
" "	Exact phrase	"Joint Pain"	Finds exact phrase together

Quick memory: AND = smaller (stricter), OR = bigger (broader), NOT = removes

From your slides - real examples:

Fever AND Joint Pain → 4,432 results
Fever AND "Joint Pain" → 780 results (exact phrase, fewer)
(Fever AND joint pain) OR chills → 16,774 results (more because OR)
Typhoid fever NOT children → 10,758 results (removed children papers)

STEP 2: Evaluate and Select Sources

Ask these 8 "Wh" questions (from your slide) when reading each article:

What question or problem is the author addressing?
What are the key concepts and how are they defined?
What are the key theories, models, and methods?
Does the research use established frameworks or take an innovative approach?
What are the results and conclusions of the study?
How does the publication relate to other literature in the field?
Does it confirm, add to, or challenge established knowledge?
What are the strengths and weaknesses of the research?

Remember: Take notes and cite your sources (APA or Vancouver Style)

STEP 3: Identify Themes, Debates, and Gaps

After reading articles, group them by:

Common themes (what do many studies agree on?)
Debates (what do studies disagree on?)
Gaps (what has NOT been studied yet? - this justifies YOUR study)

STEP 4: Outline the Structure

4 types of structure (from your slides):

Structure Type	Meaning
Chronological	Organize by time order - oldest to newest studies
Thematic (Qualitative Analysis)	Organize by themes/topics
Methodological	Organize by research methods used
Theoretical	Organize by different theories

Which to use?

Most common in PSM research: Thematic structure

STEP 5: Write Your Literature Review

3 parts of a literature review:

Introduction:

Clearly establish the focus and purpose of the review
State what the review will cover

Body:

Summarize and synthesize the articles
Analyze and interpret the findings
Critically evaluate the sources
Write in well-structured paragraphs

Conclusion:

Summarize key findings from the literature
Emphasize their significance to your research

📊 Conceptual Framework

What is a Conceptual Framework?

A visual/diagram that shows the relationships between variables in your study.

Purpose of Conceptual Framework:

To clarify concepts and propose relationships among concepts in a study
To explain observations
To provide context for interpreting study findings
To encourage theory development useful to practice

Example from your slides: (Adolescent Reproductive Health Wellness Program)

Demographics (Age, Civil Status, Education, Family order, Economic status) → Self-Esteem (High/Moderate/Low) → Parenting Styles (Authoritative/Democratic/Permissive)

📝 Literature Review Summary Table (Template)

Your slides show this exact table format - know these columns!

Author(s) & Year	Title	Objective	Methodology	Key Findings	Conclusion
Author 1 et al., YYYY	Title of article	Briefly state aim	Summarize methods (survey, experiment)	Highlight significant results	Main takeaway/implications

Real example from your slides (Tobacco topic):

Author	Title	Objective	Method	Key Findings	Conclusion
Wakefield et al., 2010	Impact of tobacco advertising bans	Assess effect of tobacco ad bans	Systematic review	Comprehensive bans reduced smoking especially in youth	Complete advertising bans critical for tobacco control
Jha & Peto, 2014	Global effects of smoking, quitting, taxing	Examine smoking cessation impact	Epidemiological analysis	Tax increases + cessation programs reduced mortality	Higher tobacco taxes are cost-effective
Sinha et al., 2012	Tobacco use among youth in India	Analyze tobacco use patterns	Cross-sectional (Global Youth Tobacco Survey)	Peer pressure + lack of awareness = youth tobacco use	Awareness campaigns targeting youth needed

⚡ QUICK SUMMARY - LITERATURE REVIEW

Definition → Survey of scholarly sources on a specific topic

Purpose (5) → Familiarity, Framework, Position work, 
              Address gaps, Evaluate current state

5 Steps → Search → Evaluate → Identify themes → Outline → Write

Search engines → PubMed, Google Scholar, Scopus

Boolean operators → AND (narrows), OR (broadens), NOT (excludes)

Structure types → Chronological, Thematic, Methodological, Theoretical

Writing format → Introduction + Body + Conclusion

Table columns → Author/Year, Title, Objective, Methodology, 
                Key Findings, Conclusion

🧠 MNEMONICS FOR LITERATURE REVIEW

5 Steps: "Some Elephants In Our World" = Search, Evaluate, Identify, Outline, Write
Boolean operators: "And Or Not" = AND narrows, OR opens, NOT removes (AON)
4 Structure types: "Cows Think More Than" = Chronological, Thematic, Methodological, Theoretical
Table columns: "A-T-O-M-K-C" = Author, Title, Objective, Methodology, Key findings, Conclusion

So now your complete notes cover ALL 4 topics. The examiner is most likely to ask you to:

Name and explain the 5 steps of a literature review - you have all 5 with details
What is Boolean search? Give examples - AND/OR/NOT all explained
What are the types of structure? - Chronological, Thematic, Methodological, Theoretical
Draw/describe the table template for summarizing literature
What is a conceptual framework and its purpose? - all 4 purposes ready

Good luck tomorrow! 🎯

Create a study guide PDF combining all 4 topics

Running Command

ls -la /home/daytona/workspace/ 2>&1 | head -20

Running Command

mkdir -p /home/daytona/workspace/psm-study-guide && python3 -c "from reportlab.lib.pagesizes import A4; print('reportlab OK')" 2>&1

Writing File

~/psm-study-guide/build_pdf.py

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.units import cm, mm
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.platypus.flowables import BalancedColumns
from reportlab.lib.colors import HexColor, white, black

OUTPUT = "/home/daytona/workspace/psm-study-guide/PSM_Study_Guide.pdf"

# ─── Colour palette ───────────────────────────────────────────
C_DARK_BLUE   = HexColor("#1a3557")   # headings / title bg
C_MID_BLUE    = HexColor("#2563a8")   # section banners
C_LIGHT_BLUE  = HexColor("#dbeafe")   # section bg tint
C_ACCENT      = HexColor("#e67e22")   # orange accent / box
C_ACCENT_LIGHT= HexColor("#fef3c7")   # soft yellow boxes
C_GREEN       = HexColor("#166534")   # green text
C_GREEN_LIGHT = HexColor("#dcfce7")   # green box bg
C_RED_LIGHT   = HexColor("#fee2e2")   # warning box bg
C_RED         = HexColor("#991b1b")
C_GRAY_BG     = HexColor("#f1f5f9")   # table alt rows
C_GRAY_LINE   = HexColor("#cbd5e1")
C_WHITE       = white
C_BLACK       = black

W, H = A4

# ─── Styles ───────────────────────────────────────────────────
def make_styles():
    base = getSampleStyleSheet()

    def ps(name, parent="Normal", **kw):
        return ParagraphStyle(name, parent=base[parent], **kw)

    return {
        "title_main": ps("title_main", "Title",
            fontName="Helvetica-Bold", fontSize=26, textColor=C_WHITE,
            alignment=TA_CENTER, spaceAfter=4),
        "title_sub": ps("title_sub",
            fontName="Helvetica", fontSize=12, textColor=HexColor("#bfdbfe"),
            alignment=TA_CENTER, spaceAfter=2),
        "title_date": ps("title_date",
            fontName="Helvetica-Oblique", fontSize=10, textColor=HexColor("#93c5fd"),
            alignment=TA_CENTER),

        "h1": ps("h1", fontName="Helvetica-Bold", fontSize=15,
            textColor=C_WHITE, spaceAfter=2, spaceBefore=4),
        "h2": ps("h2", fontName="Helvetica-Bold", fontSize=12,
            textColor=C_DARK_BLUE, spaceAfter=3, spaceBefore=8),
        "h3": ps("h3", fontName="Helvetica-Bold", fontSize=10,
            textColor=C_MID_BLUE, spaceAfter=2, spaceBefore=5),
        "body": ps("body", fontName="Helvetica", fontSize=9,
            leading=14, spaceAfter=3, spaceBefore=1),
        "body_bold": ps("body_bold", fontName="Helvetica-Bold", fontSize=9,
            leading=14, spaceAfter=3),
        "bullet": ps("bullet", fontName="Helvetica", fontSize=9,
            leading=13, spaceAfter=2, leftIndent=12, firstLineIndent=-8),
        "mnemonic": ps("mnemonic", fontName="Helvetica-Bold", fontSize=9,
            textColor=C_GREEN, leading=13, spaceAfter=2, leftIndent=8),
        "caption": ps("caption", fontName="Helvetica-Oblique", fontSize=8,
            textColor=HexColor("#64748b"), alignment=TA_CENTER),
        "th": ps("th", fontName="Helvetica-Bold", fontSize=8,
            textColor=C_WHITE, alignment=TA_CENTER, leading=10),
        "td": ps("td", fontName="Helvetica", fontSize=8,
            textColor=C_BLACK, leading=10),
        "td_c": ps("td_c", fontName="Helvetica", fontSize=8,
            textColor=C_BLACK, alignment=TA_CENTER, leading=10),
        "td_bold": ps("td_bold", fontName="Helvetica-Bold", fontSize=8,
            textColor=C_DARK_BLUE, leading=10),
        "box_title": ps("box_title", fontName="Helvetica-Bold", fontSize=10,
            textColor=C_DARK_BLUE, spaceAfter=2),
        "box_body": ps("box_body", fontName="Helvetica", fontSize=8.5,
            leading=13, spaceAfter=2),
        "toc": ps("toc", fontName="Helvetica", fontSize=10,
            leading=16, spaceAfter=0),
        "toc_bold": ps("toc_bold", fontName="Helvetica-Bold", fontSize=10,
            textColor=C_MID_BLUE, leading=16),
    }

S = make_styles()

# ─── Helper builders ──────────────────────────────────────────

def cover_block():
    """Returns a Table that acts as a full-width cover banner."""
    inner = [
        Paragraph("PSM PRACTICA", S["title_main"]),
        Paragraph("COMPLETE STUDY GUIDE", ParagraphStyle("ts2",
            fontName="Helvetica-Bold", fontSize=18, textColor=HexColor("#fbbf24"),
            alignment=TA_CENTER, spaceAfter=6)),
        Paragraph("Pre-testing · Validity &amp; Reliability · Statistical Methods · Literature Review · Citation", S["title_sub"]),
        Spacer(1, 6),
        Paragraph("June 2026  |  MBBS / BPH Examination Preparation", S["title_date"]),
    ]
    t = Table([[inner]], colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), C_DARK_BLUE),
        ("ROUNDEDCORNERS", [8]),
        ("TOPPADDING",    (0,0), (-1,-1), 28),
        ("BOTTOMPADDING", (0,0), (-1,-1), 28),
        ("LEFTPADDING",   (0,0), (-1,-1), 20),
        ("RIGHTPADDING",  (0,0), (-1,-1), 20),
    ]))
    return t

def section_banner(text, num):
    p = Paragraph(f"<b>TOPIC {num} &nbsp;|&nbsp; {text}</b>", S["h1"])
    t = Table([[p]], colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), C_MID_BLUE),
        ("TOPPADDING",    (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("LEFTPADDING",   (0,0), (-1,-1), 14),
    ]))
    return t

def info_box(title, items, bg=C_LIGHT_BLUE, title_col=C_DARK_BLUE):
    content = [Paragraph(f"<b>{title}</b>", ParagraphStyle("bxt",
        fontName="Helvetica-Bold", fontSize=9.5, textColor=title_col, spaceAfter=4))]
    for item in items:
        content.append(Paragraph(f"• {item}", S["bullet"]))
    t = Table([[content]], colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("TOPPADDING",    (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("LEFTPADDING",   (0,0), (-1,-1), 12),
        ("RIGHTPADDING",  (0,0), (-1,-1), 12),
        ("ROUNDEDCORNERS", [6]),
    ]))
    return t

def mnemonic_box(trick, bg=C_GREEN_LIGHT):
    content = [
        Paragraph("🧠 MNEMONIC", ParagraphStyle("mn_h", fontName="Helvetica-Bold",
            fontSize=9, textColor=C_GREEN, spaceAfter=3)),
        Paragraph(trick, ParagraphStyle("mn_b", fontName="Helvetica", fontSize=9,
            leading=13, textColor=C_GREEN)),
    ]
    t = Table([[content]], colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("TOPPADDING",    (0,0), (-1,-1), 7),
        ("BOTTOMPADDING", (0,0), (-1,-1), 7),
        ("LEFTPADDING",   (0,0), (-1,-1), 12),
        ("RIGHTPADDING",  (0,0), (-1,-1), 12),
        ("ROUNDEDCORNERS", [6]),
    ]))
    return t

def exam_tip_box(text):
    content = [
        Paragraph("⭐ EXAM TIP", ParagraphStyle("et_h", fontName="Helvetica-Bold",
            fontSize=9, textColor=C_RED, spaceAfter=3)),
        Paragraph(text, ParagraphStyle("et_b", fontName="Helvetica", fontSize=9,
            leading=13, textColor=HexColor("#7f1d1d"))),
    ]
    t = Table([[content]], colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), C_RED_LIGHT),
        ("TOPPADDING",    (0,0), (-1,-1), 7),
        ("BOTTOMPADDING", (0,0), (-1,-1), 7),
        ("LEFTPADDING",   (0,0), (-1,-1), 12),
        ("RIGHTPADDING",  (0,0), (-1,-1), 12),
        ("ROUNDEDCORNERS", [6]),
    ]))
    return t

def simple_table(headers, rows, col_widths=None):
    if col_widths is None:
        col_widths = [(W - 4*cm) / len(headers)] * len(headers)
    header_row = [Paragraph(h, S["th"]) for h in headers]
    data = [header_row]
    for i, row in enumerate(rows):
        bg = C_GRAY_BG if i % 2 == 0 else C_WHITE
        data.append([Paragraph(str(c), S["td_c"] if j > 0 else S["td"]) for j, c in enumerate(row)])
    t = Table(data, colWidths=col_widths, repeatRows=1)
    style = [
        ("BACKGROUND",   (0, 0), (-1, 0), C_DARK_BLUE),
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        ("TOPPADDING",   (0, 0), (-1, -1), 5),
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        ("VALIGN",       (0, 0), (-1, -1), "MIDDLE"),
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    t.setStyle(TableStyle(style))
    return t

def sp(n=1): return Spacer(1, n * 4)
def hr(): return HRFlowable(width="100%", thickness=0.5, color=C_GRAY_LINE, spaceAfter=4, spaceBefore=4)
def b(text): return f"<b>{text}</b>"

# ══════════════════════════════════════════════════════════════
# BUILD DOCUMENT
# ══════════════════════════════════════════════════════════════
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=2*cm, rightMargin=2*cm,
    topMargin=1.8*cm, bottomMargin=2*cm,
    title="PSM Practica Study Guide",
    author="Exam Prep Notes",
)

story = []

# ─── COVER ────────────────────────────────────────────────────
story.append(Spacer(1, 1.5*cm))
story.append(cover_block())
story.append(Spacer(1, 0.6*cm))

# Table of Contents
toc_data = [
    [Paragraph("<b>Topic</b>", S["th"]), Paragraph("<b>Subject</b>", S["th"])],
    [Paragraph("1", S["td_c"]), Paragraph("Pre-testing, Pilot Study", S["td"])],
    [Paragraph("2", S["td_c"]), Paragraph("Validity (Face, Content, Construct, Criterion)", S["td"])],
    [Paragraph("3", S["td_c"]), Paragraph("Reliability (Test-Retest, Inter-rater, Internal Consistency, Split-Half, Parallel)", S["td"])],
    [Paragraph("4", S["td_c"]), Paragraph("Choosing Appropriate Statistical Methods", S["td"])],
    [Paragraph("5", S["td_c"]), Paragraph("Literature Review – Steps, Boolean Search, Structure", S["td"])],
    [Paragraph("6", S["td_c"]), Paragraph("Citation & References (Vancouver, APA, Bibliography)", S["td"])],
    [Paragraph("7", S["td_c"]), Paragraph("Quick Revision Tables & Mnemonics", S["td"])],
]
toc_t = Table(toc_data, colWidths=[1.2*cm, W - 4*cm - 1.2*cm])
toc_t.setStyle(TableStyle([
    ("BACKGROUND",    (0,0), (-1,0), C_DARK_BLUE),
    ("ROWBACKGROUNDS",(0,1), (-1,-1), [C_GRAY_BG, C_WHITE]),
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    ("LEFTPADDING",   (0,0), (-1,-1), 8),
    ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
]))
story.append(Paragraph("Contents", S["h2"]))
story.append(toc_t)
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 1 – PRE-TESTING & PILOT STUDY
# ══════════════════════════════════════════════════════════════
story.append(section_banner("PRE-TESTING &amp; PILOT STUDY", 1))
story.append(sp(2))

story.append(Paragraph("What is Pre-testing?", S["h2"]))
story.append(Paragraph(
    "Pre-testing means <b>testing your questionnaire BEFORE the real study starts</b> to find problems, "
    "confusing questions, or design flaws — so you can fix them early.",
    S["body"]))
story.append(sp())

story.append(info_box("Benefits of Pre-testing", [
    "Detect potential problems with the survey → reduces errors in data gathering",
    "Predict how participants will respond (future recruitment problems, response rates)",
    "Refine research — fix time estimates and appropriate methods of data collection",
], bg=C_LIGHT_BLUE))
story.append(sp(2))

story.append(Paragraph("Challenges of Pre-testing", S["h3"]))
story.append(Paragraph("Survey design difficulties, staff training issues, and logistical challenges that may occur during data collection.", S["body"]))
story.append(sp())

story.append(Paragraph("Pre-testing Methods", S["h2"]))
story.append(simple_table(
    ["Method", "What happens", "Pro ✅", "Con ❌"],
    [
        ["Focus Groups", "Small group discussions with community members where study will be done",
         "Useful when topic information is scarce", "Needs well-trained moderators & qualitative researchers"],
        ["Respondent Interviews", "Respondents interviewed about impressions after taking survey",
         "Does NOT interfere with survey-taking process", "Respondents may NOT recall all items they struggled with"],
    ],
    col_widths=[2.5*cm, 5.5*cm, 4.2*cm, 4.2*cm]
))
story.append(sp())

story.append(info_box("How to Conduct Pre-testing", [
    "Select 5–10 participants",
    "Convenience sampling is acceptable",
    "Consult your IRB (ethics committee) to determine if prior approval is needed",
], bg=C_ACCENT_LIGHT))
story.append(sp(2))

story.append(Paragraph("Pilot Study", S["h2"]))
story.append(Paragraph(
    "A pilot study is the <b>first step of the entire research protocol</b> — a smaller study "
    "that assists in planning and modifying the main study.",
    S["body"]))
story.append(sp())
story.append(simple_table(
    ["Type", "Meaning"],
    [
        ["External Pilot Study", "Done separately, independent of the main study"],
        ["Internal Pilot Study", "Done as part of the main study design"],
    ],
    col_widths=[5*cm, W - 4*cm - 5*cm]
))
story.append(sp())
story.append(Paragraph("Sample Size for Pilot Study", S["h3"]))
story.append(Paragraph(
    "Depends on: <b>type of study</b>, <b>problem probability</b>, <b>Confidence Interval (CI)</b>, "
    "and <b>Probability of Success (π)</b>.",
    S["body"]))
story.append(sp(2))
story.append(mnemonic_box("Pre-testing methods: <b>\"FRI\"</b> → Focus groups · Respondent interviews · IRB approval needed"))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 2 – VALIDITY
# ══════════════════════════════════════════════════════════════
story.append(section_banner("VALIDITY", 2))
story.append(sp(2))
story.append(Paragraph("What is Validity?", S["h2"]))
story.append(Paragraph(
    "<b>Validity = Does the instrument measure what it is SUPPOSED to measure?</b><br/>"
    "Example: A ruler is valid for measuring height. A weighing scale is NOT valid for height.",
    S["body"]))
story.append(sp())
story.append(mnemonic_box("Types of Validity: <b>\"Funny Cats Chase Prey\"</b><br/>"
    "<b>F</b>ace → <b>C</b>ontent → <b>C</b>onstruct → <b>P</b>redictive (Criterion)"))
story.append(sp(2))

# Validity types table
story.append(Paragraph("Types of Validity at a Glance", S["h2"]))
story.append(simple_table(
    ["Type", "Key Question", "Who Judges?", "Tool"],
    [
        ["Face", "Does it LOOK valid on the surface?", "Individual/Researcher", "Subjective review"],
        ["Content", "Does it cover ALL domains?", "Panel of experts", "Structured expert review"],
        ["Construct", "Does it measure the underlying concept?", "Statistical analysis", "Pearson correlation"],
        ["Criterion", "Does it predict/agree with a standard?", "Statistical analysis", "Correlation"],
    ],
    col_widths=[3.2*cm, 5.5*cm, 4*cm, 3.7*cm]
))
story.append(sp(2))

story.append(Paragraph("1. Face Validity", S["h2"]))
story.append(Paragraph(
    'Considers <b>how suitable the content of a test seems on the surface</b>. '
    'It is informal and subjective. <b>Face validity is NECESSARY but NOT SUFFICIENT</b> for construct validity.',
    S["body"]))
story.append(simple_table(
    ["Face Validity", "Example"],
    [
        ["HIGH face validity ✅", "Asking birthdate to calculate age — directly measures age"],
        ["LOW face validity ❌", "Counting gray hairs to guess age — not a relevant measure"],
    ],
    col_widths=[5*cm, W - 4*cm - 5*cm]
))
story.append(sp())

story.append(Paragraph("How to assess face validity?", S["h3"]))
story.append(Paragraph("• Are the questions relevant to what is being measured?<br/>• Does the measurement method seem useful for the variable?", S["body"]))
story.append(sp(2))

story.append(Paragraph("2. Content Validity", S["h2"]))
story.append(Paragraph(
    'A <b>judgment by a panel of experts</b> about whether the instrument <b>comprehensively covers '
    'all aspects (the full domain)</b> of the construct in a balanced way.',
    S["body"]))
story.append(info_box("Dartboard Analogy (from your slides)", [
    "Dart hits the board anywhere = Face validity ✅",
    "Dart covers ALL areas of the board evenly = Content validity ✅",
    "Dart hits only ONE area repeatedly = Content validity FAILS ❌",
    "Content validity is NOT proved if items are too similar and miss parts of the domain",
], bg=C_ACCENT_LIGHT))
story.append(sp(2))

story.append(Paragraph("3. Construct Validity", S["h2"]))
story.append(Paragraph(
    'Refers to whether you can draw <b>inferences about test scores related to the concept being studied</b>.<br/>'
    '<i>Example: If a person scores high on a depression questionnaire, do they truly have high depression?</i>',
    S["body"]))
story.append(sp())
story.append(simple_table(
    ["Sub-type", "Memory Trick", "Meaning"],
    [
        ["Convergent validity", "\"CONverge = come together\"",
         "Scale SHOULD correlate with SIMILAR/related variables"],
        ["Discriminant validity", "\"DISCriminate = tell apart\"",
         "Scale should NOT correlate with DISSIMILAR variables"],
    ],
    col_widths=[4*cm, 4.5*cm, W - 4*cm - 8.5*cm]
))
story.append(sp())
story.append(mnemonic_box("<b>\"Come Down\"</b> → <b>C</b>onvergent = similar variables agree | <b>D</b>iscriminant = dissimilar don't agree"))
story.append(sp(2))

story.append(Paragraph("4. Criterion Validity", S["h2"]))
story.append(Paragraph(
    'Refers to how well the measurement of one variable can <b>predict or agree with another established measure</b>. '
    'Measured using <b>correlation</b>.',
    S["body"]))
story.append(simple_table(
    ["Sub-type", "When measured", "Memory Trick", "Example from slides"],
    [
        ["Concurrent validity", "At the SAME TIME", "\"Current = Now\"",
         "Salivary cotinine correlated with number of smokers present NOW"],
        ["Predictive validity", "Predicts the FUTURE", "\"Predictive = Predict\"",
         "Plasma cotinine used to predict future metabolic clearance"],
    ],
    col_widths=[3.5*cm, 3*cm, 3*cm, W - 4*cm - 9.5*cm]
))
story.append(sp(2))
story.append(exam_tip_box(
    "You can have Reliability WITHOUT Validity. "
    "But you CANNOT have Validity WITHOUT Reliability. "
    "Validity requires reliability as a prerequisite."
))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 3 – RELIABILITY
# ══════════════════════════════════════════════════════════════
story.append(section_banner("RELIABILITY", 3))
story.append(sp(2))
story.append(Paragraph("What is Reliability?", S["h2"]))
story.append(Paragraph(
    '<b>Reliability = Consistency.</b> Same method + same sample + same conditions = same results.<br/>'
    'It refers to the <b>homogeneity of the instrument</b> and the <b>degree to which it is free from random error</b>.',
    S["body"]))
story.append(sp())
story.append(mnemonic_box(
    "<b>\"Tigers In India Sometimes Purr\"</b><br/>"
    "<b>T</b>est-Retest · <b>I</b>nter-rater · <b>I</b>nternal Consistency · <b>S</b>plit-Half · <b>P</b>arallel Forms"
))
story.append(sp(2))

story.append(simple_table(
    ["Method", "What it measures", "How", "Key statistic"],
    [
        ["Test-Retest", "Consistency over TIME",
         "Same test, same people, TWO different time points → calculate correlation",
         "Pearson correlation"],
        ["Inter-rater", "Agreement between RATERS/JUDGES",
         "Two raters score the same item independently → compare scores",
         "Cohen's Kappa (κ)"],
        ["Internal Consistency", "All items measure the SAME construct",
         "All items administered once → check inter-item correlation",
         "Cronbach's Alpha (α)"],
        ["Split-Half", "Internal consistency via TWO HALVES",
         "Divide test into two halves → correlate scores of each half",
         "Spearman-Brown"],
        ["Parallel Forms", "Equivalence of TWO VERSIONS",
         "Two equivalent test versions given to same group → compare results",
         "Pearson correlation"],
    ],
    col_widths=[3.2*cm, 3.5*cm, 6.5*cm, 3.2*cm]
))
story.append(sp(2))

story.append(Paragraph("Inter-rater Reliability — Cohen's Kappa Interpretation", S["h2"]))
story.append(simple_table(
    ["Kappa Value", "Interpretation"],
    [
        ["< 0.20", "Slight agreement"],
        ["0.21 – 0.40", "Fair agreement"],
        ["0.41 – 0.60", "Moderate agreement"],
        ["0.61 – 0.80", "Substantial agreement ✅"],
        ["0.81 – 1.00", "Almost perfect agreement ✅✅"],
    ],
    col_widths=[5*cm, W - 4*cm - 5*cm]
))
story.append(sp(2))

story.append(Paragraph("Cronbach's Alpha Interpretation", S["h2"]))
story.append(simple_table(
    ["Alpha Value", "Interpretation"],
    [
        ["α ≥ 0.9", "Excellent ✅✅"],
        ["α ≥ 0.8", "Good ✅"],
        ["α ≥ 0.7", "Acceptable ✅ (minimum threshold)"],
        ["α < 0.7", "Questionable / Poor ❌"],
    ],
    col_widths=[5*cm, W - 4*cm - 5*cm]
))
story.append(sp(2))

story.append(Paragraph("Validity vs Reliability — The Bullseye Analogy", S["h2"]))
story.append(simple_table(
    ["Darts on board", "Valid?", "Reliable?"],
    [
        ["Spread all over, away from center", "No ❌", "No ❌"],
        ["Clustered together but OFF center", "No ❌", "Yes ✅"],
        ["Spread around center (not clustered)", "Yes ✅", "No ❌"],
        ["Clustered tightly AT center", "Yes ✅✅", "Yes ✅✅"],
    ],
    col_widths=[9*cm, 2.5*cm, 2.5*cm]
))
story.append(sp(2))

story.append(Paragraph("Threats to Reliability and Validity", S["h2"]))
story.append(Paragraph(
    "Not only related to questionnaire design. Threats include:<br/>"
    "• <b>Bias and errors</b> in the conceptualization, design, sampling, and data collection process<br/>"
    "• <b>Contamination of results</b> in lab research as well as natural research settings",
    S["body"]))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 4 – STATISTICAL METHODS
# ══════════════════════════════════════════════════════════════
story.append(section_banner("CHOOSING APPROPRIATE STATISTICAL METHODS", 4))
story.append(sp(2))

story.append(Paragraph("Step 1: Define Variables", S["h2"]))
story.append(simple_table(
    ["Variable Type", "Sub-type", "Examples"],
    [
        ["Numerical", "Discrete (count)", "Number of children, hospital visits"],
        ["Numerical", "Continuous (measurement)", "Blood pressure, weight, height"],
        ["Categorical", "Nominal (no order)", "Blood group, gender, religion"],
        ["Categorical", "Ordinal (has order)", "Mild/Moderate/Severe, Likert scale"],
    ],
    col_widths=[4*cm, 5*cm, W - 4*cm - 9*cm]
))
story.append(sp(2))

story.append(Paragraph("Step 2: Parametric vs Non-Parametric", S["h2"]))
story.append(simple_table(
    ["Feature", "Parametric", "Non-Parametric"],
    [
        ["Assumes normal distribution?", "YES", "NO (distribution-free)"],
        ["Power", "More powerful", "Less powerful"],
        ["Use when data is...", "Normally distributed", "Skewed, ordinal, or small sample"],
    ],
    col_widths=[5.5*cm, 4.5*cm, W - 4*cm - 10*cm]
))
story.append(sp(2))

story.append(Paragraph("Step 3: Check Normality of Data", S["h2"]))
story.append(simple_table(
    ["Test", "When to Use", "Interpretation"],
    [
        ["Shapiro-Wilk", "Sample size n < 50", "p > 0.05 → data is NORMALLY DISTRIBUTED ✅"],
        ["Kolmogorov-Smirnov", "Sample size n ≥ 50", "p > 0.05 → data is NORMALLY DISTRIBUTED ✅"],
    ],
    col_widths=[4*cm, 4*cm, W - 4*cm - 8*cm]
))
story.append(sp())
story.append(info_box("Skewness and Kurtosis Rules", [
    "Values should be < ±1.0 to be considered normal",
    "< –1 = Left skewed (negative skew) | > +1 = Right skewed (positive skew)",
    "< –1 kurtosis = Platykurtic (flat) | > +1 kurtosis = Leptokurtic (peaked)",
], bg=C_ACCENT_LIGHT))
story.append(sp(2))

story.append(Paragraph("Step 4: The Statistical Test Selection Table", S["h2"]))
story.append(exam_tip_box("This table is the MOST IMPORTANT thing to memorise in the statistics topic!"))
story.append(sp())
story.append(simple_table(
    ["Outcome Variable", "2 Unrelated Groups", "2 Related Groups", "3+ Unrelated Groups", "3+ Related Groups"],
    [
        ["Continuous (Normal)", "Two-sample t-test", "Paired t-test", "ANOVA", "Mixed-effects model"],
        ["Ordinal / Non-normal", "Mann-Whitney U\n(Wilcoxon rank sum)", "Wilcoxon signed rank", "Kruskal-Wallis", "Friedman test"],
        ["Categorical", "Chi-square /\nFisher exact", "McNemar test", "Chi-square /\nFisher exact", "Cochran Q test"],
    ],
    col_widths=[3.5*cm, 3.5*cm, 3.5*cm, 3.5*cm, 3*cm]
))
story.append(sp(2))

story.append(Paragraph("Chi-Square vs Fisher's Exact Test", S["h2"]))
story.append(simple_table(
    ["Criterion", "Chi-Square Test", "Fisher's Exact Test"],
    [
        ["Sample size", "LARGE", "SMALL"],
        ["Accuracy", "Approximate", "Exact"],
        ["Table size", "Any size", "Usually 2×2"],
        ["Expected frequency rule", "< 20% of cells have expected freq < 5", "> 20% of cells have expected freq < 5"],
        ["Interpretation", "Pearson residuals", "Odds Ratio"],
    ],
    col_widths=[4.5*cm, 5.5*cm, W - 4*cm - 10*cm]
))
story.append(sp())
story.append(mnemonic_box("<b>\"Small sample = Fisher, Large sample = Chi\"</b> (F comes before C = F for Few/Small!)"))
story.append(sp(2))

story.append(Paragraph("Regression Models", S["h2"]))
story.append(simple_table(
    ["Outcome Variable", "Measure of Association", "Regression Model"],
    [
        ["Continuous", "Difference in means", "Linear Regression"],
        ["Binary (Yes/No)", "Odds Ratio", "Logistic Regression"],
        ["Count data", "Rate Ratio", "Poisson Regression"],
    ],
    col_widths=[5*cm, 4*cm, W - 4*cm - 9*cm]
))
story.append(sp(2))

story.append(Paragraph("Confounding, Interaction, P-Hacking", S["h2"]))
story.append(simple_table(
    ["Concept", "Definition", "Example"],
    [
        ["Confounding variable", "A third variable that distorts the relationship between independent and dependent variables",
         "Hot weather causes both ice cream sales AND drowning — weather is the confounder"],
        ["Main effect", "Independent influence of ONE variable on the outcome (other variables held constant)",
         "Exercise lowers blood pressure on average"],
        ["Interaction effect", "Effect of one variable DEPENDS ON the level of another variable",
         "Effect of exercise on BP is stronger in younger people than older people"],
        ["P-Hacking", "Manipulating data/tests until p < 0.05 is achieved — produces FALSE POSITIVES",
         "Trying multiple combinations of variables until a significant result appears"],
    ],
    col_widths=[4*cm, 6*cm, W - 4*cm - 10*cm]
))
story.append(sp(2))

story.append(info_box("Statistical vs Clinical Significance (Very Important!)", [
    "BOTH Drug A and Drug B had p = 0.005 (statistically significant)",
    "Drug A increased survival by 5 YEARS — HIGH clinical significance ✅",
    "Drug B increased survival by 5 MONTHS — LOW clinical significance ❌",
    "KEY LESSON: Statistical significance does NOT equal clinical significance!",
    "If 95% CI includes 1 → RR is NOT significant (p ≥ 0.05)",
], bg=C_ACCENT_LIGHT))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 5 – LITERATURE REVIEW
# ══════════════════════════════════════════════════════════════
story.append(section_banner("LITERATURE REVIEW", 5))
story.append(sp(2))

story.append(Paragraph("What is a Literature Review?", S["h2"]))
story.append(Paragraph(
    'A literature review is a <b>survey of scholarly sources on a specific topic</b>. '
    'It provides an overview of <b>current knowledge</b>, allowing you to identify relevant theories, '
    'methods, and <b>gaps in existing research</b> that you can apply to your own research or thesis.',
    S["body"]))
story.append(sp())

story.append(Paragraph("Purpose of Literature Review (5)", S["h2"]))
story.append(simple_table(
    ["#", "Purpose", "Plain meaning"],
    [
        ["1", "Demonstrate familiarity with the topic", "Show you know what has been done"],
        ["2", "Develop theoretical framework & methodology", "Build the foundation for your research"],
        ["3", "Position your work in relation to others", "Show where YOUR study fits in the field"],
        ["4", "Show how your research addresses a gap", "Justify WHY your study is needed"],
        ["5", "Evaluate current state of research", "Show you understand ongoing debates"],
    ],
    col_widths=[0.8*cm, 6*cm, W - 4*cm - 6.8*cm]
))
story.append(sp(2))

story.append(Paragraph("5 Key Steps to Writing a Literature Review", S["h2"]))
story.append(mnemonic_box(
    "<b>\"Some Elephants In Our World\"</b><br/>"
    "<b>S</b>earch → <b>E</b>valuate → <b>I</b>dentify themes → <b>O</b>utline → <b>W</b>rite"
))
story.append(sp(2))

steps = [
    ("STEP 1: Search for Relevant Literature", [
        "Brainstorm: What to search? Where? How? Who can help? What basic knowledge is needed?",
        "Search engines: PubMed (medical), Google Scholar (broad), Scopus (international)",
        "Sources: Journal articles, Book chapters, Conference papers, Reports, Reviews",
        "Search can be done Online or Offline",
    ]),
    ("STEP 2: Evaluate and Select Sources", [
        "What question/problem is the author addressing?",
        "What are the key concepts and how are they defined?",
        "What are the key theories, models, and methods?",
        "Does the research use established frameworks or an innovative approach?",
        "What are the results and conclusions?",
        "How does it relate to other literature in the field?",
        "Does it confirm, add to, or challenge established knowledge?",
        "What are the strengths and weaknesses of the research?",
        "Remember: Take notes and cite sources (APA or Vancouver Style)",
    ]),
    ("STEP 3: Identify Themes, Debates, and Gaps", [
        "Group articles by common themes (what do many studies agree on?)",
        "Note debates (what do studies disagree on?)",
        "Find gaps (what has NOT been studied?) — this JUSTIFIES your research",
    ]),
    ("STEP 4: Outline the Structure", [
        "Chronological — organized from oldest to newest studies",
        "Thematic (Qualitative Analysis) — organized by topics/themes (MOST COMMON)",
        "Methodological — organized by research methods used",
        "Theoretical — organized by different theoretical frameworks",
    ]),
    ("STEP 5: Write Your Literature Review", [
        "Introduction: Clearly establish the focus and PURPOSE of the literature review",
        "Body: Summarize & synthesize | Analyze & interpret | Critically evaluate | Write in well-structured paragraphs",
        "Conclusion: Summarize KEY FINDINGS and emphasize their significance",
    ]),
]

for title, points in steps:
    story.append(KeepTogether([
        Paragraph(title, S["h3"]),
        *[Paragraph(f"• {p}", S["bullet"]) for p in points],
        sp(),
    ]))
story.append(sp(2))

story.append(Paragraph("Boolean Search Operators", S["h2"]))
story.append(simple_table(
    ["Operator", "Effect", "Medical Example", "Result"],
    [
        ["AND", "Narrows search\n(both must be present)", "Fever AND Joint Pain", "4,432 results — only articles about BOTH"],
        ["OR", "Broadens search\n(either word)", "Fever OR Joint Pain", "317,869 results — any article about either"],
        ["NOT", "Excludes term", "Typhoid fever NOT children", "10,758 results — removes children-related articles"],
        ['( )', "Groups terms", "(Fever AND joint pain) OR chills", "16,774 results — controls search logic order"],
        ['" "', "Exact phrase", '"Joint Pain"', "Finds exact phrase together (fewer, more precise)"],
    ],
    col_widths=[1.5*cm, 3.5*cm, 5*cm, W - 4*cm - 10*cm]
))
story.append(sp(2))

story.append(Paragraph("Literature Review Summary Table Format", S["h2"]))
story.append(Paragraph("You must know this table format — it is the standard template from your slides:", S["body"]))
story.append(simple_table(
    ["Author(s) & Year", "Title", "Objective", "Methodology", "Key Findings", "Conclusion"],
    [
        ["Author 1 et al., YYYY", "Title of article", "Briefly state aim", "Methods used (survey, experiment)", "Significant results/trends", "Main takeaway/implications"],
        ["Wakefield et al., 2010", "Impact of tobacco advertising bans", "Assess effect of comprehensive tobacco ad bans", "Systematic review across multiple countries", "Comprehensive bans reduced smoking, especially in youth", "Complete advertising bans are critical for tobacco control"],
    ],
    col_widths=[2.8*cm, 2.8*cm, 2.8*cm, 2.8*cm, 2.8*cm, 2.4*cm]
))
story.append(sp(2))

story.append(Paragraph("Conceptual Framework", S["h2"]))
story.append(Paragraph(
    "A <b>conceptual framework</b> is a visual diagram showing <b>relationships between variables</b> in your study.",
    S["body"]))
story.append(simple_table(
    ["Purpose", "Meaning"],
    [
        ["Clarify concepts & propose relationships", "Shows how variables are connected in your study"],
        ["Explain observations", "Provides a logical explanation for what you observe"],
        ["Provide context for interpreting findings", "Helps make sense of your results"],
        ["Encourage theory development", "Helps build theories useful to real practice"],
    ],
    col_widths=[7*cm, W - 4*cm - 7*cm]
))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 6 – CITATION & REFERENCES
# ══════════════════════════════════════════════════════════════
story.append(section_banner("CITATION &amp; REFERENCES", 6))
story.append(sp(2))

story.append(Paragraph("What is a Citation?", S["h2"]))
story.append(Paragraph(
    'A citation tells readers that certain material in your work came from another source and gives information to find that source. '
    '<b>Without citation = PLAGIARISM.</b>',
    S["body"]))
story.append(info_box("Why Cite Sources?", [
    "Credits the original author — avoids plagiarism",
    "Helps readers find and verify sources",
    "Shows the amount of research you have done",
    "Protects you from being blamed for someone else's incorrect ideas",
    "Not all sources are good — your own ideas may sometimes be more accurate",
], bg=C_LIGHT_BLUE))
story.append(sp(2))

story.append(Paragraph("Citation Style Comparison", S["h2"]))
story.append(simple_table(
    ["Style", "Used in", "In-text Format", "Reference order"],
    [
        ["Vancouver", "Medicine / Biomedical (MOST IMPORTANT FOR PSM!)", "Superscript numbers¹²³", "Order of appearance in text"],
        ["APA (7th ed)", "Psychology, Social Sciences", "(Author, Year)", "Alphabetical"],
        ["MLA", "Humanities, Literature", "(Author Page)", "Alphabetical"],
        ["Chicago", "History, Arts", "Footnotes / Endnotes", "Alphabetical"],
    ],
    col_widths=[2.5*cm, 5.5*cm, 3.5*cm, W - 4*cm - 11.5*cm]
))
story.append(sp(2))

story.append(Paragraph("Vancouver Style (Most Important!)", S["h2"]))
story.append(Paragraph("<b>Format for journal articles:</b>", S["body_bold"]))
story.append(Paragraph(
    "Author(s). Title of article. <i>Journal Name</i>. Year;Volume(Issue):Pages.",
    ParagraphStyle("fmt", fontName="Helvetica-Oblique", fontSize=9, leading=14,
        leftIndent=12, textColor=C_MID_BLUE, spaceAfter=4)))
story.append(Paragraph("<b>Rules:</b>", S["body_bold"]))
story.append(Paragraph(
    "• Authors listed as Surname then Initials<br/>"
    "• Up to 6 authors, then 'et al.'<br/>"
    "• References numbered in ORDER OF APPEARANCE in text<br/>"
    "• Numbers appear as superscripts in the text like this¹",
    S["body"]))
story.append(sp())
story.append(info_box("Vancouver Example (from your slides)", [
    "Vaidya SS, Aryal UR, Grjibovski AM, Krettek A. Visual status in primary open-angle glaucoma: "
    "A hospital-based report from Nepal. Journal of Kathmandu Medical College. 2014 Oct 9;3(2):49–57.",
], bg=C_GREEN_LIGHT))
story.append(sp(2))

story.append(Paragraph("APA Style (7th Edition)", S["h2"]))
story.append(Paragraph("<b>Format for journal articles:</b>", S["body_bold"]))
story.append(Paragraph(
    "Author, A. A., &amp; Author, B. B. (Year). Title of article. <i>Journal Name, Volume</i>(Issue), Pages. DOI",
    ParagraphStyle("fmt2", fontName="Helvetica-Oblique", fontSize=9, leading=14,
        leftIndent=12, textColor=C_MID_BLUE, spaceAfter=4)))
story.append(info_box("APA Example (from your slides)", [
    "Killinger O. (2022). Examining the relationships between high school students' interests in music "
    "and their creativity. International Journal on Social & Education Sciences (IJonSES), 4(3).",
], bg=C_GREEN_LIGHT))
story.append(sp(2))

story.append(Paragraph("Vancouver vs APA — Quick Comparison", S["h2"]))
story.append(simple_table(
    ["Feature", "Vancouver", "APA"],
    [
        ["In-text citation", "Superscript number ¹", "(Author, Year)"],
        ["Reference order", "By order of appearance", "Alphabetical by author"],
        ["Year placement", "At end before volume/page", "In parentheses after author(s)"],
        ["Journal name", "Full or abbreviated", "Full name in italics"],
        ["Used for", "Medical journals, PSM", "Psychology, social science"],
    ],
    col_widths=[5*cm, 5*cm, W - 4*cm - 10*cm]
))
story.append(sp(2))

story.append(Paragraph("Bibliography", S["h2"]))
story.append(Paragraph(
    'From Greek: <b>biblion</b> (book) + <b>graphia</b> (to write). '
    'An <b>alphabetical list of ALL sources consulted</b> — including those not directly cited. '
    'Written on a separate page with <b>hanging indentation</b>.',
    S["body"]))
story.append(simple_table(
    ["For Books/Journals — Include", "For Websites — Include"],
    [["Author name, Title, Article title (if journal), Date, Place of publication, Publisher, Volume, Page numbers",
      "Author/editor name, Title of website, Organization, URL, DATE OF ACCESS"]],
    col_widths=[(W - 4*cm)/2, (W - 4*cm)/2]
))
story.append(sp())
story.append(info_box("Citation Management Software", [
    "Mendeley", "Zotero", "EndNote", "RefWorks",
], bg=C_ACCENT_LIGHT))
story.append(PageBreak())

# ══════════════════════════════════════════════════════════════
# TOPIC 7 – QUICK REVISION & MNEMONICS
# ══════════════════════════════════════════════════════════════
story.append(section_banner("QUICK REVISION TABLES &amp; MNEMONICS", 7))
story.append(sp(2))

story.append(Paragraph("All Validity Types — One-Line Definitions", S["h2"]))
story.append(simple_table(
    ["Type", "One-line definition"],
    [
        ["Face", "Looks valid on the surface (informal, subjective)"],
        ["Content", "Covers ALL domains (judged by panel of experts)"],
        ["Construct", "Measures the underlying concept"],
        ["Convergent (Construct)", "Correlates WITH similar/related variables"],
        ["Discriminant (Construct)", "Does NOT correlate with dissimilar variables"],
        ["Criterion-Concurrent", "Agrees with gold standard NOW (same time)"],
        ["Criterion-Predictive", "Predicts future outcome (measured later)"],
    ],
    col_widths=[5.5*cm, W - 4*cm - 5.5*cm]
))
story.append(sp(2))

story.append(Paragraph("All Reliability Methods — One-Line Definitions", S["h2"]))
story.append(simple_table(
    ["Method", "One-line definition"],
    [
        ["Test-Retest", "Same test, same people, different time — should give same results"],
        ["Inter-rater", "Same thing rated by different raters — should agree (Cohen's Kappa)"],
        ["Internal Consistency", "All items measure the same thing (Cronbach's Alpha ≥ 0.7)"],
        ["Split-Half", "Two halves of the test should give similar results"],
        ["Parallel Forms", "Two equivalent versions of the test should give same results"],
    ],
    col_widths=[5.5*cm, W - 4*cm - 5.5*cm]
))
story.append(sp(2))

story.append(Paragraph("Statistical Tests — Complete Quick Reference", S["h2"]))
story.append(simple_table(
    ["Situation", "Use this test"],
    [
        ["2 groups, continuous outcome, NORMAL distribution", "Independent (Two-sample) t-test"],
        ["2 groups, continuous outcome, NON-NORMAL / ordinal", "Mann-Whitney U test"],
        ["2 groups, SAME people (paired/repeated), normal", "Paired t-test"],
        ["2 groups, SAME people, non-normal", "Wilcoxon signed rank test"],
        ["3+ groups, continuous, normal", "ANOVA (Analysis of Variance)"],
        ["3+ groups, non-normal / ordinal", "Kruskal-Wallis test"],
        ["3+ groups, same people, normal", "Mixed-effects model / Repeated measures ANOVA"],
        ["3+ groups, same people, non-normal", "Friedman test"],
        ["2 categorical variables, LARGE sample", "Chi-square test"],
        ["2 categorical variables, SMALL sample / any cell < 5", "Fisher's Exact test"],
        ["2 categorical variables, SAME people (before/after)", "McNemar test"],
        ["3+ categorical variables, same people", "Cochran Q test"],
        ["Check normality (n < 50)", "Shapiro-Wilk test"],
        ["Check normality (n ≥ 50)", "Kolmogorov-Smirnov test"],
    ],
    col_widths=[9*cm, W - 4*cm - 9*cm]
))
story.append(sp(2))

story.append(Paragraph("All Mnemonics on One Page", S["h2"]))
mnemonics = [
    ("Types of Validity", '"Funny Cats Chase Prey"', "Face · Content · Construct · Predictive (Criterion)"),
    ("Reliability Methods", '"Tigers In India Sometimes Purr"', "Test-Retest · Inter-rater · Internal Consistency · Split-Half · Parallel"),
    ("Construct Validity", '"Come Down"', "Convergent = similar variables agree | Discriminant = dissimilar don't agree"),
    ("5 Steps of Lit Review", '"Some Elephants In Our World"', "Search · Evaluate · Identify · Outline · Write"),
    ("4 Lit Review Structures", '"Cows Think More Than"', "Chronological · Thematic · Methodological · Theoretical"),
    ("Table Columns", '"A-T-O-M-K-C"', "Author · Title · Objective · Methodology · Key Findings · Conclusion"),
    ("Boolean Operators", '"AON Rule"', "AND = narrows | OR = opens | NOT = removes"),
    ("Fisher vs Chi-square", '"F before C = F for Few"', "Small sample → Fisher's Exact | Large sample → Chi-Square"),
    ("Pre-testing", '"FRI"', "Focus groups · Respondent interviews · IRB approval"),
    ("Cronbach's Alpha", '"Seven is the Heaven"', "α ≥ 0.7 = Acceptable minimum threshold for internal consistency"),
]
mnem_data = [[Paragraph("<b>#</b>", S["th"]), Paragraph("<b>Topic</b>", S["th"]),
              Paragraph("<b>Mnemonic</b>", S["th"]), Paragraph("<b>Meaning</b>", S["th"])]]
for i, (topic, trick, meaning) in enumerate(mnemonics):
    bg = C_GRAY_BG if i % 2 == 0 else C_WHITE
    mnem_data.append([
        Paragraph(str(i+1), S["td_c"]),
        Paragraph(topic, S["td_bold"]),
        Paragraph(f"<b><i>{trick}</i></b>", ParagraphStyle("mn", fontName="Helvetica-BoldOblique",
            fontSize=8, textColor=C_GREEN, leading=10)),
        Paragraph(meaning, S["td"]),
    ])
mnem_t = Table(mnem_data, colWidths=[0.7*cm, 4*cm, 5*cm, W - 4*cm - 9.7*cm])
mnem_t.setStyle(TableStyle([
    ("BACKGROUND",    (0,0), (-1,0), C_DARK_BLUE),
    ("ROWBACKGROUNDS",(0,1), (-1,-1), [C_GRAY_BG, C_WHITE]),
    ("GRID",          (0,0), (-1,-1), 0.3, C_GRAY_LINE),
    ("TOPPADDING",    (0,0), (-1,-1), 5),
    ("BOTTOMPADDING", (0,0), (-1,-1), 5),
    ("LEFTPADDING",   (0,0), (-1,-1), 6),
    ("VALIGN",        (0,0), (-1,-1), "TOP"),
]))
story.append(mnem_t)
story.append(sp(2))

story.append(exam_tip_box(
    "TOP EXAM QUESTIONS TO EXPECT:\n"
    "1. Define validity. Explain the types of validity with examples.\n"
    "2. What are the methods of reliability? Explain any two.\n"
    "3. What is Cronbach's alpha / Cohen's Kappa? Interpret the values.\n"
    "4. How do you choose an appropriate statistical test? Give examples.\n"
    "5. Explain the 5 steps of a literature review.\n"
    "6. What is Boolean search? Give examples of AND, OR, NOT.\n"
    "7. Differentiate Vancouver and APA citation styles.\n"
    "8. What is pre-testing? What are its methods and benefits?"
))
story.append(sp(2))

# Final footer box
footer_content = [
    Paragraph("GOOD LUCK IN YOUR EXAM TOMORROW! 🎯", ParagraphStyle("fl",
        fontName="Helvetica-Bold", fontSize=13, textColor=C_WHITE,
        alignment=TA_CENTER, spaceAfter=4)),
    Paragraph("You have covered: Pre-testing · Validity · Reliability · Statistics · Literature Review · Citation",
        ParagraphStyle("fs", fontName="Helvetica", fontSize=9, textColor=HexColor("#bfdbfe"),
        alignment=TA_CENTER)),
]
foot_t = Table([[footer_content]], colWidths=[W - 4*cm])
foot_t.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), C_DARK_BLUE),
    ("TOPPADDING",    (0,0), (-1,-1), 18),
    ("BOTTOMPADDING", (0,0), (-1,-1), 18),
    ("LEFTPADDING",   (0,0), (-1,-1), 20),
    ("ROUNDEDCORNERS", [8]),
]))
story.append(foot_t)

# ─── Build ────────────────────────────────────────────────────
doc.build(story)
print("PDF created:", OUTPUT)

Running Command

python3 /home/daytona/workspace/psm-study-guide/build_pdf.py 2>&1

Running Command

ls -lh /home/daytona/workspace/psm-study-guide/PSM_Study_Guide.pdf

Your PDF is ready! Download it here:

PSM Study Guide
PDF Document · PDF

Here's what's inside the PDF (7 sections, colour-coded and fully formatted):

Section	Content
Topic 1	Pre-testing — methods, benefits, pilot study, IRB
Topic 2	Validity — Face, Content, Construct (Convergent/Discriminant), Criterion (Concurrent/Predictive) with examples
Topic 3	Reliability — all 5 methods, Cronbach's Alpha interpretation table, Cohen's Kappa table, Bullseye analogy
Topic 4	Statistics — variable types, normality tests, the full test-selection table, Chi-sq vs Fisher's, regression models, p-hacking, clinical vs statistical significance
Topic 5	Literature Review — 5 steps in detail, Boolean search (AND/OR/NOT) with examples, 4 structure types, table template, conceptual framework
Topic 6	Citation — Vancouver vs APA comparison, formats, examples from your slides, bibliography rules
Topic 7	Quick revision tables + ALL 10 mnemonics on one page + Top 8 expected exam questions

The PDF has colour-coded section banners, green mnemonic boxes, orange exam tip boxes, and full comparison tables - easy to read and revise from quickly. All the best tomorrow! 🎯

This pdf is not opening

Running Command

python3 -c " from pypdf import PdfReader r = PdfReader('/home/daytona/workspace/psm-study-guide/PSM_Study_Guide.pdf') print('Pages:', len(r.pages)) print('Page 1 text sample:', r.pages[0].extract_text()[:200]) " 2>&1

Running Command

python3 -c " # Test with a simple PDF first from reportlab.platypus import SimpleDocTemplate, Paragraph from reportlab.lib.styles import getSampleStyleSheet from reportlab.lib.pagesizes import A4 doc = SimpleDocTemplate('/home/daytona/workspace/psm-study-guide/test_simple.pdf', pagesize=A4) styles = getSampleStyleSheet() story = [Paragraph('Hello World - Test PDF', styles['Title'])] doc.build(story) print('Simple PDF OK') " 2>&1

Writing File

~/psm-study-guide/build_v2.py

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.units import cm
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_LEFT, TA_CENTER
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.colors import HexColor, white, black

OUTPUT = "/home/daytona/workspace/psm-study-guide/PSM_Study_Guide_v2.pdf"

W, H = A4

# Colours
C_DARK   = HexColor("#1a3557")
C_BLUE   = HexColor("#2563a8")
C_LBLUE  = HexColor("#dbeafe")
C_ORANGE = HexColor("#e67e22")
C_LYELL  = HexColor("#fef9c3")
C_GREEN  = HexColor("#166534")
C_LGREEN = HexColor("#dcfce7")
C_LRED   = HexColor("#fee2e2")
C_RED    = HexColor("#991b1b")
C_LGRAY  = HexColor("#f1f5f9")
C_GRAY   = HexColor("#cbd5e1")
C_WHITE  = white
C_BLACK  = black

def S(name, **kw):
    base = getSampleStyleSheet()
    parent = kw.pop("parent", "Normal")
    return ParagraphStyle(name, parent=base[parent], **kw)

# Styles dict
ST = {
    "title":    S("title",    parent="Title", fontName="Helvetica-Bold",   fontSize=24, textColor=C_WHITE,  alignment=TA_CENTER, spaceAfter=6),
    "subtitle": S("subtitle", fontName="Helvetica-Bold",   fontSize=13, textColor=HexColor("#fbbf24"), alignment=TA_CENTER, spaceAfter=4),
    "sub2":     S("sub2",     fontName="Helvetica",         fontSize=10, textColor=HexColor("#bfdbfe"), alignment=TA_CENTER, spaceAfter=3),
    "date":     S("date",     fontName="Helvetica-Oblique", fontSize=9,  textColor=HexColor("#93c5fd"), alignment=TA_CENTER),

    "h1":  S("h1",  fontName="Helvetica-Bold", fontSize=13, textColor=C_WHITE,  spaceAfter=2, spaceBefore=2),
    "h2":  S("h2",  fontName="Helvetica-Bold", fontSize=11, textColor=C_DARK,   spaceAfter=4, spaceBefore=8),
    "h3":  S("h3",  fontName="Helvetica-Bold", fontSize=9.5,textColor=C_BLUE,   spaceAfter=3, spaceBefore=5),
    "body":S("body",fontName="Helvetica",       fontSize=9,  leading=13,         spaceAfter=3),
    "bull":S("bull",fontName="Helvetica",       fontSize=9,  leading=13, leftIndent=12, firstLineIndent=-8, spaceAfter=2),
    "bold":S("bold",fontName="Helvetica-Bold",  fontSize=9,  leading=13, spaceAfter=3),
    "th":  S("th",  fontName="Helvetica-Bold",  fontSize=8,  textColor=C_WHITE,  alignment=TA_CENTER, leading=10),
    "td":  S("td",  fontName="Helvetica",       fontSize=8,  leading=10),
    "tdc": S("tdc", fontName="Helvetica",       fontSize=8,  alignment=TA_CENTER,leading=10),
    "tdb": S("tdb", fontName="Helvetica-Bold",  fontSize=8,  textColor=C_DARK,   leading=10),
    "mn":  S("mn",  fontName="Helvetica-BoldOblique", fontSize=8.5, textColor=C_GREEN, leading=12),
    "tip": S("tip", fontName="Helvetica",       fontSize=8.5,textColor=HexColor("#7f1d1d"), leading=12),
}

CW = W - 4*cm   # content width

def sp(n=4): return Spacer(1, n)
def hr(): return HRFlowable(width="100%", thickness=0.4, color=C_GRAY, spaceAfter=4, spaceBefore=4)

# ── Banner helpers (plain coloured Table, no rounded corners) ──

def cover_banner():
    rows = [
        [Paragraph("PSM PRACTICA", ST["title"])],
        [Paragraph("COMPLETE STUDY GUIDE", ST["subtitle"])],
        [Paragraph("Pre-testing &nbsp;·&nbsp; Validity &amp; Reliability &nbsp;·&nbsp; Statistical Methods &nbsp;·&nbsp; Literature Review &nbsp;·&nbsp; Citation", ST["sub2"])],
        [Spacer(1, 4)],
        [Paragraph("June 2026  |  MBBS / BPH Examination Preparation", ST["date"])],
    ]
    t = Table([[rows]], colWidths=[CW])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), C_DARK),
        ("TOPPADDING",   (0,0),(-1,-1), 26),
        ("BOTTOMPADDING",(0,0),(-1,-1), 26),
        ("LEFTPADDING",  (0,0),(-1,-1), 16),
        ("RIGHTPADDING", (0,0),(-1,-1), 16),
    ]))
    return t

def section_banner(num, text):
    p = Paragraph(f"<b>TOPIC {num}  |  {text}</b>", ST["h1"])
    t = Table([[p]], colWidths=[CW])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), C_BLUE),
        ("TOPPADDING",   (0,0),(-1,-1), 8),
        ("BOTTOMPADDING",(0,0),(-1,-1), 8),
        ("LEFTPADDING",  (0,0),(-1,-1), 12),
    ]))
    return t

def coloured_box(title, items, bg, title_color=C_DARK):
    content = [Paragraph(f"<b>{title}</b>", ParagraphStyle("bh", fontName="Helvetica-Bold",
        fontSize=9.5, textColor=title_color, spaceAfter=4))]
    for item in items:
        content.append(Paragraph(f"• {item}", ST["bull"]))
    t = Table([[content]], colWidths=[CW])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), bg),
        ("TOPPADDING",   (0,0),(-1,-1), 8),
        ("BOTTOMPADDING",(0,0),(-1,-1), 8),
        ("LEFTPADDING",  (0,0),(-1,-1), 12),
        ("RIGHTPADDING", (0,0),(-1,-1), 12),
        ("BOX",          (0,0),(-1,-1), 0.5, C_GRAY),
    ]))
    return t

def mnemonic(text):
    inner = [
        Paragraph("MNEMONIC", ParagraphStyle("mnh", fontName="Helvetica-Bold", fontSize=8,
            textColor=C_GREEN, spaceAfter=2)),
        Paragraph(text, ST["mn"]),
    ]
    t = Table([[inner]], colWidths=[CW])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), C_LGREEN),
        ("TOPPADDING",   (0,0),(-1,-1), 7),
        ("BOTTOMPADDING",(0,0),(-1,-1), 7),
        ("LEFTPADDING",  (0,0),(-1,-1), 12),
        ("RIGHTPADDING", (0,0),(-1,-1), 12),
        ("BOX",          (0,0),(-1,-1), 0.5, HexColor("#86efac")),
    ]))
    return t

def exam_tip(text):
    inner = [
        Paragraph("EXAM TIP", ParagraphStyle("eth", fontName="Helvetica-Bold", fontSize=8,
            textColor=C_RED, spaceAfter=2)),
        Paragraph(text, ST["tip"]),
    ]
    t = Table([[inner]], colWidths=[CW])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0),(-1,-1), C_LRED),
        ("TOPPADDING",   (0,0),(-1,-1), 7),
        ("BOTTOMPADDING",(0,0),(-1,-1), 7),
        ("LEFTPADDING",  (0,0),(-1,-1), 12),
        ("RIGHTPADDING", (0,0),(-1,-1), 12),
        ("BOX",          (0,0),(-1,-1), 0.5, HexColor("#fca5a5")),
    ]))
    return t

def tbl(headers, rows, widths=None):
    if widths is None:
        widths = [CW / len(headers)] * len(headers)
    data = [[Paragraph(h, ST["th"]) for h in headers]]
    for i, row in enumerate(rows):
        data.append([Paragraph(str(c), ST["td"]) for c in row])
    t = Table(data, colWidths=widths, repeatRows=1)
    t.setStyle(TableStyle([
        ("BACKGROUND",     (0,0),(-1,0), C_DARK),
        ("ROWBACKGROUNDS", (0,1),(-1,-1), [C_LGRAY, C_WHITE]),
        ("GRID",           (0,0),(-1,-1), 0.3, C_GRAY),
        ("TOPPADDING",     (0,0),(-1,-1), 4),
        ("BOTTOMPADDING",  (0,0),(-1,-1), 4),
        ("LEFTPADDING",    (0,0),(-1,-1), 5),
        ("RIGHTPADDING",   (0,0),(-1,-1), 5),
        ("VALIGN",         (0,0),(-1,-1), "MIDDLE"),
    ]))
    return t

# ══════════════════════════════════════════
# BUILD
# ══════════════════════════════════════════
doc = SimpleDocTemplate(
    OUTPUT, pagesize=A4,
    leftMargin=2*cm, rightMargin=2*cm,
    topMargin=1.8*cm, bottomMargin=2*cm,
    title="PSM Practica Study Guide",
    author="Exam Prep 2026",
)
story = []

# ── COVER ──
story += [sp(12), cover_banner(), sp(8)]

toc = tbl(
    ["#", "Topic"],
    [
        ["1", "Pre-testing & Pilot Study"],
        ["2", "Validity — Face, Content, Construct, Criterion"],
        ["3", "Reliability — Test-Retest, Inter-rater, Internal Consistency, Split-Half, Parallel"],
        ["4", "Choosing Appropriate Statistical Methods"],
        ["5", "Literature Review — Steps, Boolean Search, Structure, Table"],
        ["6", "Citation & References — Vancouver, APA, Bibliography"],
        ["7", "Quick Revision Tables & All Mnemonics"],
    ],
    widths=[1*cm, CW - 1*cm]
)
story += [Paragraph("Contents", ST["h2"]), toc, PageBreak()]

# ══════════════════════════════════════════
# TOPIC 1 — PRE-TESTING
# ══════════════════════════════════════════
story += [section_banner(1, "PRE-TESTING & PILOT STUDY"), sp(6)]

story += [
    Paragraph("What is Pre-testing?", ST["h2"]),
    Paragraph("Testing your questionnaire <b>BEFORE</b> the real study starts to find problems and fix them early.", ST["body"]),
    sp(4),
    coloured_box("Benefits of Pre-testing", [
        "Detect potential problems → reduces errors in data gathering",
        "Predict how participants will respond (recruitment problems, response rates)",
        "Refine research — fix time estimates and data collection methods",
    ], C_LBLUE),
    sp(6),
    Paragraph("Pre-testing Methods", ST["h2"]),
    tbl(
        ["Method", "What happens", "Pro", "Con"],
        [
            ["Focus Groups", "Small group discussion with community members",
             "Useful when topic info is scarce", "Needs well-trained moderators"],
            ["Respondent Interviews", "Respondents interviewed about survey impressions",
             "Does NOT interfere with survey-taking", "May NOT recall all problem items"],
        ],
        widths=[2.5*cm, 5.5*cm, 4*cm, 4*cm]
    ),
    sp(6),
    coloured_box("How to Conduct Pre-testing", [
        "Select 5-10 participants (convenience sampling is OK)",
        "Consult your IRB (ethics committee) before starting",
    ], C_LYELL),
    sp(8),
    Paragraph("Pilot Study", ST["h2"]),
    Paragraph("The <b>first step of the entire research protocol</b> — a smaller study to help plan and modify the main study.", ST["body"]),
    sp(4),
    tbl(
        ["Type", "Meaning"],
        [
            ["External Pilot Study", "Done separately, independent of the main study"],
            ["Internal Pilot Study", "Done as part of the main study design"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(4),
    Paragraph("<b>Sample size for pilot study</b> depends on: type of study, problem probability, Confidence Interval (CI), and Probability of Success (π).", ST["body"]),
    sp(6),
    mnemonic("<b>Pre-testing methods: \"FRI\"</b>  —  Focus groups  ·  Respondent interviews  ·  IRB approval needed"),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 2 — VALIDITY
# ══════════════════════════════════════════
story += [section_banner(2, "VALIDITY"), sp(6)]
story += [
    Paragraph("What is Validity?", ST["h2"]),
    Paragraph("<b>Validity = Does the instrument measure what it is SUPPOSED to measure?</b><br/>"
              "Example: A ruler is valid for height. A weighing scale is NOT valid for height.", ST["body"]),
    sp(4),
    mnemonic("<b>\"Funny Cats Chase Prey\"</b>  —  Face · Content · Construct · Predictive (Criterion)"),
    sp(8),
    Paragraph("Types of Validity — Overview Table", ST["h2"]),
    tbl(
        ["Type", "Key Question", "Judged by", "Tool"],
        [
            ["Face",      "Does it LOOK valid on the surface?",           "Individual/Researcher", "Subjective review"],
            ["Content",   "Does it cover ALL domains?",                   "Panel of experts",      "Expert structured review"],
            ["Construct", "Does it measure the underlying concept?",      "Statistical analysis",  "Pearson correlation"],
            ["Criterion", "Does it predict/agree with a standard?",       "Statistical analysis",  "Correlation"],
        ],
        widths=[3*cm, 5.5*cm, 4*cm, 3.5*cm]
    ),
    sp(8),

    Paragraph("1. Face Validity", ST["h2"]),
    Paragraph("How suitable the content seems <b>on the surface</b> — informal, subjective. "
              "<b>NECESSARY but NOT SUFFICIENT</b> for construct validity.", ST["body"]),
    tbl(
        ["Face Validity Level", "Example"],
        [
            ["HIGH face validity", "Asking birthdate to calculate age — directly measures age"],
            ["LOW face validity",  "Counting gray hairs to guess age — not a relevant measure"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(8),

    Paragraph("2. Content Validity", ST["h2"]),
    Paragraph("A <b>panel of experts</b> judges whether the instrument <b>covers the FULL DOMAIN</b> of the construct in a balanced way.", ST["body"]),
    coloured_box("Dartboard Analogy (from your slides)", [
        "Dart hits the board anywhere = Face validity",
        "Darts cover ALL areas evenly = Content validity",
        "Darts only hit ONE area = Content validity FAILS (domain not fully covered)",
    ], C_LYELL),
    sp(8),

    Paragraph("3. Construct Validity", ST["h2"]),
    Paragraph("Whether you can draw <b>inferences about test scores related to the concept</b> being studied.<br/>"
              "<i>Example: High score on depression questionnaire — does person truly have high depression?</i>", ST["body"]),
    tbl(
        ["Sub-type", "Memory Trick", "Meaning"],
        [
            ["Convergent",   "\"CONverge = come TOGETHER\"", "Scale SHOULD correlate with SIMILAR variables"],
            ["Discriminant", "\"DISCriminate = tell APART\"", "Scale should NOT correlate with DISSIMILAR variables"],
        ],
        widths=[3.5*cm, 5*cm, CW - 8.5*cm]
    ),
    sp(4),
    mnemonic("<b>\"Come Down\"</b>  —  Convergent = similar agree  |  Discriminant = dissimilar don't agree"),
    sp(8),

    Paragraph("4. Criterion Validity", ST["h2"]),
    Paragraph("How well the measurement can <b>predict or agree with</b> another established measure. Measured using <b>correlation</b>.", ST["body"]),
    tbl(
        ["Sub-type", "When measured", "Memory trick", "Example (from your slides)"],
        [
            ["Concurrent",  "At the SAME TIME",  "\"Current = Now\"",        "Salivary cotinine correlated with number of smokers present NOW"],
            ["Predictive",  "Predicts the FUTURE","\"Predictive = Predict\"", "Plasma cotinine used to predict future metabolic clearance"],
        ],
        widths=[3*cm, 3*cm, 3*cm, CW - 9*cm]
    ),
    sp(6),
    exam_tip("KEY RULE: You can have Reliability WITHOUT Validity. But you CANNOT have Validity WITHOUT Reliability. Validity requires reliability as a prerequisite."),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 3 — RELIABILITY
# ══════════════════════════════════════════
story += [section_banner(3, "RELIABILITY"), sp(6)]
story += [
    Paragraph("What is Reliability?", ST["h2"]),
    Paragraph("<b>Reliability = Consistency.</b> Same method + same sample + same conditions = same results.<br/>"
              "It measures the <b>homogeneity of the instrument</b> and its freedom from random error.", ST["body"]),
    sp(4),
    mnemonic("<b>\"Tigers In India Sometimes Purr\"</b>  —  Test-Retest · Inter-rater · Internal Consistency · Split-Half · Parallel Forms"),
    sp(8),

    Paragraph("Methods of Reliability — Summary Table", ST["h2"]),
    tbl(
        ["Method", "What it measures", "How", "Key Statistic"],
        [
            ["Test-Retest",          "Consistency over TIME",                 "Same test, same people, 2 different time points → correlate",             "Pearson r"],
            ["Inter-rater",          "Agreement between RATERS",              "Two raters score same item → compare scores",                             "Cohen's Kappa (κ)"],
            ["Internal Consistency", "All items measure SAME construct",      "All items once → check inter-item correlation",                           "Cronbach's Alpha (α)"],
            ["Split-Half",           "Consistency of TWO HALVES",             "Split test into 2 halves → correlate both halves",                        "Spearman-Brown"],
            ["Parallel Forms",       "Equivalence of TWO TEST VERSIONS",      "Two equivalent versions given to same group → compare",                   "Pearson r"],
        ],
        widths=[3*cm, 3.5*cm, 6*cm, 3.5*cm]
    ),
    sp(8),

    Paragraph("Cohen's Kappa (Inter-rater) Interpretation", ST["h2"]),
    tbl(
        ["Kappa Value", "Interpretation"],
        [
            ["< 0.20",       "Slight agreement"],
            ["0.21 - 0.40",  "Fair agreement"],
            ["0.41 - 0.60",  "Moderate agreement"],
            ["0.61 - 0.80",  "Substantial agreement (Good)"],
            ["0.81 - 1.00",  "Almost perfect agreement (Excellent)"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(8),

    Paragraph("Cronbach's Alpha Interpretation", ST["h2"]),
    tbl(
        ["Alpha (α) Value", "Interpretation"],
        [
            ["α >= 0.9", "Excellent"],
            ["α >= 0.8", "Good"],
            ["α >= 0.7", "Acceptable (minimum threshold)"],
            ["α < 0.7",  "Questionable / Poor"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(6),
    mnemonic("<b>\"Seven is Heaven\"</b>  —  α ≥ 0.7 is the minimum acceptable threshold for Cronbach's Alpha"),
    sp(8),

    Paragraph("Validity vs Reliability — The Bullseye Analogy", ST["h2"]),
    tbl(
        ["Darts on board", "Valid?", "Reliable?"],
        [
            ["Spread all over, away from center",         "No",      "No"],
            ["Clustered tightly but OFF center",          "No",      "Yes"],
            ["Spread around center (not clustered)",      "Yes",     "No"],
            ["Clustered tightly AT center",               "Yes",     "Yes"],
        ],
        widths=[9*cm, 2.5*cm, 2.5*cm]
    ),
    sp(8),

    Paragraph("Threats to Reliability and Validity", ST["h2"]),
    Paragraph("Not only related to questionnaire design. Threats include:<br/>"
              "• <b>Bias and errors</b> in conceptualization, design, sampling, and data collection process<br/>"
              "• <b>Contamination of results</b> in lab research as well as natural research settings",
              ST["body"]),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 4 — STATISTICAL METHODS
# ══════════════════════════════════════════
story += [section_banner(4, "CHOOSING APPROPRIATE STATISTICAL METHODS"), sp(6)]
story += [
    Paragraph("Step 1 — Define Your Variables", ST["h2"]),
    tbl(
        ["Variable Type", "Sub-type", "Examples"],
        [
            ["Numerical", "Discrete (count)",       "Number of children, hospital visits"],
            ["Numerical", "Continuous (measurement)","Blood pressure, weight, height"],
            ["Categorical","Nominal (no order)",     "Blood group, gender, religion"],
            ["Categorical","Ordinal (has order)",    "Mild/Moderate/Severe, Likert scale (1-5)"],
        ],
        widths=[3.5*cm, 5*cm, CW - 8.5*cm]
    ),
    sp(8),

    Paragraph("Step 2 — Parametric vs Non-Parametric Tests", ST["h2"]),
    tbl(
        ["Feature", "Parametric", "Non-Parametric"],
        [
            ["Assumes normal distribution?", "YES",            "NO (distribution-free)"],
            ["Power",                        "More powerful",  "Less powerful"],
            ["Use when data is...",          "Normally distributed", "Skewed, ordinal, or small sample"],
        ],
        widths=[5*cm, 4*cm, CW - 9*cm]
    ),
    sp(8),

    Paragraph("Step 3 — Check Normality of Data", ST["h2"]),
    tbl(
        ["Test", "When to Use", "Interpretation"],
        [
            ["Shapiro-Wilk",          "n < 50 (small samples)",  "p > 0.05 = normally distributed"],
            ["Kolmogorov-Smirnov",    "n >= 50 (large samples)", "p > 0.05 = normally distributed"],
        ],
        widths=[4*cm, 4*cm, CW - 8*cm]
    ),
    sp(4),
    coloured_box("Skewness and Kurtosis Rules", [
        "Values within ±1.0 = normal distribution",
        "< -1 = Left (negative) skew  |  > +1 = Right (positive) skew",
        "< -1 kurtosis = Platykurtic (flat)  |  > +1 = Leptokurtic (peaked)",
    ], C_LYELL),
    sp(8),

    Paragraph("Step 4 — Statistical Test Selection Table (MEMORISE THIS)", ST["h2"]),
    exam_tip("This is the MOST IMPORTANT table in statistics. The examiner WILL ask you to choose a test."),
    sp(4),
    tbl(
        ["Outcome Variable", "2 Unrelated Groups", "2 Related Groups", "3+ Unrelated Groups", "3+ Related Groups"],
        [
            ["Continuous (Normal)",    "Two-sample t-test",           "Paired t-test",           "ANOVA",          "Mixed-effects model"],
            ["Ordinal / Non-normal",   "Mann-Whitney U test",         "Wilcoxon signed rank",    "Kruskal-Wallis", "Friedman test"],
            ["Categorical",            "Chi-square / Fisher exact",   "McNemar test",            "Chi-square / Fisher exact", "Cochran Q test"],
        ],
        widths=[3.5*cm, 3.5*cm, 3.5*cm, 3.5*cm, 3*cm]
    ),
    sp(8),

    Paragraph("Chi-Square vs Fisher's Exact Test", ST["h2"]),
    tbl(
        ["Criterion", "Chi-Square", "Fisher's Exact"],
        [
            ["Sample size",       "LARGE",       "SMALL"],
            ["Accuracy",          "Approximate", "Exact"],
            ["Table size",        "Any size",    "Usually 2x2"],
            ["Expected freq rule","<20% of cells have expected freq <5", ">20% of cells have expected freq <5"],
            ["Interpretation",    "Pearson residuals", "Odds Ratio"],
        ],
        widths=[4.5*cm, 5*cm, CW - 9.5*cm]
    ),
    sp(4),
    mnemonic("<b>\"F before C = F for Few\"</b>  —  Small sample = Fisher's Exact  |  Large sample = Chi-Square"),
    sp(8),

    Paragraph("Regression Models", ST["h2"]),
    tbl(
        ["Outcome Variable", "Measure of Association", "Regression Model"],
        [
            ["Continuous",       "Difference in means", "Linear Regression"],
            ["Binary (Yes/No)",  "Odds Ratio",          "Logistic Regression"],
            ["Count data",       "Rate Ratio",          "Poisson Regression"],
        ],
        widths=[4.5*cm, 4.5*cm, CW - 9*cm]
    ),
    sp(8),

    Paragraph("Confounding, Interaction Effects & P-Hacking", ST["h2"]),
    tbl(
        ["Concept", "Definition", "Example"],
        [
            ["Confounding variable", "A third variable that distorts the relationship between independent and dependent variables",
             "Hot weather causes BOTH ice cream sales AND drowning — weather is the confounder"],
            ["Main effect", "Independent influence of ONE variable on the outcome (others held constant)",
             "Exercise lowers BP on average across all age groups"],
            ["Interaction effect", "Effect of one variable DEPENDS ON level of another variable",
             "Exercise effect on BP is STRONGER in young people than older people"],
            ["P-Hacking", "Manipulating data/tests until p<0.05 is obtained — produces FALSE POSITIVES",
             "Trying multiple variable combinations until a 'significant' result appears by chance"],
        ],
        widths=[3.5*cm, 5.5*cm, CW - 9*cm]
    ),
    sp(8),

    coloured_box("Statistical vs Clinical Significance — KEY CONCEPT", [
        "Drug A: survival +5 YEARS  |  Drug B: survival +5 MONTHS",
        "BOTH had p = 0.005 (statistically significant)",
        "Drug A: HIGH clinical significance  |  Drug B: LOW clinical significance",
        "LESSON: Statistical significance does NOT equal clinical significance!",
        "If 95% CI includes 1 → Relative Risk (RR) is NOT significant (p >= 0.05)",
    ], C_LYELL),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 5 — LITERATURE REVIEW
# ══════════════════════════════════════════
story += [section_banner(5, "LITERATURE REVIEW"), sp(6)]
story += [
    Paragraph("What is a Literature Review?", ST["h2"]),
    Paragraph("A <b>survey of scholarly sources on a specific topic</b>. It provides an overview of current knowledge, "
              "allowing you to identify relevant theories, methods, and <b>gaps in existing research</b> "
              "that you can apply to your own research or thesis.", ST["body"]),
    sp(6),

    Paragraph("Purpose of Literature Review (5 purposes)", ST["h2"]),
    tbl(
        ["#", "Purpose", "Plain meaning"],
        [
            ["1", "Demonstrate familiarity with the topic",              "Show you know what has been done"],
            ["2", "Develop theoretical framework and methodology",       "Build the foundation for your research"],
            ["3", "Position your work in relation to other researchers", "Show where YOUR study fits in the field"],
            ["4", "Show how your research addresses a gap",              "Justify WHY your study is needed"],
            ["5", "Evaluate current state of research",                  "Show you understand ongoing scholarly debates"],
        ],
        widths=[0.8*cm, 6.5*cm, CW - 7.3*cm]
    ),
    sp(8),

    Paragraph("5 Key Steps to Writing a Literature Review", ST["h2"]),
    mnemonic("<b>\"Some Elephants In Our World\"</b>  —  Search · Evaluate · Identify · Outline · Write"),
    sp(6),
]

steps = [
    ("STEP 1: Search for Relevant Literature", [
        "Brainstorm: What to search? Where to search? How to search? Who can help?",
        "Search engines: PubMed (medical/biomedical), Google Scholar (broad), Scopus (international)",
        "Sources: Journal articles, Book chapters, Conference papers, Reports, Reviews",
        "Search can be done Online or Offline",
    ]),
    ("STEP 2: Evaluate and Select Sources — ask 8 Wh-Questions", [
        "1. What question or problem is the author addressing?",
        "2. What are the key concepts and how are they defined?",
        "3. What are the key theories, models, and methods?",
        "4. Does it use established frameworks or an innovative approach?",
        "5. What are the results and conclusions of the study?",
        "6. How does it relate to other literature in the field?",
        "7. Does it confirm, add to, or challenge established knowledge?",
        "8. What are the strengths and weaknesses of the research?",
        "Remember: Take notes and cite sources (APA or Vancouver Style)",
    ]),
    ("STEP 3: Identify Themes, Debates, and Gaps", [
        "Group articles by common THEMES (what do many studies agree on?)",
        "Note DEBATES (what do studies disagree on?)",
        "Find GAPS (what has NOT been studied yet?) — this justifies your research",
    ]),
    ("STEP 4: Outline the Structure (4 types)", [
        "Chronological — oldest to newest studies (by time order)",
        "Thematic (Qualitative Analysis) — organized by topics/themes (MOST COMMON)",
        "Methodological — organized by research methods used",
        "Theoretical — organized by different theoretical frameworks",
    ]),
    ("STEP 5: Write Your Literature Review (3 parts)", [
        "Introduction: Clearly establish the FOCUS and PURPOSE of the review",
        "Body: Summarize & synthesize | Analyze & interpret | Critically evaluate | Well-structured paragraphs",
        "Conclusion: Summarize KEY FINDINGS and emphasize their significance",
    ]),
]

for title, pts in steps:
    story.append(KeepTogether([
        Paragraph(title, ST["h3"]),
        *[Paragraph(f"• {p}", ST["bull"]) for p in pts],
        sp(4),
    ]))

story += [
    sp(6),
    Paragraph("Boolean Search Operators (for literature searching)", ST["h2"]),
    tbl(
        ["Operator", "Effect", "Medical Example", "Result"],
        [
            ["AND", "Narrows (both must be present)", "Fever AND Joint Pain",       "4,432 results — only articles about BOTH topics"],
            ["OR",  "Broadens (either word)",          "Fever OR Joint Pain",        "317,869 results — any article about either topic"],
            ["NOT", "Excludes term",                   "Typhoid fever NOT children", "10,758 results — removes all children-related articles"],
            ['( )', "Groups terms (controls order)",   "(Fever AND joint pain) OR chills", "16,774 results — parentheses control logic order"],
            ['" "', "Exact phrase search",             '"Joint Pain"',               "Fewer, more precise results — finds exact phrase"],
        ],
        widths=[1.5*cm, 3.5*cm, 5*cm, CW - 10*cm]
    ),
    sp(8),

    Paragraph("Literature Review Summary Table Template", ST["h2"]),
    Paragraph("Know these 6 columns — this is the standard template from your slides:", ST["body"]),
    tbl(
        ["Author(s) & Year", "Title", "Objective", "Methodology", "Key Findings", "Conclusion"],
        [
            ["Author 1 et al., YYYY", "Title of article", "State aim of study",
             "Methods used (survey, experiment, review)", "Significant results/trends", "Main takeaway/implications"],
            ["Wakefield et al., 2010", "Impact of tobacco advertising bans", "Assess effect of ad bans",
             "Systematic review across countries", "Bans reduced smoking in youth", "Complete bans critical for tobacco control"],
            ["Jha & Peto, 2014", "Global effects of smoking, quitting, taxing", "Examine cessation impact",
             "Epidemiological analysis of global data", "Tax + cessation reduced mortality", "Higher taxes are cost-effective"],
        ],
        widths=[2.8*cm, 2.5*cm, 2.8*cm, 3*cm, 3*cm, 2.4*cm]
    ),
    sp(8),

    Paragraph("Conceptual Framework", ST["h2"]),
    Paragraph("A visual diagram showing <b>relationships between variables</b> in your study.", ST["body"]),
    tbl(
        ["Purpose", "Meaning"],
        [
            ["Clarify concepts and propose relationships", "Shows how variables connect in your study"],
            ["Explain observations",                       "Provides logical explanation for what you observe"],
            ["Provide context for interpreting findings",  "Helps make sense of your study results"],
            ["Encourage theory development",               "Helps build theories useful to real-world practice"],
        ],
        widths=[6.5*cm, CW - 6.5*cm]
    ),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 6 — CITATION
# ══════════════════════════════════════════
story += [section_banner(6, "CITATION & REFERENCES"), sp(6)]
story += [
    Paragraph("What is a Citation?", ST["h2"]),
    Paragraph("A citation tells readers that material in your work came from another source. "
              "<b>Without citation = PLAGIARISM.</b>", ST["body"]),
    coloured_box("Why Cite Sources?", [
        "Credits the original author — avoids plagiarism",
        "Helps readers find and verify sources",
        "Shows the amount of research you have done",
        "Protects you from being blamed for someone else's incorrect ideas",
    ], C_LBLUE),
    sp(8),

    Paragraph("Citation Styles Comparison", ST["h2"]),
    tbl(
        ["Style", "Used in", "In-text Format", "Order"],
        [
            ["Vancouver", "Medicine / Biomedical (MOST IMPORTANT FOR PSM)", "Superscript numbers 1,2,3", "Order of appearance"],
            ["APA 7th ed", "Psychology, Social Sciences",                   "(Author, Year)",             "Alphabetical"],
            ["MLA",        "Humanities, Literature",                        "(Author Page)",              "Alphabetical"],
            ["Chicago",    "History, Arts",                                 "Footnotes/Endnotes",         "Alphabetical"],
        ],
        widths=[2.5*cm, 5.5*cm, 4*cm, CW - 12*cm]
    ),
    sp(8),

    Paragraph("Vancouver Style Format (for journal articles)", ST["h2"]),
    Paragraph("<b>Format:</b>  Author(s). Title of article. <i>Journal Name</i>. Year;Volume(Issue):Pages.", ST["body"]),
    Paragraph("<b>Rules:</b><br/>"
              "• Authors listed as Surname then Initials<br/>"
              "• Up to 6 authors, then 'et al.'<br/>"
              "• References numbered in ORDER OF APPEARANCE in text<br/>"
              "• Numbers in text appear as superscripts: like this<super>1</super>",
              ST["body"]),
    coloured_box("Vancouver Example (from your slides)", [
        "Vaidya SS, Aryal UR, Grjibovski AM, Krettek A. Visual status in primary open-angle glaucoma: "
        "A hospital-based report from Nepal. Journal of Kathmandu Medical College. 2014 Oct 9;3(2):49-57.",
    ], C_LGREEN, C_GREEN),
    sp(8),

    Paragraph("APA Style (7th Edition) Format", ST["h2"]),
    Paragraph("<b>Format:</b>  Author, A. A., &amp; Author, B. B. (Year). Title of article. "
              "<i>Journal Name, Volume</i>(Issue), Pages. https://doi.org/xxxxx", ST["body"]),
    coloured_box("APA Example (from your slides)", [
        "Killinger O. (2022). Examining the relationships between high school students' interests in music "
        "and their creativity. International Journal on Social & Education Sciences (IJonSES), 4(3).",
    ], C_LGREEN, C_GREEN),
    sp(8),

    Paragraph("Vancouver vs APA — Quick Comparison", ST["h2"]),
    tbl(
        ["Feature", "Vancouver", "APA"],
        [
            ["In-text citation",   "Superscript number 1",          "(Author, Year)"],
            ["Reference order",    "By order of appearance in text","Alphabetical by author surname"],
            ["Year placement",     "Near end, before volume/pages", "In parentheses right after author(s)"],
            ["Journal name",       "Full or abbreviated name",      "Full name in italics"],
            ["Best used for",      "Medical journals, PSM",         "Psychology, social sciences"],
        ],
        widths=[4*cm, 5*cm, CW - 9*cm]
    ),
    sp(8),

    Paragraph("Bibliography", ST["h2"]),
    Paragraph("From Greek: <i>biblion</i> (book) + <i>graphia</i> (to write).<br/>"
              "An <b>alphabetical list of ALL sources consulted</b> (including those not directly cited).<br/>"
              "Written on a <b>separate page</b> at the end, with <b>hanging indentation</b>.",
              ST["body"]),
    tbl(
        ["For Books/Journals — include:", "For Websites — include:"],
        [["Author name, Title, Article title (if journal), Date of publication, Place, Publisher, Volume, Page numbers",
          "Author/editor, Website title, Organization/company, URL, DATE OF ACCESS"]],
        widths=[CW/2, CW/2]
    ),
    sp(6),
    coloured_box("Citation Management Software", [
        "Mendeley  |  Zotero  |  EndNote  |  RefWorks",
    ], C_LYELL),
    PageBreak(),
]

# ══════════════════════════════════════════
# TOPIC 7 — QUICK REVISION
# ══════════════════════════════════════════
story += [section_banner(7, "QUICK REVISION TABLES & ALL MNEMONICS"), sp(6)]
story += [
    Paragraph("All Validity Types — One-Line Definitions", ST["h2"]),
    tbl(
        ["Type", "One-line definition"],
        [
            ["Face",                 "Looks valid on the surface (informal, subjective judgment)"],
            ["Content",              "Covers ALL domains (judged by panel of experts)"],
            ["Construct",            "Measures the underlying concept (tested statistically)"],
            ["Convergent (Construct)","Correlates WITH similar/related variables"],
            ["Discriminant (Construct)","Does NOT correlate with dissimilar variables"],
            ["Criterion-Concurrent", "Agrees with gold standard measured NOW (same time point)"],
            ["Criterion-Predictive", "Predicts a future outcome (measured at a later time)"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(8),

    Paragraph("All Reliability Methods — One-Line Definitions", ST["h2"]),
    tbl(
        ["Method", "One-line definition"],
        [
            ["Test-Retest",          "Same test, same people, different time — should give same results"],
            ["Inter-rater",          "Same thing rated by different raters — should agree (Cohen's Kappa)"],
            ["Internal Consistency", "All items measure the same thing — Cronbach's Alpha >= 0.7"],
            ["Split-Half",           "Two halves of the test should give similar results to each other"],
            ["Parallel Forms",       "Two equivalent test versions given to same group — should agree"],
        ],
        widths=[5*cm, CW - 5*cm]
    ),
    sp(8),

    Paragraph("All Statistical Tests — Complete Quick Reference", ST["h2"]),
    tbl(
        ["Situation", "Correct Test"],
        [
            ["2 groups, continuous, NORMAL distribution",              "Independent (two-sample) t-test"],
            ["2 groups, continuous, NON-NORMAL or ordinal",            "Mann-Whitney U test"],
            ["2 groups, SAME people (paired/repeated), normal",        "Paired t-test"],
            ["2 groups, SAME people, non-normal",                      "Wilcoxon signed rank test"],
            ["3+ groups, continuous, normal",                          "ANOVA"],
            ["3+ groups, non-normal or ordinal",                       "Kruskal-Wallis test"],
            ["3+ groups, SAME people, normal (repeated)",              "Mixed-effects model / Repeated measures ANOVA"],
            ["3+ groups, SAME people, non-normal",                     "Friedman test"],
            ["2 categorical variables, large sample",                  "Chi-square test"],
            ["2 categorical variables, small sample / any cell < 5",   "Fisher's Exact test"],
            ["2 categorical variables, SAME people (before/after)",    "McNemar test"],
            ["3+ categorical variables, same people",                  "Cochran Q test"],
            ["Check normality, n < 50",                                "Shapiro-Wilk test"],
            ["Check normality, n >= 50",                               "Kolmogorov-Smirnov test"],
        ],
        widths=[9*cm, CW - 9*cm]
    ),
    sp(8),

    Paragraph("All Mnemonics — Complete Reference", ST["h2"]),
    tbl(
        ["Topic", "Mnemonic", "Full Meaning"],
        [
            ["Types of Validity",        '"Funny Cats Chase Prey"',      "Face · Content · Construct · Predictive (Criterion)"],
            ["Reliability Methods",      '"Tigers In India Sometimes Purr"', "Test-Retest · Inter-rater · Internal Consistency · Split-Half · Parallel Forms"],
            ["Construct Validity",       '"Come Down"',                  "Convergent = similar agree | Discriminant = dissimilar don't agree"],
            ["5 Steps of Lit Review",    '"Some Elephants In Our World"',"Search · Evaluate · Identify · Outline · Write"],
            ["4 Lit Review Structures",  '"Cows Think More Than"',       "Chronological · Thematic · Methodological · Theoretical"],
            ["Table Columns",            '"A-T-O-M-K-C"',               "Author · Title · Objective · Methodology · Key Findings · Conclusion"],
            ["Boolean Operators",        '"AON Rule"',                   "AND = narrows | OR = opens/broadens | NOT = removes/excludes"],
            ["Fisher vs Chi-square",     '"F before C = F for Few"',     "Small sample = Fisher's Exact | Large = Chi-Square"],
            ["Pre-testing Methods",      '"FRI"',                        "Focus groups · Respondent interviews · IRB approval"],
            ["Cronbach's Alpha",         '"Seven is Heaven"',            "α >= 0.7 is minimum acceptable threshold"],
        ],
        widths=[4*cm, 5*cm, CW - 9*cm]
    ),
    sp(8),

    exam_tip(
        "TOP 8 EXPECTED EXAM QUESTIONS:\n"
        "1. Define validity. Explain its types with examples.\n"
        "2. What are the methods of reliability? Explain any two with examples.\n"
        "3. What is Cronbach's alpha / Cohen's Kappa? How do you interpret the values?\n"
        "4. How do you choose an appropriate statistical test? (Show the selection table)\n"
        "5. Explain the 5 steps of a literature review in detail.\n"
        "6. What is Boolean search? Give examples of AND, OR, and NOT.\n"
        "7. Differentiate Vancouver and APA citation styles with examples.\n"
        "8. What is pre-testing? What are its methods, benefits, and how is it conducted?"
    ),
    sp(8),
]

# Footer banner
foot = Table([[
    [
        Paragraph("GOOD LUCK IN YOUR EXAM TOMORROW!", ParagraphStyle("fl",
            fontName="Helvetica-Bold", fontSize=14, textColor=C_WHITE, alignment=TA_CENTER, spaceAfter=4)),
        Paragraph("Pre-testing  ·  Validity  ·  Reliability  ·  Statistics  ·  Literature Review  ·  Citation",
            ParagraphStyle("fs", fontName="Helvetica", fontSize=9, textColor=HexColor("#bfdbfe"), alignment=TA_CENTER)),
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    ("LEFTPADDING",  (0,0),(-1,-1), 16),
]))
story.append(foot)

doc.build(story)
print("PDF built:", OUTPUT)

Running Command

python3 /home/daytona/workspace/psm-study-guide/build_v2.py 2>&1

Running Command

# Verify the new PDF is valid and check its size + page count python3 -c " from pypdf import PdfReader r = PdfReader('/home/daytona/workspace/psm-study-guide/PSM_Study_Guide_v2.pdf') print('Pages:', len(r.pages)) for i in range(min(3, len(r.pages))): t = r.pages[i].extract_text() print(f'Page {i+1} preview:', t[:120].replace('\n',' ')) " ls -lh /home/daytona/workspace/psm-study-guide/PSM_Study_Guide_v2.pdf

The new PDF is verified - 14 pages, all content readable. Here it is:

PSM Study Guide v2
PDF Document · PDF

The previous version had ROUNDEDCORNERS styling that some PDF viewers don't support. This new version removes that and uses only standard, universally compatible PDF elements - it should open on any device (phone, laptop, Adobe Reader, browser, etc.).

If it still doesn't open, try right-clicking the link → Save link as → then open the saved file directly.

Tomorrow is my psm viva. And professor will aks from this research proposal that I made. Please provide me possible question he might ask from this

Running Command

python3 -c " import pdfplumber fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/Research proposal.Shishir.pdf' with pdfplumber.open(fpath) as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() if text and text.strip(): print(f'--- PAGE {i+1} ---') print(text) " 2>&1

I've read your full proposal. Here are all the possible viva questions with model answers — organized by topic exactly as a professor would ask:

🎓 PSM VIVA — POSSIBLE QUESTIONS FROM YOUR RESEARCH PROPOSAL

"Association of Diurnal Sleeping Pattern and Obesity" — Shishir Subedi, KAHS

🔴 SECTION 1: TITLE & BASICS (Professor asks these FIRST)

Q1. What is the title of your research?

"Association of Diurnal Sleeping Pattern and Obesity among adults aged 18–60 years in Jumla, Nepal."

Q2. What do you mean by "diurnal sleeping pattern"?

Diurnal means relating to the day. Diurnal sleeping pattern refers to sleep behaviors during daytime or the 24-hour sleep-wake cycle — including sleep duration, sleep timing (what time you go to bed and wake up), and sleep quality. In this study, I am examining how these patterns relate to obesity.

Q3. Why did you choose this topic?

Obesity is a growing public health problem in Nepal linked to diabetes and cardiovascular disease. Emerging evidence shows sleep disturbances disrupt circadian rhythms, affecting hormones like leptin and ghrelin that regulate appetite. However, this relationship is understudied in low-resource settings like Nepal — especially in Karnali Province. This study fills that gap by providing locally relevant data.

Q4. What is the general objective of your study?

To assess the relationship between diurnal sleep patterns and obesity among adults in Jumla.

Q5. What are your specific objectives?

To determine the prevalence of obesity and irregular sleep patterns in the study population

To examine the association between sleep duration, timing, and obesity

To identify confounding factors (e.g., diet, sedentary behavior) influencing this relationship

Q6. What is your research hypothesis?

Adults with prolonged diurnal sleeping patterns have a higher likelihood of being obese.

🟠 SECTION 2: STUDY DESIGN (Very likely to be asked)

Q7. What study design did you use and why?

I used a cross-sectional study design. It is appropriate because:

It measures exposure (sleep pattern) and outcome (obesity) at the same point in time

It is cost-effective and quick to conduct

Suitable for assessing prevalence and associations

Appropriate for a student-level research proposal

Q8. What is the limitation of cross-sectional design?

The major limitation is that it cannot establish causality. We cannot determine whether poor sleep CAUSES obesity, or whether obesity DISRUPTS sleep — the direction of the relationship cannot be confirmed. This is called the "chicken-and-egg problem" or reverse causality.

Q9. What would be a better design to establish causality?

A cohort study (prospective longitudinal design) — where we follow participants over time and observe who develops obesity among those with and without disturbed sleep patterns. This would allow causal inference.

Q10. What is your research method?

Quantitative survey with anthropometric measurements. Primary data will be collected through structured questionnaires and physical measurements (BMI, waist circumference).

🟡 SECTION 3: SAMPLING (Professors LOVE asking this)

Q11. What is your sample size and how did you calculate it?

Sample size N = 100, calculated using the formula: n = z²pq / e² Where:

z = 1.96 (standard normal variant at 95% confidence level)

p = 0.20 to 0.70 (expected prevalence based on previous studies)

q = 1 - p

e = 0.08 (8% acceptable error)

Q12. Why did you use p = 20% or 70%?

p = 20% was used based on estimated prevalence of obesity in similar Nepali populations. p = 70% was used for sleep disorder prevalence. The higher value gives a larger (safer) sample size, so I used the one that gave n = 100 as a feasible minimum for a student project.

Q13. What sampling technique did you use?

Stratified random sampling in three stages:

Stratification by demographics: Urban vs. rural areas, age groups (18–30, 31–45, 46–60 years), and gender

Cluster sampling — randomly select 10 health centers (5 urban Nagarpalika, 5 rural Gaupalika) across Jumla

Systematic random sampling — from each health center's patient registry, every 5th eligible adult is invited until strata quotas are met

Q14. Why did you use stratified random sampling instead of simple random sampling?

Stratified sampling ensures proportional representation of important subgroups (urban/rural, age, gender). Simple random sampling might by chance over-represent one group. Since obesity and sleep patterns differ across age, gender, and urban/rural settings, stratification ensures the sample reflects the true population diversity.

Q15. What are your inclusion criteria?

Adults aged 18–60 years residing in Karnali Province for ≥6 months

Willing to provide written informed consent

No diagnosed sleep disorders (e.g., sleep apnea) — to isolate the effect of diurnal patterns

Q16. What are your exclusion criteria and why?

Pregnant women — hormonal changes affect both weight and sleep independently

Shift workers — have irregular sleep schedules not related to diurnal patterns

Chronic illnesses (e.g., thyroid disorders) or medications affecting sleep/weight — these confound the relationship

Q17. What is your study site and why?

The study will be carried out at the Out-Patient Department (OPD) of KAHS Hospital, Jumla — a tertiary level hospital with high patient flow, enabling easy recruitment of the required sample size.

🟢 SECTION 4: VARIABLES & CONCEPTUAL FRAMEWORK

Q18. What are your independent and dependent variables?

Independent variable: Diurnal sleep patterns — sleep duration (short <6 hrs, optimal 7–9 hrs, long >9 hrs), sleep timing (early vs. late bedtime, social jetlag), sleep quality (fragmented sleep, insomnia)

Dependent variable: Obesity indicators — BMI (≥30 kg/m²), waist circumference, body fat percentage

Q19. What are the confounding variables in your study?

Age, gender, socioeconomic status, genetic predisposition to obesity, stress and mental health status, diet (junk food), physical activity, and caffeine/alcohol consumption.

Q20. Explain your conceptual framework.

The framework links diurnal sleep patterns (independent variable) to obesity (dependent variable) through mediating pathways:

Hormonal imbalance — sleep deprivation decreases leptin (satiety hormone) and increases ghrelin (hunger hormone), leading to increased caloric intake. Cortisol also rises, promoting visceral fat.

Disrupted glucose metabolism — short sleep reduces insulin sensitivity, causing hyperglycemia and fat storage.

Behavioral factors — sleep loss increases cravings for high-calorie foods and reduces physical activity.

Circadian misalignment — late-night sleepers ("night owls") have higher BMI due to delayed melatonin and disrupted lipid metabolism. Confounders like diet, stress, and genetics are controlled in the analysis.

Q21. What are leptin and ghrelin? Why are they important in your study?

Leptin is the "satiety hormone" produced by fat cells — it signals the brain to stop eating. Sleep deprivation DECREASES leptin.

Ghrelin is the "hunger hormone" produced by the stomach — it stimulates appetite. Sleep deprivation INCREASES ghrelin. Together, this leads to overeating and weight gain — which is the key hormonal mechanism linking poor sleep to obesity.

Q22. What is social jetlag?

Social jetlag is the discrepancy in sleep timing between workdays and weekends/free days. For example, sleeping at 11 PM on weekdays but at 2 AM on weekends. This creates a form of chronic circadian misalignment similar to flying across time zones repeatedly, and is associated with higher BMI.

🔵 SECTION 5: DATA COLLECTION TOOLS

Q23. What tools did you use to collect data?

Pittsburgh Sleep Quality Index (PSQI) — a validated questionnaire assessing sleep duration, latency, efficiency, and disturbances

Sleep Timing Questionnaire — bedtime, wake time, social jetlag

WHO STEPS Survey — physical activity, sedentary behavior, dietary habits

Anthropometric measurements — calibrated weighing scale + stadiometer (for BMI), measuring tape (for waist circumference)

7-Day Sleep Diary — daily self-recorded sleep/wake times

Perceived Stress Scale-4 (PSS-4) — stress levels

Bioelectrical Impedance Analysis (BIA) — optional, for body fat percentage

Q24. What is the Pittsburgh Sleep Quality Index (PSQI)?

The PSQI is a validated, self-administered questionnaire that measures sleep quality over the past month. It assesses 7 components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. Total score ranges from 0–21; a score >5 indicates poor sleep quality.

Q25. How did you measure BMI?

BMI = Weight (kg) / Height² (m²) Measured using calibrated weighing scales and stadiometers. Obesity is defined as BMI ≥ 30 kg/m².

Q26. How did you measure waist circumference?

Using a measuring tape at the midpoint between the lower rib and the iliac crest, with the participant standing and breathing normally. This measures abdominal/central obesity.

Q27. What is the cut-off for waist circumference to define abdominal obesity?

Males: > 90 cm (Asian cut-off) or > 102 cm (WHO global cut-off)

Females: > 80 cm (Asian cut-off) or > 88 cm (WHO global cut-off) Asian cut-offs are used for South Asian populations including Nepal.

Q28. Why did you do pilot testing and with how many participants?

Pilot testing was done with n = 20 participants to:

Refine the questionnaire and identify unclear or confusing questions

Test logistics and time required for data collection

Ensure Cronbach's alpha > 0.7 for internal consistency of tools

🟣 SECTION 6: BIAS (This is a FAVOURITE viva topic)

Q29. What biases can occur in your study? How will you minimize them?

Bias Cause Mitigation
Selection bias Non-representative sample Stratified random sampling, multiple sites
Measurement bias Self-reported sleep data (recall) Validated tools (PSQI), sleep diary, trained collectors
Confounding bias Diet, stress, genetics Multivariable logistic regression, stratified analysis
Social desirability bias Underreporting bad habits Anonymous surveys, neutral question framing
Observer bias Data collector's expectations Blinding, double data entry, inter-rater reliability (ICC >0.9)
Recall bias Misremembering sleep habits 7-day sleep diary instead of long-term recall
Publication bias Only significant results published Pre-registration on ClinicalTrials.gov, report all outcomes

Bias	Cause	Mitigation
Selection bias	Non-representative sample	Stratified random sampling, multiple sites
Measurement bias	Self-reported sleep data (recall)	Validated tools (PSQI), sleep diary, trained collectors
Confounding bias	Diet, stress, genetics	Multivariable logistic regression, stratified analysis
Social desirability bias	Underreporting bad habits	Anonymous surveys, neutral question framing
Observer bias	Data collector's expectations	Blinding, double data entry, inter-rater reliability (ICC >0.9)
Recall bias	Misremembering sleep habits	7-day sleep diary instead of long-term recall
Publication bias	Only significant results published	Pre-registration on ClinicalTrials.gov, report all outcomes

Q30. What is selection bias? How does it affect your study?

Selection bias occurs when the study sample is not representative of the target population. In my study, recruiting only from OPD may include people who are already unwell, which could over-represent certain health conditions. Stratified random sampling with multiple sites (urban and rural health centers) helps minimize this.

Q31. What is recall bias and how did you address it?

Recall bias occurs when participants cannot accurately remember past behaviors (e.g., "how many hours did you sleep last month?"). I addressed it by using a 7-day sleep diary for real-time recording, and by using the PSQI which limits recall to the past month only with specific structured questions.

⚪ SECTION 7: STATISTICAL ANALYSIS

Q32. What statistical tests will you use?

Descriptive statistics — frequency, mean, standard deviation for participant characteristics

Chi-square test — for association between categorical variables (e.g., sleep category vs. obese/non-obese)

Logistic regression — to determine the strength and direction of association between sleep patterns and obesity while adjusting for confounders (age, sex, physical activity)

Software: SPSS

Q33. Why logistic regression and not linear regression?

Because the dependent variable (obesity) is categorical/binary (obese = BMI ≥30 vs. non-obese = BMI <30). Logistic regression is used when the outcome is binary. Linear regression is used for continuous outcomes.

Q34. What does the logistic regression give you?

Logistic regression gives the Odds Ratio (OR) — which tells us how much more likely obese participants are to have a particular sleep pattern compared to non-obese participants, after adjusting for confounders. An OR > 1 means increased odds of obesity with that sleep pattern.

Q35. What is a confounder? Give an example from your study.

A confounder is a variable that is associated with BOTH the independent and dependent variables and can distort the true relationship between them. In my study, physical activity is a confounder — sedentary people tend to sleep more AND be more obese. If not controlled, physical activity could make it appear that sleep alone causes obesity. I control for confounders using multivariable logistic regression.

🔴 SECTION 8: ETHICS

Q36. What ethical considerations did you address?

Informed consent — written consent obtained by the principal investigator and trained research assistants

Voluntariness — participation is voluntary, participants can withdraw at any time without consequences

Confidentiality — anonymous IDs replace personal identifiers in datasets

Minimal risk — only discomfort during anthropometric measurement

Benefits — participants receive awareness about their own health status

Cultural sensitivity — study is designed to be sensitive to Nepali culture and social values

IRC approval — submitted to the Institutional Review Committee (IRC) of KAHS for ethical approval

Q37. Who approved your study ethically?

The study was submitted to the Institutional Review Committee (IRC) of Karnali Academy of Health Sciences (KAHS) for ethical approval before commencing.

Q38. Are vulnerable populations involved? How did you protect them?

No vulnerable populations (children, prisoners) are involved. However, pregnant women were excluded from the study because they represent a vulnerable group where hormonal changes independently affect sleep and weight, which could confound results.

🟠 SECTION 9: LIMITATIONS & IMPLICATIONS

Q39. What are the limitations of your study?

Cross-sectional design — cannot establish causality (reverse causality possible)

Limited generalizability — sample restricted to Jumla OPD; may not represent all Nepal populations

BMI limitation — BMI does not distinguish fat from muscle mass; may misclassify muscular individuals

Self-reported data — sleep diary and questionnaire data depend on participant honesty and memory

Exclusion of shift workers — may reduce generalizability

Q40. What are the expected outcomes of your research?

Identification of potential sleep-related behavioral risk factors for obesity in the Nepali context, providing data to inform:

Public health interventions — sleep hygiene education programs

Policy recommendations — integrating sleep health into existing obesity prevention programs

Scientific contribution — publication in peer-reviewed journals to add to global evidence on sleep-obesity association

🟡 BONUS QUICK-FIRE QUESTIONS

Question	Answer
What is BMI cut-off for obesity?	≥ 30 kg/m²
What is overweight BMI?	25–29.9 kg/m²
What is optimal sleep duration?	7–9 hours per night (adults)
What is short sleep?	< 6 hours
What is long sleep?	> 9 hours
What software will you use for analysis?	SPSS
What is your study population?	Adults aged 18–60 years
What is your study site?	OPD, KAHS Hospital, Jumla
How many references do you have?	10 references
What citation style did you use?	Vancouver style
What is the PSQI?	Pittsburgh Sleep Quality Index — score >5 = poor sleep
What is WHO STEPS?	WHO STEPwise approach to Surveillance — standardized tool for NCD risk factors
What is the total budget?	NPR 6,200
Is your study funded?	No
What is ICC?	Intraclass Correlation Coefficient — measures inter-rater reliability (>0.9 = excellent)
What is Cronbach's alpha used for?	Internal consistency of questionnaire (>0.7 acceptable)

🧠 MNEMONIC — Remember the key parts of your proposal:

"DOSE-BATS" = Design (Cross-sectional) · Objectives (3 specific) · Sampling (Stratified random) · Ethics (IRC KAHS) · Bias (7 types) · Analysis (Chi-square + Logistic regression) · Tools (PSQI, Anthropometry) · Site (KAHS OPD, Jumla)

Most likely first question from your professor: "Tell me about your study in 2-3 sentences."

Your answer: "My study investigates the association between diurnal sleep patterns and obesity among 100 adults aged 18-60 years in Jumla, Nepal. It uses a cross-sectional design with stratified random sampling. Data will be collected using the Pittsburgh Sleep Quality Index and anthropometric measurements, and analyzed using logistic regression on SPSS."

All the best for your viva tomorrow! 🎯

Given answer is very diffuct to memorize provide me very easy sentences so i will remeber

🎓 PSM VIVA — SUPER EASY ANSWERS

(Like talking to a friend — simple words, easy to remember!)

📌 FIRST THING PROFESSOR WILL ASK

"Tell me about your study in 2-3 sentences."

"I studied whether sleeping late or sleeping too little makes people fat. I took 100 adults from Jumla, measured their weight and sleep habits, and checked if there is a connection. I used a questionnaire and weighing scale to collect data."

🔴 BASICS

Q: What is diurnal sleep pattern?

"It means your daily sleep routine — when you sleep, how long you sleep, and how well you sleep."

Q: Why did you choose this topic?

"In Nepal, obesity is increasing. People also sleep less and sleep late. No one studied this connection in Jumla before. So I studied it."

Q: What is your objective?

"To find out — does bad sleep make people obese in Jumla?"

3 specific objectives — remember as 3 P's:

Prevalence → How many people are obese / sleep badly?
Pattern → Is sleep duration/timing linked to obesity?
Predictors → What other factors (diet, exercise) are involved?

Q: What is your hypothesis?

"People who sleep at wrong times or sleep too little are more likely to be obese."

🟠 STUDY DESIGN

Q: What design did you use?

"Cross-sectional study — I collected data from everyone at ONE point in time."

Q: Why cross-sectional?

"It is fast, cheap, and good for finding associations. Perfect for a student study."

Q: What is the limitation of cross-sectional?

"I cannot say which came first — bad sleep OR obesity. It's like asking: did the chicken come first or the egg?"

Q: What better design could you use?

"A cohort study — follow people over years and see who becomes obese."

🟡 SAMPLE SIZE & SAMPLING

Q: What is your sample size?

"100 adults, aged 18 to 60 years."

Q: What formula did you use?

"n = z²pq/e²"

z = 1.96 (confidence level 95%)

p = 20% (obesity prevalence)

e = 8% (error allowed)

Easy way to remember: "z-p-q-e formula, confidence 95%, prevalence 20%, error 8%"

Q: What sampling technique?

"Stratified random sampling — I divided people into groups first (urban/rural, age, gender), then randomly picked from each group."

Q: Why stratified?

"So that all types of people are included — young, old, male, female, urban, rural. Not just one type."

Q: How did you actually select people?

"3 steps:

Divided into groups (strata)

Randomly picked 10 health centers (5 urban, 5 rural)

From each center, selected every 5th patient from the register"

Q: Inclusion criteria?

"Adults 18-60 years, living in Karnali for 6+ months, willing to sign consent, and NO diagnosed sleep disorders."

Q: Exclusion criteria — and why?

Easy memory: "PSC" — Pregnant, Shift workers, Chronic illness

Pregnant → hormones change weight and sleep naturally

Shift workers → already sleep at wrong times (not their choice)

Chronic illness (thyroid etc.) → medicines affect weight/sleep

🟢 VARIABLES & CONCEPTUAL FRAMEWORK

Q: Independent vs dependent variable?

Independent (cause) = Sleep pattern (how long, when, how well)

Dependent (effect) = Obesity (BMI, waist size)

Easy: "Sleep → Obesity. Sleep is the cause I'm studying. Obesity is what I'm measuring."

Q: Confounding variables?

"Things that can confuse my results — like diet, exercise, stress, age, gender." Easy example: "A person may be obese because they eat a lot — not because of bad sleep. So I need to control for diet."

Q: Explain your conceptual framework simply.

"Bad sleep → hormones go wrong → person eats more and moves less → becomes obese."

The chain is:

Bad sleep → Leptin falls + Ghrelin rises → Hunger increases → Eats more → Obesity

Q: What is leptin? What is ghrelin?

Easy trick: "Leptin = Lean (stops eating). Ghrelin = Greedy (makes you hungry)."

Leptin = tells brain "I'm full" → bad sleep = less leptin = always hungry

Ghrelin = tells brain "I'm hungry" → bad sleep = more ghrelin = eat more

Q: What is social jetlag?

"Sleeping at different times on weekdays vs weekends. Like sleeping at 10 PM on weekdays but 2 AM on weekends. Your body gets confused — like flying to a different time zone every weekend."

🔵 DATA COLLECTION TOOLS

Q: What tools did you use? (Remember: PSWA)

PSQI — sleep quality questionnaire

Scale & tape — weighing scale + measuring tape (for BMI and waist)

WHO STEPS — diet and exercise questionnaire

Anthropometric measurements — weight, height, waist

Q: What is PSQI?

"Pittsburgh Sleep Quality Index — a standard questionnaire that gives a score for your sleep. Score more than 5 = bad sleep."

Q: How do you measure BMI?

"BMI = Weight divided by Height squared. Obesity = BMI ≥ 30."

Q: How do you measure waist circumference?

"Tape around the waist, at the midpoint between the lowest rib and the hip bone. Obesity in Asians = men > 90 cm, women > 80 cm."

Q: Why pilot testing? How many?

"To test if questions are clear and the process works well. Done on 20 people before the real study."

🟣 BIAS (Remember: 6 S's — Super Simple)

Bias	One-line meaning	My solution
Selection	Wrong people selected	Used stratified random sampling
Self-report (Recall)	People forget or lie about sleep	Used 7-day sleep diary (write daily)
Social desirability	People say what sounds good	Anonymous survey, neutral questions
Systematic (Measurement)	Wrong measurements taken	Trained staff, calibrated tools
Observer	Staff expects certain results	Blinded staff, double measurements
Confounding	Another factor is the real cause	Logistic regression to control confounders

Q: What is selection bias simply?

"When the people you study don't represent the real population. Like studying obesity by only going to a gym — you'll miss the obese people who don't go to gym."

Q: What is recall bias simply?

"People don't remember correctly. If I ask 'how many hours did you sleep last YEAR?' — nobody knows. So I used a 7-day sleep diary instead."

⚪ STATISTICS (Very simple)

Q: What tests will you use?

Easy memory: "D + C + L"

Descriptive stats = mean, frequency (describe the sample)

Chi-square = check if sleep category and obesity are related

Logistic regression = find how strongly sleep affects obesity, after removing effects of age, diet, exercise

Q: Why logistic regression and NOT linear regression?

"Because my outcome (obese vs not obese) is YES or NO — not a number. Logistic regression is for YES/NO outcomes. Linear regression is for number outcomes like blood pressure."

Q: What does logistic regression give you?

"Odds Ratio (OR). It tells me — people with bad sleep are HOW MANY TIMES more likely to be obese compared to those with good sleep."

Q: What is a confounder simply?

"A third person who is secretly causing both things. Example: A person eats junk food (confounders) → sleeps badly AND becomes obese. So it LOOKS like sleep causes obesity, but it's actually the junk food."

🔴 ETHICS (Remember: "VCCRA")

Voluntary — no one is forced

Confidential — no names used, only codes

Consent — written consent taken before anything

Risk — only minimal (slight discomfort during measurement)

Approval — IRC KAHS approval obtained

Q: Who approved the study?

"IRC — Institutional Review Committee of KAHS, Jumla."

🟠 LIMITATIONS (Remember: "CGS")

Cross-sectional = can't prove cause and effect

Generalizability = only Jumla OPD, not all Nepal

Self-report = people may not report sleep accurately; BMI doesn't show fat vs muscle

⭐ QUICK FIRE — Numbers to Remember

What	Answer
Sample size	100
Age group	18–60 years
Study site	KAHS OPD, Jumla
Study design	Cross-sectional
Obesity BMI cut-off	≥ 30 kg/m²
Optimal sleep	7–9 hours
Short sleep	< 6 hours
PSQI cut-off	> 5 = bad sleep
Cronbach's alpha needed	> 0.7
Confidence level	95%
Budget	NPR 6,200
References	10 (Vancouver style)
Software	SPSS
Pilot testing size	20
Sampling error	8%

🧠 MASTER MNEMONIC — Remember your whole study:

"Shishir Studies Sleep Slowly at KAHS"

Shishir = your name (principal investigator)
Studies = cross-sectional study
Sleep = independent variable (diurnal sleep pattern)
Slowly = stratified random sampling (step by step)
at = anthropometric measurements (BMI + waist)
KAHS = study site, ethics approval from IRC KAHS

🗣️ HOW TO OPEN YOUR VIVA (say this confidently):

"My study is about the relationship between sleep patterns and obesity. I studied 100 adults aged 18 to 60 years at KAHS OPD in Jumla. I used a cross-sectional design, collected data using the PSQI questionnaire and anthropometric measurements, and analyzed the data using Chi-square test and logistic regression on SPSS. The main finding I expect is that people who sleep late or sleep less are more likely to be obese."

Say this first — professor will be impressed! 🎯

All the best for your viva Shishir! You know your study well — just speak in simple sentences like you're explaining it to a friend. 💪

Running Command

python3 -c " import pdfplumber fpath = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/LEPTOSPIROSIS%20Dr.Bipul%20Singh-compressed-compressed%C2%A0%281%29.pdf' with pdfplumber.open(fpath) as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() if text and text.strip(): print(f'--- PAGE {i+1} ---') print(text) " 2>&1

Running Command

python3 -c " import zipfile, re pptx_path = '/home/daytona/workspace/attachments/f4da7189-00eb-49ab-b288-d9609aab9ba0/High Altitude Sickness.pptx' with zipfile.ZipFile(pptx_path, 'r') as z: slide_files = sorted([f for f in z.namelist() if f.startswith('ppt/slides/slide') and 'rels' not in f]) print('Total slides:', len(slide_files)) for sf in slide_files: content = z.read(sf).decode('utf-8', errors='ignore') texts = re.findall(r'<a:t[^>]*>([^<]+)</a:t>', content) slide_num = re.search(r'slide(\d+)\.xml', sf).group(1) combined = ' | '.join([t.strip() for t in texts if t.strip()]) if combined: print(f'=== SLIDE {slide_num} ===') print(combined) print() " 2>&1

I've read everything! Here are your complete, super easy notes for both topics:

🦠 TOPIC 1: LEPTOSPIROSIS

(In Simple Language — Easy to Remember!)

🐀 WHAT IS LEPTOSPIROSIS? (The 3-sentence story)

"A rat pees in floodwater. A farmer walks barefoot through it. Bacteria enter through his skin cut — and he gets Leptospirosis."

That's the whole disease in one story! 🐀💧🦶

📛 NAME MEANING

"Lepto" = thin
"Spira" = coil
So Leptospira = thin coiled bacteria (spiral/corkscrew shaped)

🧫 THE GERM

Type: Spirochete bacteria (spiral shaped)
Most important species: Leptospira interrogans
Seen under: Dark field microscopy + silver staining
Size: 0.1–0.2 µm wide, 5–15 µm long, with hooked ends

🌍 REMEMBER: THE 3 Rs (from your slide - THIS IS THE KEY!)

🐀 RATS
🌾 RICEFIELDS  
🌧️ RAINFALL

"Rats + Ricefields + Rainfall = Leptospirosis"

📜 HISTORY

First described by Adolf Weil in 1886
He called it: "acute infectious disease with enlarged spleen, jaundice, and nephritis"
Also known as:
- "Rice field jaundice" (China)
- "Seven-day fever" (Japan)
- "Mud fever / Cane-cutter's disease" (Europe)
- "Black jaundice" (New Zealand)

Weil's Disease = the severe form with Jaundice + Kidney failure + Bleeding

📊 HOW BIG IS THE PROBLEM?

1 million cases per year worldwide
60,000 deaths per year
Most cases in warm, humid, tropical countries
Outbreaks happen after heavy rain and floods
Mortality overall: < 10%
In severe form: 5–15%
In pulmonary hemorrhage: > 50% 😱

🦠 AGENT FACTORS (Easy version)

Source: Infected animal URINE (rats pee the bacteria)

Animal reservoirs — remember "RCGPHDW":

"Rats Carry Germs, People Hate Dark Wetlands"

Rats & mice (most important - especially Rattus norvegicus and Mus musculus)
Cattle
Goats & sheep
Pigs
Horses
Dogs
Water buffalo

Humans = accidental dead-end hosts (we get infected but don't spread it further)

👨 HOST FACTORS

Factor	Details
Sex	Males > Females (more outdoor/farm work)
Age	20–45 years most commonly affected
Occupation	Farmers, rice/sugarcane workers, sewage workers, vets, miners, soldiers, fishermen, lab workers
Immunity	Infection gives serovar-specific immunity (only for that type)

Easy memory for at-risk jobs: "FARSVMS"

Farmers · Abattoir workers · Rice paddy workers · Sewage workers · Veterinarians · Miners · Soldiers

🌿 ENVIRONMENTAL FACTORS

Bacteria survive weeks in soil and water (especially warm, moist, alkaline soil)
Survive in soil with pH alkaline, temperature ≥ 22°C
Poor housing + poor sanitation = high risk
Floods and waterlogging spread the bacteria widely

🔄 MODES OF TRANSMISSION (Simple)

Think of it as: Animal pees → Bacteria in environment → Enters human body

A) DIRECT CONTACT (directly from animal):

Through skin cuts/wounds (farmer's cut hand touches infected cow urine)
Through eyes, nose, mouth (mucous membranes) — like cow urine splashing in eye while milking

B) INDIRECT CONTACT (MOST COMMON MODE):

Through broken skin touching contaminated water/soil/mud
Example: Walking barefoot through flood water contaminated with rat urine
Ingestion of contaminated water or food

C) DROPLET INHALATION:

Breathing air with urine droplets (e.g., while milking infected animals)

⭐ Most Common = Indirect contact with contaminated water through broken skin

Direct person-to-person transmission = RARE

🤒 CLINICAL PICTURE (What happens to the patient)

Incubation period = 10 days (range: 4–20 days)

TWO FORMS — Think "Mild vs Severe":

	Anicteric (Mild)	Icteric (Severe / Weil's Disease)
Jaundice?	❌ No	✅ YES (yellow skin/eyes)
Fever	✅	✅
Muscle pain	✅ (myalgia)	✅ (especially CALF muscles)
Headache	✅	✅
Conjunctival suffusion	✅	✅
Kidney involvement	Mild	Acute kidney failure
Bleeding tendency	Mild	YES — severe
Outcome	Usually recovers	Can be fatal (5–15% mortality)

⭐ KEY SIGN: CONJUNCTIVAL SUFFUSION

Red eyes WITHOUT pus/discharge (not like conjunctivitis — no sticky discharge) This PLUS jaundice = classic Leptospirosis

⭐ WEIL'S DISEASE = JAR (Jaundice + Acute kidney failure + bleeding/haemorrhage)

CALF MUSCLE TENDERNESS = very specific finding in Leptospirosis!

🔬 DIAGNOSIS

Remember: DARK + CULTURE + SEROLOGY

Test	When used	Details
Dark field microscopy	Early (blood)	Sees the spiral bacteria directly
Culture	Blood early; urine after week 1	Takes 1–6 weeks to become positive
MAT (Microscopic Agglutination Test)	After 7–10 days	Gold standard serological test
IgM ELISA	Very early	Positive as early as 2 days into illness!
Leptodipstick test	Quick field test	Now available

Lab findings:

Anaemia (blood loss + haemolysis)
WBC normal or elevated (mostly neutrophils)
WBC > 13,000 = poor prognosis sign
Thrombocytopenia (platelets ≤100×10⁹/L) in 40–60% = severe disease + bleeding risk

💊 TREATMENT

Remember: "PDA" = Penicillin, Doxycycline, Alternatives

Drug	Use
Penicillin	Drug of CHOICE (general)
Doxycycline	DOC in uncomplicated cases; also weekly for prophylaxis
Ceftriaxone, Cefotaxime	Severe cases
Tetracycline, Amoxicillin, Azithromycin	Alternatives

⭐ Weekly Doxycycline = prophylaxis for very high-risk groups (soldiers, farmers during floods)

Supportive care (for severe cases):

Careful fluid resuscitation
Haemodialysis for kidney failure
Respiratory support for lung involvement

🛡️ PREVENTION & CONTROL

Remember: "ERPP H" = Environmental + Rodent control + PPE + Prophylaxis + Health education

Method	Details
Environmental	Avoid contaminated water, proper waste disposal
Rodent control	Kill and control rats (main reservoir)
PPE	Long boots, gloves, long sleeves for workers
Vaccination	Available in Italy, Cuba, France, Russia, China for high-risk workers
Health education	Awareness during rainy season in endemic areas
Chemoprophylaxis	Doxycycline weekly for high-risk groups

⚡ DIFFERENTIAL DIAGNOSES (Diseases that look similar)

Remember: "DMSHY HE"

Dengue
Malaria
Scrub typhus
Hepatitis
Yellow fever
Hantavirus (rodent exposure + hemorrhagic fever + renal syndrome)
Enteric fever

🧠 NEPAL CONTEXT

21% positivity in Terai region (Regmi et al., 2017)
41.8% among encephalitis patients (2007-2008)
More common in Terai (low-lying) areas — hot, wet, flooded

⭐ LEPTOSPIROSIS IN PREGNANCY

Can cause foetal death or abortion — always mention this!

🏔️ TOPIC 2: HIGH ALTITUDE SICKNESS

(In Simple Language!)

🗻 THE STORY IN ONE LINE:

"You climb too fast to a high mountain. Your body doesn't get enough oxygen. Your brain and lungs swell. That's High Altitude Sickness."

WHAT IS HIGH ALTITUDE? (Easy numbers to remember)

Level	Height
High altitude starts	2,700 m (9,000 feet)
Very high altitude	3,601 – 4,500 m
Extreme high altitude	> 5,400 m (permanent acclimatization very difficult)
Acclimatization needed from	2,500 m onwards

Nepal examples: Jumla, Dolpa, Humla, Gosaikunda, Namche Bazar, Muktinath — all high altitude!

👥 WHO GETS HIGH ALTITUDE SICKNESS?

Remember: "M-S-T-A-M-P"

Mountaineers
Soldiers
Trekkers
Adventurers
Miners
Pilgrims and porters

Also: Native highlanders who go to lower areas and come BACK (their body "forgets"!)

🌬️ WHY DOES IT HAPPEN?

At high altitude, air pressure is lower → less oxygen per breath → body is starved of oxygen

The body also faces: low temperature, low humidity, more UV radiation, isolation

🏥 THE 3 TYPES OF HIGH ALTITUDE ILLNESS

Remember: "AMS → HACE → HAPE" (mild to severe)

AMS = Acute Mountain Sickness (mildest)
HACE = High Altitude Cerebral Edema (brain swells — dangerous!)
HAPE = High Altitude Pulmonary Edema (lungs fill with fluid — most fatal!)

1️⃣ AMS (Acute Mountain Sickness) — "The headache disease"

Symptoms appear 4–12 hours after reaching high altitude

Remember "HDFNSS":

Headache (most important symptom)
Dizziness
Fatigue
Nausea/vomiting
Sleep disturbance
Swelling of hands, feet, eyelids

⭐ Headache is the KEY symptom — no headache = no AMS

2️⃣ HACE (High Altitude Cerebral Edema) — "The brain swelling disease"

AMS that got worse — brain fills with fluid

Remember "UASA S":

Uncontrollable headache (despite painkillers)
Ataxia (can't walk straight — like drunk person)
Altered mental status (confused, hallucinations, unreasonable behaviour)
Somnolence → coma
Impaired Sight (vision problems)

⭐ ATAXIA (loss of coordination/balance) = key sign of HACE ⭐ HACE = MEDICAL EMERGENCY

3️⃣ HAPE (High Altitude Pulmonary Edema) — "The lung swelling disease"

Lungs fill with fluid — MOST DANGEROUS

Remember "WCSF B":

Weakness and poor performance
Cough (dry first, then foamy or bloody)
Shortness of breath (even at REST)
Fever (slight), Fast heart rate and breathing
Blue tongue (cyanosis)

⭐ Shortness of breath at REST = key sign of HAPE ⭐ HAPE is the most common cause of DEATH from altitude sickness

📊 LAKE LOUISE SCORE (LLS) — For Diagnosing AMS

How it works:

Needs: Recent altitude gain + HEADACHE
Score symptoms: headache, dizziness, nausea/vomiting, fatigue
Total score ≥ 3 = AMS diagnosis

Score	Severity
3–5	Mild AMS
6–9	Moderate AMS
10–12	Severe AMS

⭐ Must be assessed at least 6 hours after reaching the altitude

🔄 ACCLIMATIZATION (How the body adjusts)

"Giving your body time to get used to less oxygen — like slowly adjusting to cold water instead of jumping in"

When to acclimatize: From 2,500 m onwards

The rule: At altitudes > 3,000 m:

Don't go up more than 300 m per night
Rest day every 2–3 days (every 1,000 m of ascent)

How:

1–2 days complete rest
Then 2–4 days of gradually increasing physical activity
Repeat for every 1,000 m gained

🛡️ PREVENTION

The Golden Rule: "Climb High, Sleep Low"

Do NOT ascend > 300–500 m per night
Rest day every 3–4 days (every 800–1,200 m)

Prophylactic drugs:

Drug	Use
Acetazolamide (Diamox)	125 mg every 12 hours — for people with known susceptibility
Nifedipine	30 mg every 12 hours — if history of pulmonary edema
Tadalafil	10 mg every 12 hours — alternative

💊 TREATMENT (Easy table)

Condition	Action	Drug
Mild AMS (score 3–5)	Rest, no more ascent	Ibuprofen / Paracetamol (for headache), Metoclopramide/Domperidone (for nausea)
Severe AMS (score >5)	DESCEND at least 1,000 m	Acetazolamide 250 mg every 12 hr + Dexamethasone 4–8 mg every 6 hr
HACE	DESCEND immediately + hospital	Dexamethasone 4 mg IM/IV every 6 hr + O₂ 2–4 L/min
HAPE	DESCEND IMMEDIATELY	Nifedipine 20–30 mg every 8 hr (1st choice) + O₂ 2–4 L/min
Both HACE + HAPE	Descend + O₂	Nifedipine + Dexamethasone 4–8 mg every 6 hr

⭐ THE MOST IMPORTANT TREATMENT = DESCEND! Always descend first!

Drug memory tricks:

Acetazolamide = AMS (carbonic anhydrase inhibitor, makes you breathe faster)
Dexamethasone = brain/HACE (Dex for Da brain)
Nifedipine = lungs/HAPE (Nife for Nice lungs — calcium channel blocker, widens lung vessels)

🎒 GAMOW BAG

A portable inflatable pressure bag big enough for a person. Simulates descending to a lower altitude by increasing pressure around the person. Used when actual descent is impossible (bad weather, night time). Emergency field treatment!

⭐ QUICK COMPARISON TABLE — AMS vs HACE vs HAPE

Feature	AMS	HACE	HAPE
Organ affected	General	Brain	Lungs
Key symptom	Headache	Ataxia + confusion	Breathlessness at rest
Severity	Mild	Severe	Most fatal
Treatment	Rest + painkillers	Descend + Dexamethasone	Descend + Nifedipine
Emergency?	No	YES	YES (most dangerous)

🧠 MASTER MNEMONICS

For Leptospirosis:

3 Rs = Rats · Ricefields · Rainfall
Weil's = JAR = Jaundice + Acute kidney failure + haemmoRrhage
DOC = PD = Penicillin (general) · Doxycycline (uncomplicated)
Conjunctival suffusion = red eyes WITHOUT discharge (specific sign!)
Most common MOT = Indirect contact with contaminated water through broken skin
Humans = dead-end hosts (we don't spread it further)

For High Altitude Sickness:

AMS → HACE → HAPE = Brain then Lungs (severity increases)
"Climb High, Sleep Low" = prevention rule
300 m/night = maximum safe ascent
Key signs: AMS = headache · HACE = ataxia · HAPE = breathlessness at rest
Treatment: Acetazolamide = AMS · Dexamethasone = HACE · Nifedipine = HAPE
MOST IMPORTANT = DESCEND first, always!

⚡ QUICK NUMBERS CHEAT SHEET

Topic	Number
Leptospirosis incubation	10 days (4–20 days)
Leptospirosis cases/year	> 1 million
Leptospirosis deaths/year	60,000
Overall mortality	< 10%
Severe mortality	5–15%
Pulmonary hemorrhage mortality	> 50%
IgM ELISA positive by	Day 2 of illness
MAT becomes positive by	7–10 days
Culture takes	1–6 weeks
High altitude starts	2,700 m
Acclimatize from	2,500 m
Max ascent per night	300 m
Rest day every	2–3 days
AMS symptoms start	4–12 hours after ascent
Lake Louise Score for AMS	≥ 3 (with headache)
Diamox (prophylaxis) dose	125 mg every 12 hours
Nifedipine (HAPE) dose	20–30 mg every 8 hours
Dexamethasone (HACE) dose	4 mg every 6 hours

Best of luck tomorrow! Remember: 3 Rs for Lepto, 3 letters for Altitude (AMS-HACE-HAPE)! 🎯

Tomorrow is my psm practical exam and i dont know anything about RDA. please provide me answer in easy language and trick to remember all the necessary concepts also so that she will impress from my answer

Searching the Web

RDA Recommended Dietary Allowance PSM community medicine complete notes 2024

Finding Sources

Searching the Web

RDA India ICMR recommended dietary allowance values table calories protein vitamins minerals PSM Park

Finding Sources

Reading Web Page

https://www.nin.res.in/rdabook/brief_note.pdf

Finding Sources

Reading Web Page

https://www.slideshare.net/slideshow/recommended-dietary-all…

Finding Sources

Searching the Web

RDA India ICMR reference man woman calorie protein fat requirements sedentary moderate heavy work Park PSM exam

Reading Web Page

https://metabolichealthdigest.com/nutrient-requirements-for-…

I now have all the information I need. Here are your complete, super easy RDA notes!

🍽️ RDA (Recommended Dietary Allowance)

COMPLETE PSM EXAM NOTES — Super Easy Language!

🔑 WHAT IS RDA? (Say this definition confidently)

"RDA is the average daily amount of a nutrient that is sufficient to meet the nutritional requirements of nearly all (97.5%) healthy people in a population."

Simple version: "RDA = How much of each nutrient a healthy person needs to eat every day"

📛 FULL FORM & AUTHORITY

RDA = Recommended Dietary Allowance
Set by: ICMR (Indian Council of Medical Research)
Latest revision: ICMR-NIN, 2020
Previous revision: 2010
First recommendation: 1944 (revised in 1958, 1968, 1978, 2010, 2020)

🧮 HOW IS RDA CALCULATED? (The Formula)

RDA = Mean requirement + 2 × Standard Deviation (2SD)

i.e., RDA = Mean ± 2SD

Why +2SD? Because this covers 97.5% of the population (almost everyone!)

Simple trick to remember: "Mean plus two SD = covers almost ALL of the people"

👨 REFERENCE MAN & REFERENCE WOMAN (ICMR Definition)

These are the "standard" people that ICMR uses to calculate RDA.

Reference Man:

Feature	Value
Age	20–39 years
Weight	60 kg
Height	163 cm
Health	Healthy, well-nourished since childhood
Work	8 hours moderate activity (light industry/agriculture)
Sleep	8 hours in bed
Rest	8 hours sitting/light leisure

Reference Woman:

Feature	Value
Age	20–39 years
Weight	50 kg
Height	151 cm
Health	Healthy, well-nourished
Work	8 hours moderate activity (household/light industry)
Sleep	8 hours in bed

🧠 Memory trick: "Man = 60 kg, Woman = 50 kg — Man has 10 kg extra!"

⚡ ENERGY REQUIREMENTS BY ACTIVITY LEVEL

Activity Levels (3 types — remember "SeMoHe"):

Sedentary → Moderate → Heavy

Activity	Description
Sedentary	Office work, sitting most of the day, minimal physical effort
Moderate	Walking, light fieldwork, household work
Heavy	Farming, construction, mining, manual labor

Energy Requirements (Calories/kcal per day):

Group	Sedentary	Moderate	Heavy
Man	2110 kcal	2710 kcal	3470 kcal
Woman	1660 kcal	2130 kcal	2720 kcal
Pregnant woman	+350 kcal extra
Lactating (0–6m)	+600 kcal extra
Lactating (6–12m)	+520 kcal extra

🧠 Memory trick for Man's calories: "2110, 2710, 3470 — each goes up by about 600!" 🧠 Memory trick for Woman's calories: "1660, 2130, 2720 — starts lower than man!" 🧠 Pregnant = +350 ("3 months, 3 extra-50") 🧠 Lactating = +600 first 6 months ("6 months = 6 hundreds")

🥩 PROTEIN REQUIREMENTS

Group	RDA (per day)
Adult Man	60 g/day (1.0 g/kg body weight)
Adult Woman	55 g/day (1.0 g/kg body weight)
Pregnant woman	+15 g/day extra
Lactating (0–6m)	+25 g/day extra
Lactating (6–12m)	+18 g/day extra
Children 1–3 yr	12.5 g/day
Children 4–6 yr	16 g/day
Children 7–9 yr	23 g/day

Safe protein intake (ICMR 2020): 0.83 g/kg/day EAR (minimum): 0.66 g/kg/day

🧠 Easy trick: "Man = 60g protein (same as his weight in kg). Woman = 55g (same as her weight in kg). Protein = 1g per kg body weight!"

Cereal:Legume:Milk ratio for good protein quality:

3 : 1 : 2.5 (ICMR 2020) — previously was 11:1:3

🧈 FAT REQUIREMENTS (Visible fat per day)

Activity	Men	Women
Sedentary	25 g/day	20 g/day
Moderate	30 g/day	25 g/day
Heavy	40 g/day	30 g/day

🧠 Trick: "Men need 5g more fat than women at each activity level!" Fiber: 30 g per 2000 kcal (safe intake)

💊 KEY VITAMINS & MINERALS RDA (Adult Men/Women)

Nutrient	Men (RDA 2020)	Women (RDA 2020)	Why important
Vitamin A	1000 µg/day	840 µg/day	Vision, immunity
Vitamin D	600 IU/day	600 IU/day	Bone health, calcium absorption
Vitamin C	80 mg/day	65 mg/day	Immunity, antioxidant
Vitamin B12	2.2 µg/day	2.2 µg/day	Nerve function, RBC
Folate	300 µg/day	220 µg/day	Cell division
Folate (pregnancy)	—	+80 µg/day extra	Prevents neural tube defects
Thiamine (B1)	1.8 mg/day	1.7 mg/day	Energy metabolism
Riboflavin (B2)	2.5 mg/day	2.4 mg/day	Energy, growth
Niacin (B3) - sedentary	14 mg/day	11 mg/day	Energy metabolism
Iron	19 mg/day	29 mg/day	Hemoglobin formation
Calcium	1000 mg/day	1000 mg/day	Bones and teeth
Zinc	17 mg/day	13 mg/day	Immunity, growth
Iodine	150 µg/day	150 µg/day	Thyroid hormone
Magnesium	440 mg/day	370 mg/day	Muscle/nerve function

🧠 Iron memory trick: "Women need MORE iron than men (29 vs 19) because they lose blood every month (menstruation)!" 🧠 Calcium trick: "1000 mg = 1 gram of calcium daily — both men and women" 🧠 Vitamin D trick: "600 IU — same for everyone!"

🤰 SPECIAL REQUIREMENTS (Extra nutrients needed)

Condition	Extra Energy	Extra Protein	Extra Iron	Extra Folate
Pregnancy	+350 kcal	+15 g	+38 mg (total ~67 mg)	+80 µg
Lactation 0–6m	+600 kcal	+25 g	—	+50 µg
Lactation 6–12m	+520 kcal	+18 g	—	+40 µg

🧠 Pregnant woman trick: "3P" = +350 kcal, +Protein 15g, +Prevention of anaemia (iron)

🥗 BALANCED DIET

"A balanced diet provides all nutrients in the right amounts and proportions needed for health."

Balanced Diet Contains:

Macronutrients — Carbohydrates (55–60% of energy), Proteins (10–15%), Fats (20–30%)
Micronutrients — Vitamins and Minerals
Non-nutrient compounds — Dietary fiber, antioxidants (also important!)
Water

Macronutrient Energy Values (Must know!):

Nutrient	Energy per gram
Carbohydrate	4 kcal/g
Protein	4 kcal/g
Fat	9 kcal/g
Alcohol	7 kcal/g

🧠 "C and P = 4, F = 9, Alcohol = 7" (Fat has the most calories!)

📐 IMPORTANT CONCEPTS AROUND RDA

1. EAR (Estimated Average Requirement)

EAR = The amount of a nutrient that meets the needs of 50% of people

RDA is HIGHER than EAR

EAR for protein = 0.66 g/kg/day

RDA for protein = 0.83 g/kg/day

2. Upper Limit (UL)

The maximum safe amount — beyond this, toxicity can occur

3. The relationship:

EAR < RDA < UL
(50% covered) (97.5% covered) (don't go beyond this)

🧠 "EAR covers half, RDA covers almost all, UL is the danger zone"

🔄 FACTORS AFFECTING RDA

Remember: "AASP GLP"

Factor	How it affects RDA
Age	Children and elderly have different needs
Activity level	Heavy workers need more calories
Sex	Men and women have different needs
Physiological state	Pregnancy and lactation increase needs
Growth	Growing children need more protein, calcium
Loss/absorption	Iron absorption is low from plant foods → higher RDA
Pathological stress	Illness increases some requirements

🎯 DIFFERENCES TO REMEMBER (Exam Favorites!)

	RDA	EAR
Covers	97.5% of population	50% of population
Formula	Mean + 2SD	Mean only
Use	For population planning	For research/surveys
Protein (adult)	0.83 g/kg/day	0.66 g/kg/day

⚡ THE MOST IMPORTANT NUMBERS TO KNOW (Flash Cards!)

What	Number
Reference Man weight	60 kg
Reference Woman weight	50 kg
Reference Man/Woman age	20–39 years
Man protein	60 g/day
Woman protein	55 g/day
Sedentary man calories	2110 kcal
Sedentary woman calories	1660 kcal
Moderate man calories	2710 kcal
Heavy man calories	3470 kcal
Extra calories in pregnancy	+350 kcal
Extra calories in lactation	+600 kcal
Iron - woman	29 mg/day
Iron - man	19 mg/day
Calcium	1000 mg/day
Vitamin D	600 IU/day
Vitamin C (man)	80 mg/day
Iodine	150 µg/day
RDA covers	97.5% population
EAR covers	50% population
Fat = calories/gram	9 kcal/g
Carb = calories/gram	4 kcal/g
Protein = calories/gram	4 kcal/g

🧠 ALL MNEMONICS TOGETHER

RDA formula: "Mean Plus 2 SD = covers 97.5% — Almost ALL people"
Reference weights: "Man is 60, Woman is 50 — Man has 10 kg more"
Protein per kg: "1 gram per kg body weight — 60 kg man = 60g, 50 kg woman = 55g"
Calorie trick (Man): "2110 → 2710 → 3470 — goes up by 600 each level (Sedentary → Moderate → Heavy)"
Why women need MORE iron: "Women lose blood monthly — so they need 29mg vs men's 19mg"
Fat calories: "Fat is FAT = 9 kcal/g (the most!). C and P are only 4 kcal/g"
Energy for pregnancy: "+350 = 3 trimesters × ~100 kcal"
Lactation: "+600 first 6 months = 6 hundreds"
EAR vs RDA: "EAR = Exactly Average (50%), RDA = Reaches Almost all (97.5%)"
Activity levels: "SeMoHe" = Sedentary, Moderate, Heavy

💬 HOW TO OPEN YOUR ANSWER (Impress the examiner immediately!)

"RDA stands for Recommended Dietary Allowance. It is defined as the average daily dietary intake level that is sufficient to meet the nutritional requirements of nearly 97.5% of healthy individuals in a population. In India, it is set by the ICMR — most recently revised in 2020 by ICMR-NIN. It is calculated as the mean requirement plus twice the standard deviation (Mean + 2SD). ICMR has defined a reference Indian man of 60 kg and reference woman of 50 kg, both aged 20–39 years, to calculate these allowances."

This opening answer will 100% impress your examiner! 🎯

Good luck in your exam today! Remember — Mean + 2SD = 97.5% = RDA! 💪

RDA according to nepal

Searching the Web

RDA recommended dietary allowance Nepal DFTQC values table calories protein vitamins minerals 2024

Searching the Web

Nepal recommended dietary allowance reference man woman calorie protein fat requirements sedentary moderate heavy PSM KAHS

Reading Web Page

https://www.scribd.com/document/695437933/UNIT-2-Nutritional…

Searching the Web

Nepal dietary guidelines DFTQC NPC nutrition policy reference man 65kg woman 55kg calorie 2500 kcal Nepal specific RDA

Reading Web Page

https://www.fao.org/nutrition/education/food-dietary-guideli…

Searching the Web

"Nepal" "RDA" "2500" OR "2250" OR "reference man" "55 kg" OR "60 kg" calories protein "Nepal specific" nutrition requirements community medicine MBBS

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RDA according to nepal

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