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A

📜 WHO Prompt Pack (Healthcare / Research Literature Review)

Session 1: Data / Background (x, y)

Prompt:

You are a WHO research assistant. I want you to act as if I am conducting a literature review. 
Step 1: List the raw "scatter" of data sources (PubMed, WHO reports, national surveys, grey literature) related to [TOPIC]. 
Step 2: Clearly separate what is "in-scope" vs "out-of-scope" for my review. 
Output: a structured table with 2 columns: Data Source | Why Relevant / Not Relevant.

Session 2: Methods (y(x))

Prompt:

I want you to mirror my research question back to me, to check fidelity of encoding. 
Question: [INSERT QUESTION]. 

Task: Restate my question in your own words, highlighting:
1. Population
2. Exposure/Intervention
3. Comparator
4. Outcomes
5. Time / Setting

If the restatement is unclear, suggest 2–3 refinements. 

Session 3: Results (dy/dx)

Prompt:

Now I want to identify invariants and flows in the literature. 
Topic: [INSERT TOPIC].  

Task:
1. List consistent findings (e.g., relative risks, hazard ratios, effect sizes). 
2. Note any directional flows/trends over time. 
3. Highlight 2–3 "stable gradients" that repeat across multiple studies. 

Session 4: Limitations (d²y/dx²)

Prompt:

Play the role of a peer-reviewer. 
Topic: [INSERT TOPIC].  

Task:
1. Identify at least 5 methodological limitations in the available evidence. 
2. Differentiate which limitations are common across studies vs unique to specific ones. 
3. Suggest how future studies could resolve these. 

Format: Limitations | Common/Unique | Suggested Fix.

Session 5: Conclusions (∫y dx)

Prompt:

Summarize the cumulative ledger of evidence for [INSERT TOPIC]. 

Task:
1. Write a one-paragraph abstract (scientific style). 
2. Write a 3-bullet WHO policy brief (for government health officers). 
3. Write a 2-sentence community message (for general public). 

Each should be faithful to the evidence but scaled for the audience. 

🌍 WHO Context Add-Ons

Always cross-check with PubMed IDs or WHO report numbers (to reduce hallucination).
Use Okukona ×5 inside each session:
- Ask for scatter → mirror back → extract flows → stress-test → sum it up.
Databases to prioritize (5 tiers):
1. PubMed / Embase (nonparametric scatter)
2. Cochrane (semiparametric synthesis)
3. National registries (relative risk & HR)
4. WHO country reports (uncertainty / limits)
5. Policy briefs & SDG dashboards (ledger / inferences).

⚡This way the officers experience LLMs not as “mystical black boxes,” but as structured literature review co-pilots that respect the epistemic ladder they already know from epidemiology.

B

🎓 Facilitator’s Guide — Prompt Engineering for Healthcare Research

Audience: WHO country officers (India) Format: 5 sessions × 1–2h (can be adapted as half-day or full-day modules) Goal: Use prompt engineering as a disciplined way of conducting literature reviews, mapping directly to epidemiology’s five-part paper structure (Background → Methods → Results → Limitations → Conclusions).

Session 1: Background / Data (x,y)

Talking Points:
- Prompt engineering = imposing order on the chaotic scatter of LLM training text.
- Just like an epidemiologist defines inclusion/exclusion criteria for a dataset.
Activities:
- Icebreaker: Everyone writes a messy PubMed search on a chosen health topic.
- Then, prompt the LLM to structure the scatter into a table: in-scope vs out-of-scope.
Timing: 30m overview + 30m practice.

Session 2: Methods (y(x))

Talking Points:
- A prompt = model specification.
- Fidelity of encoding: does the LLM restate the research question accurately?
- Use PICO as a structure: Population, Intervention, Comparator, Outcomes.
Activities:
- Ask each officer to write a research question.
- Prompt the LLM: “Mirror my question back to me in PICO form.”
- Discuss where fidelity is high vs distorted.
Timing: 40m guided + 20m group feedback.

Session 3: Results (dy/dx)

Talking Points:
- Prompts can compress literature into invariants and flows.
- Think relative risk, hazard ratios, stable gradients over time.
Activities:
- Officers feed in their topic: “Extract consistent findings from 5+ sources.”
- LLM outputs flows: e.g. rising diabetes prevalence, consistent ORs.
- Discuss: which flows are robust vs hallucinated?
Timing: 30m demo + 30m practice.

Session 4: Limitations (d²y/dx²)

Talking Points:
- Every LLM output must be stress-tested.
- Prompt for indeterminacy: CI ranges, biases, missing data.
- Use the model as a peer-review partner.
Activities:
- Prompt: “List 5 limitations in this evidence base.”
- Compare with known methodological critiques (cross-sectional vs longitudinal, confounding, sample size).
- Small groups refine the “limitations prompt” for their own work.
Timing: 40m workshop + 20m plenary share-out.

Session 5: Conclusions (∫y dx)

Talking Points:
- Integration = survival / ledger.
- Prompts can produce audience-scaled summaries: scientific abstract, policy brief, community message.
- This is where negentropy is turned into usable inference.
Activities:
- Officers prompt: “Summarize 20 years of [TOPIC] research in 3 formats: abstract, policy brief, community message.”
- Compare tone/faithfulness across levels.
- Reflect: what survives as the true inference?
Timing: 30m practice + 30m final reflections.

Tips for Facilitators

Anchor every session in the epi-paper structure. Officers already know this rhythm: Background → Methods → Results → Limitations → Conclusions.
Use Okukona ×5 cycle in every prompt:
1. Scatter (x,y)
2. Mirror back (y(x))
3. Flows/invariants (dy/dx)
4. Stress-test/limits (d²y/dx²)
5. Integration/ledger (∫y dx)
Encourage critical distance: LLMs mirror biases in training data — ask officers to treat every output as a hypothesis, not a verdict.
Leverage group energy: Let small teams try different prompts and compare outputs; the variance itself is instructive.

Suggested Flow

Day 1: Sessions 1–2 (Background + Methods).
Day 2: Sessions 3–5 (Results + Limitations + Conclusions).

C

🎯 Level of Audience

These are WHO field/office staff, not professional statisticians or methodologists.
The course is framed as literature review + prompt discipline, not as a technical deep-dive into time-series analysis.
Too much detail on second-derivative sign shifts (concavity, inflection regimes, oscillation detection) risks intimidating rather than empowering.

🪵 What to Teach Instead

Stick to the 5-step ledger they already understand:

Data/Background (x,y) — gather scatter of evidence.
Methods (y(x)) — check if the question is faithfully encoded.
Results (dy/dx) — identify stable flows/invariants (e.g. HR ≈ 1.5 across studies).
Limitations (d²y/dx²) — acknowledge uncertainty, branching, instability.
Conclusions (∫y dx) — cumulative ledger → abstract, policy brief, community message.

At step 4, you can simply say:

“Here we watch for instability: are the gradients steady or do they flip around? If so, that’s where our confidence collapses.” That’s enough without doing formal regime shift detection.

🌍 Practical Analogy for WHO

dy/dx: “Smoking increases TB risk by ~2x.”
d²y/dx²: “But some studies show no effect in certain age groups — that’s instability, so we must qualify our inference.” No need to mathematically track sign flips. Just flag when results are inconsistent or contradictory.

✅ Bottom Line

Don’t teach regime-shift sign detection.
Do teach the intuition: stability vs instability.
Let them see that “limitations = where the slope is not consistent,” which is enough to ground critical appraisal.

D

What felt like an intimidating “stack-overflow” of concepts becomes a structured ladder that prunes variance into flow, rather than overwhelming.

What you just saw in practice is the meta-lesson of the whole course:

Scatter → Ledger. Even when ideas (or code, or health data, or Shakespeare scenes) overflow their container, the canonical 5-step (x,y → y(x) → dy/dx → d²y/dx² → ∫y dx) brings order.
Stability vs. instability. We don’t need regime-shift detection in an intro class — it’s enough to teach the intuition: “when the slope flips around, that’s uncertainty; when it’s stable, that’s an invariant.”
Compression discipline. Gertrude’s line “more matter, less art” is the Σ-audit embodied: prune flourish, keep signal.

🔑 Takeaway for your WHO curriculum: Every session should leave officers feeling like they can now prune overflow into a ledger. That’s the core confidence they need: that they can tame messy scatter (text, data, LLM output, or bureaucratic reports) into something that survives scrutiny and reseeds policy.