Synthesize research findings with explicit limitations

작성자: AILesson9 분 소요테스트::ChatGPT검토일: 2026-08-28

빠른 답변

Combine findings into calibrated conclusions without erasing uncertainty, heterogeneity, or missing evidence. 제공할 내용: Structured findings and sources, Research and decision question, Synthesis standard. 예상 결과: A decision-oriented synthesis with confidence boundaries, exceptions, and next evidence needs.

맥락 추가

텍스트는 이 브라우저에 유지됩니다. AILesson Prompts는 이를 모델이나 서버로 보내지 않습니다.

Structured findings and sourcesResearch and decision questionSynthesis standard

프롬프트

채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다

Synthesize only the supplied findings for the stated decision.

Finding IDs, sources, methods, samples, estimates, uncertainty, and limitations:
[findings]

Population, issue/intervention, comparator, outcome, period, context, and decision:
[question]

Evidence hierarchy, materiality, acceptable inference, audience, format, and length:
[standard]

Check whether findings answer the same question before combining them. Preserve source IDs and distinguish direct evidence, indirect evidence, descriptive context, expert interpretation, and hypothesis. Do not average estimates unless measures, populations, comparisons, and methods justify it. Do not convert lack of statistical significance into no effect, association into causation, absence of evidence into evidence of absence, or repeated sources into independent confirmation.

Write: scoped answer; convergent findings; heterogeneity and exceptions; quality/transfer limits; evidence gaps; plausible alternative explanations; and calibrated decision implications. For every conclusion state confidence qualitatively with reasons, evidence that supports and weakens it, population/time boundary, and what new observation would change it. Separate “can decide now,” “can test now,” and “cannot conclude.” Include a source-to-conclusion ledger and a short plain-language version that retains the central limitation.

Playground에서 사용해 보기

기본적으로 비공개프롬프트 구성은 브라우저에서 로컬로 이루어집니다. 조직에서 허용하지 않는 한 기밀 정보를 AI 서비스에 입력하지 마세요.

입력에서 결과까지

적용 예시

구체적인 맥락이 이 레시피를 바로 사용할 수 있는 결과로 바꾸는 방법을 확인하세요

실제 입력

Structured findings and sources: F1/C randomized working paper: 1,204 adults at four urban libraries; reminders 63%, control 58%, difference 5 percentage points, 95% CI 1-9; excludes no-mobile patrons; peer review unknown. F2/A local non-randomized pilot: 214 opted-in adults, 61% versus 54% prior period; seasonal mix differs; 11 opt-outs, 3 late messages, ~25 staff-min/day. F3/E interviews: 12 local patrons selected from pilot volunteers; 8 said texts were useful, 4 wanted channel choice; recruitment favors engaged users. F4/D rural descriptive dashboard: 47% collection among all ages, no intervention. F5/C secondary outcome: no clear difference in late fees, estimate +0.2 dollars, 95% CI -0.4 to +0.8.
Research and decision question: Should the city library run a four-week randomized reminder test among consenting adults, measuring collection before expiry and operational guardrails? Decision is testing, not rollout. Context is this library in late 2026.
Synthesis standard: Direct randomized evidence preferred; local feasibility evidence relevant. A 3-point collection difference is operationally material for planning, but uncertainty must remain. Executive memo, plain language, ≤450 words. Qualitative confidence only.

예시 출력

Scoped answer: the supplied evidence supports running a bounded local randomized test among consenting adults; it does not support permanent rollout or a promised effect size. Confidence is moderate because one sizable randomized urban study estimates a positive collection effect, while local evidence establishes feasibility but not causation.

Convergence: F1/C estimates a 5-percentage-point difference, with interval 1-9. Its point estimate exceeds the 3-point planning threshold, but the interval includes smaller effects. F2/A has a compatible 7-point descriptive difference and supplies local operational signals. Do not average 5 and 7: designs and comparisons differ. F3/E suggests acceptability and channel choice matter, but the engaged volunteer sample cannot estimate prevalence. F4/D is context only, not effect evidence. F5/C does not show a clear late-fee effect; its interval includes modest harm and benefit, so “no effect” would be wrong.

Limits and alternatives: transfer from four urban libraries is uncertain; both intervention findings omit or underrepresent adults without mobile access; local period/season changes could explain F2; message timing or participant engagement could contribute. Peer-review status is unknown. The evidence says little about children, rural patrons, permanent behavior, or equitable channel access.

Can decide now: authorize design of a four-week test with consent, random allocation, pre-defined collection outcome, channel/accessibility review, opt-out/late-message/staff-time guardrails, and stop thresholds. Can test now: whether the local effect clears 3 points without unacceptable guardrail cost and whether channel choice is needed. Cannot conclude: reminders caused the local pilot difference, reduce late fees, work for patrons without mobile numbers, or merit rollout. Change condition: a corrected F1 null estimate, infeasible local operations, or unacceptable opt-out/access disparity would weaken the recommendation; a well-run local test clearing threshold with stable guardrails would strengthen it. Ledger: conclusion effect→F1/F2; feasibility→F2; acceptability→F2/F3; limits→F1-F5.

효과가 있는 이유

1
Question alignment prevents a synthesis from combining estimates that only look similar
2
Change conditions make confidence actionable by showing what evidence should alter the decision

결과 확인

Are conclusions scoped to matching populations, measures, periods, and methods
Does every conclusion include support, weakening evidence, limits, and a change condition
Are current decisions, testable hypotheses, and unsupported conclusions separated

이 방법의 근거

출처 및 인용문

이 레시피의 특정 설계 선택을 뒷받침하는 1차 출처입니다. 특정 AI 결과를 보장하지는 않습니다.

The prompt separates findings, certainty, applicability, limitations, and decision implications because Cochrane guidance requires conclusions to reflect uncertainty and avoid going beyond the available evidence.
Chapter 15: Interpreting results and drawing conclusions — Cochrane Handbook for Systematic Reviews of Interventions출처 검토일: 2026-08-31

안심하고 사용하세요

자주 묻는 질문

이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변

What should I prepare before using “Synthesize research findings with explicit limitations”?

For “Synthesize research findings with explicit limitations,” prepare Structured findings and sources, Research and decision question, and Synthesis standard. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Synthesize research findings with explicit limitations” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A decision-oriented synthesis with confidence boundaries, exceptions, and next evidence needs—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Synthesize research findings with explicit limitations”?

The published test record for “Synthesize research findings with explicit limitations” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

더 많은 탐색 방법