Strengthening systematic reviews in public health: guidance in the <i>Cochrane Handbook for Systematic Reviews of Interventions</i>, 2nd edition
Bibliographic record
Abstract
AIMS: Decision makers in public health practice and policy rely on access to trustworthy, relevant, synthesized evidence. The second edition of the Cochrane Handbook for Systematic Reviews of Interventions ('the Handbook') reflects a major revision in guidance for authors of systematic reviews, incorporating a decade of methodological development and a number of significant changes to previous recommendations. This paper aims to highlight new guidance that addresses a number of key methodological challenges for authors of systematic reviews in public health. RESULTS: The revised Handbook includes guidance on framing public health research questions for synthesis, considering equity, intervention complexity, risk of bias assessment and synthesis methods other than meta-analysis. Reviews of public health interventions frequently encounter the types of methodological complexity addressed in this new guidance. CONCLUSION: We hope that readers will find that the Cochrane Handbook includes detailed and thoughtful guidance on both conceptualizing and executing systematic reviews relevant to public health questions. Considering the available methods guidance will, we hope, provide support for authors of public health reviews to tackle the challenges they encounter, strengthen their analysis and provide useful answers to the important questions asked by stakeholders and users of public health evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.263 | 0.544 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.028 | 0.035 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".