Global priority setting for Cochrane systematic reviews of health promotion and public health research
Bibliographic record
Abstract
BACKGROUND: Systematic reviews of health promotion and public health interventions are increasingly being conducted to assist public policy decision making. Many intra-country initiatives have been established to conduct systematic reviews in their relevant public health areas. The Cochrane Collaboration, an international organisation established to conduct and publish systematic reviews of healthcare interventions, is committed to high quality reviews that are regularly updated, published electronically, and meeting the needs of the consumers. AIMS: To identify global priorities for Cochrane systematic reviews of public health topics. METHODS: Systematic reviews of public health interventions were identified and mapped against global health risks. Global health organisations were engaged and nominated policy-urgent titles, evidence based selection criteria were applied to set priorities. RESULTS: 26 priority systematic review titles were identified, addressing interventions such as community building activities, pre-natal and early infancy psychosocial outcomes, and improving the nutrition status of refugee and displaced populations. DISCUSSION: The 26 priority titles provide an opportunity for potential reviewers and indeed, the Cochrane Collaboration as a whole, to address the previously unmet needs of global health policy and research agencies.
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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 | 0.242 | 0.494 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.029 | 0.017 |
| Bibliometrics | 0.084 | 0.062 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.024 | 0.013 |
| Insufficient payload (model declined to judge) | 0.057 | 0.010 |
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".