Improving the reporting of public health intervention research: advancing TREND and CONSORT
Bibliographic record
Abstract
BACKGROUND: Evidence-based public health decision-making depends on high quality and transparent accounts of what interventions are effective, for whom, how and at what cost. Improving the quality of reporting of randomized and non-randomized study designs through the CONSORT and TREND statements has had a marked impact on the quality of study designs. However, public health users of systematic reviews have been concerned with the paucity of synthesized information on context, development and rationale, implementation processes and sustainability factors. METHODS: This paper examines the existing reporting frameworks for research against information sought by users of systematic reviews of public health interventions and suggests additional items that should be considered in future recommendations on the reporting of public health interventions. RESULTS: Intervention model, theoretical and ethical considerations, study design choice, integrity of intervention/process evaluation, context, differential effects and inequalities and sustainability are often overlooked in reports of public health interventions. CONCLUSION: Population health policy makers need synthesized, detailed and high quality a priori accounts of effective interventions in order to make better progress in tackling population morbidities and inequalities. Adding simple criteria to reporting standards will significantly improve the quality and usefulness of published evidence and increase its impact on public health program planning.
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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 | 0.846 | 0.927 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.020 | 0.035 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".