Improving the reporting of public health intervention research: advancing TREND and CONSORT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.211 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it