The Commercial Determinants of Health and Evidence Synthesis (CODES): methodological guidance for systematic reviews and other evidence syntheses
Bibliographic record
Abstract
BACKGROUND: The field of the commercial determinants of health (CDOH) refers to the commercial products, pathways and practices that may affect health. The field is growing rapidly, as evidenced by the WHO programme on the economic and commercial determinants of health and a rise in researcher and funder interest. Systematic reviews (SRs) and evidence synthesis more generally will be crucial tools in the evolution of CDOH as a field. Such reviews can draw on existing methodological guidance, though there are areas where existing methods are likely to differ, and there is no overarching guidance on the conduct of CDOH-focussed systematic reviews, or guidance on the specific methodological and conceptual challenges. METHODS/RESULTS: CODES provides guidance on the conduct of systematic reviews focussed on CDOH, from shaping the review question with input from stakeholders, to disseminating the review. Existing guidance was used to identify key stages and to provide a structure for the guidance. The writing group included experience in systematic reviews and other forms of evidence synthesis, and in equity and CDOH research (both primary research and systematic reviews). CONCLUSIONS: This guidance highlights the special methodological and other considerations for CDOH reviews, including equity considerations, and pointers to areas for future methodological and guideline development. It should contribute to the reliability and utility of CDOH reviews and help stimulate the production of reviews in this growing field.
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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 | 0.447 | 0.778 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.055 | 0.045 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".