Mechanisms for addressing and managing the influence of corporations on public health policy, research and practice: a scoping review
Bibliographic record
Abstract
OBJECTIVE: We identified mechanisms for addressing and/or managing the influence of corporations on public health policy, research and practice, as well as examples of where these mechanisms have been adopted from across the globe. DESIGN: We conducted a scoping review. We conducted searches in five databases on 4 June 2019. Twenty-eight relevant institutions and networks were contacted to identify additional mechanisms and examples. In addition, we identified mechanisms and examples from our collective experience working on the influence of corporations on public health policy, research and practice. SETTING: We identified mechanisms at the national, regional and global levels. RESULTS: Thirty-one documents were included in our review. Eight were peer-reviewed scientific articles. Nine discussed mechanisms to address and/or manage the influence of different types of industries; while other documents targeted specific industries. In total, we identified 49 mechanisms for addressing and/or managing the influence of corporations on public health policy, research and practice, and 43 of these were adopted at the national, regional or global level. We identified four main types of mechanisms: transparency; management of interactions with industry and of conflicts of interest; identification, monitoring and education about the practices of corporations and associated risks to public health; prohibition of interactions with industry. Mechanisms for governments (n=17) and academia (n=13) were most frequently identified, with fewer for the media and civil society. CONCLUSIONS: We identified several mechanisms that could help address and/or manage the negative influence of corporations on public health policy, research and practice. If adopted and evaluated more widely, many of the mechanisms described in this manuscript could contribute to efforts to prevent and control non-communicable diseases. TRIAL REGISTRATION DETAILS: The protocol was registered with the Open Science Framework on 27 May 2019 (https://osf.io/xc2vp).
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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 | 0.211 | 0.491 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.056 | 0.042 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".