Characteristics of commercial determinants of health research on corporate activities: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Business practices have influenced human health for centuries, yet an overarching concept to study these activities across nations, time periods, and industries (called 'the commercial determinants of health' (CDH)) has emerged only recently. The purpose of this review was to assess the descriptive characteristics of CDH research and to identify remaining research gaps. METHODS: We systematically searched four databases (Scopus, OVID Medline, Ovid Embase, and Ovid Global Health) on Sept 13, 2022 for literature using CDH terms that described corporate activities that have the potential to influence population health and/or health equity (n = 116). We evaluated the following characteristics of the literature: methods employed, industries studied, regions investigated, funders, reported conflicts of interest, and publication in open-access formats. RESULTS: The characteristics of the articles included that many were conceptual (50/116 articles; 43%) or used qualitative methods (37; 32%). Only eight articles (7%) used quantitative or mixed methods. The articles most often discussed corporate activities in relation to the food and beverage (51/116; 44%), tobacco (20; 17%), and alcohol industries (19; 16%), with limited research on activities occurring in other industries. Most articles (42/58 articles reporting a regional focus; 72%) focused on corporate activities occurring in high-income regions of the world. CONCLUSIONS: Our findings indicate that literature that has used CDH terms and described corporate practices that influence human health has primarily focused on three major industries in higher-income regions of the world. Qualitative methods were the most common empirical method for investigating these activities. CDH-focused investigations of corporate practices conducted by less-studied industries (e.g., social media) and in lower-income regions are recommended. Longitudinal quantitative studies assessing the associations between corporate practices and a range of health outcomes is also a necessary next step for this 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 | 0.072 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.069 | 0.077 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".