Corporate activities that influence population health: a scoping review and qualitative synthesis to develop the HEALTH-CORP typology
Bibliographic record
Abstract
INTRODUCTION: The concept of the commercial determinants of health (CDH) is used to study the actions of commercial entities and the political and economic systems, structures, and norms that enable these actions and ultimately influence population health and health inequity. The aim of this study was to develop a typology that describes the diverse set of activities through which commercial entities influence population health and health equity across industries. METHODS: We conducted a scoping review to identify articles using CDH terms (n = 116) published prior to September 13, 2022 that discuss corporate activities that can influence population health and health equity across 16 industries. We used the qualitative constant comparative method to inductively code descriptions and examples of corporate activities within these articles, arrange the activities into descriptive domains, and generate an overarching typology. RESULTS: The resulting Corporate Influences on Population Health (HEALTH-CORP) typology identifies 70 corporate activities that can influence health across industries, which are categorized into seven domains of corporate influence (i.e., political practices, preference and perception shaping practices, corporate social responsibility practices, economic practices, products & services, employment practices, and environmental practices). We present a model that situates these domains based on their proximity to health outcomes and identify five population groups (i.e., consumers, workers, disadvantaged groups, vulnerable groups, and local communities) to consider when evaluating corporate health impacts. DISCUSSION: The HEALTH-CORP typology facilitates an understanding of the diverse set of corporate activities that can influence population health and the population groups affected by these activities. We discuss how the HEALTH-CORP model and typology could be used to support the work of policy makers and civil society actors, as well as provide the conceptual infrastructure for future surveillance efforts to monitor corporate practices that affect health across industries. Finally, we discuss two gaps in the CDH literature that we identified based on our findings: the lack of research on environmental and employment practices and a dearth of scholarship dedicated to investigating corporate practices in low- and middle-income countries. We propose potential avenues to address these gaps (e.g., aligning CDH monitoring with other occupational health monitoring initiatives).
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".