Measuring the Commercial Determinants of Health and Disease: A Proposed Framework
Bibliographic record
Abstract
The commercial determinants of health (CDoH) describe the adverse health effects associated with for-profit actors and their actions. Despite efforts to advance the definition, conceptualization, and empirical analyses of CDoH, the term's practical application to mitigate these effects requires the capacity to measure the influences of specific components of CDoH and the cumulative impacts of CDoH on the health and well-being of specific populations. Building on the Global Burden of Disease Study, we begin by conceptualizing CDoH as risk factor exposures that span agency and structural influences. We identify 6 components of these influences and propose an initial set of indicators and datasets to rank exposures as high, medium, or low. These are combined into a commercial determinants of health index (CDoHi) and illustrated by 3 countries. Although now a proof of concept, comparative analysis of CDoH exposures by population, over time and space, and their associated health outcomes will become possible with further development of indicators and datasets. Expansion of the CDoHi and application to varied populations groups will enable finer targeting of interventions to reduce health harms. The measurement of improvements to health and wellness from such interventions will, in turn, inform overall efforts to address the CDoH.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".