Defining Priorities for Action and Research on the Commercial Determinants of Health: A Conceptual Review
Bibliographic record
Abstract
In recent years, the concept of commercial determinants of health (CDoH) has attracted scholarly, public policy, and activist interest. To date, however, this new attention has failed to yield a clear and consistent definition, well-defined metrics for quantifying its impact, or coherent directions for research and intervention. By tracing the origins of this concept over 2 centuries of interactions between market forces and public health action and research, we propose an expanded framework and definition of CDoH. This conceptualization enables public health professionals and researchers to more fully realize the potential of the CDoH concept to yield insights that can be used to improve global and national health and reduce the stark health inequities within and between nations. It also widens the utility of CDoH from its main current use to study noncommunicable diseases to other health conditions such as infectious diseases, mental health conditions, injuries, and exposure to environmental threats. We suggest specific actions that public health professionals can take to transform the burgeoning interest in CDoH into meaningful improvements in health. (Am J Public Health. 2021;111(12):2202–2211. https://doi.org/10.2105/AJPH.2021.306491 )
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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 | 0.052 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| 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".