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Record W4200259882 · doi:10.2105/ajph.2021.306491

Defining Priorities for Action and Research on the Commercial Determinants of Health: A Conceptual Review

2021· article· en· W4200259882 on OpenAlexfundno aff

Bibliographic record

VenueAmerican Journal of Public Health · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchWorld Health Organization
KeywordsConceptualizationPublic healthAction (physics)Conceptual frameworkYield (engineering)Mental healthHealth equity

Abstract

fetched live from OpenAlex

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 )

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.381
GPT teacher head0.493
Teacher spread0.111 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations77
Published2021
Admission routes1
Has abstractyes

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