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Record W4200092698 · doi:10.1093/heapro/daab181

Does unionization and working under collective agreements promote health?

2021· article· en· W4200092698 on OpenAlexaffabout
Jessica Muller, Dennis Raphael

Bibliographic record

VenueHealth Promotion International · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork University
Fundersnot available
KeywordsSocial determinants of healthPolitical sciencePoliticsHealth promotionEconomic inequalityInequalityEconomic growthDemographic economicsEconomicsHealth careLaw

Abstract

fetched live from OpenAlex

Health promoters recognize the social determinants of health (SDOH) shape health outcomes yet generally neglect how unionization and collective agreements (CAs) shape these SDOH. This is surprising since extensive evidence indicates unions and CAs influence wages and benefits, job security, working conditions and income inequality, which go on to affect additional SDOH of food and housing security, child development and social exclusion. We argue unions and CAs should be a health promotion focus by examining how they influence the SDOH and health outcomes in wealthy developed nations in four ways. First, we consider how union density (UD) and CA coverage (CAC) are associated with differences between wealthy western nations in percentage of low-waged workers, extent of income inequality, and low birthweight and infant mortality rates. Second, we bring together literature that shows greater UD and CAC within national sub-jurisdictions are associated over time with more equitable distribution of the SDOH and better health outcomes. Third, we document-also using available literature-how within nations, union membership and working under a CA shape the SDOH one experiences. Fourth, we carry out a Canadian case study-applying a political economy lens-to examine how power relations, working through economic and political systems, determine extent of unionization and CAC and the inclination of health promoters to consider these issues. Implications for health promoters are considered.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.445
Teacher spread0.344 · 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.

Study designObservational
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

Citations15
Published2021
Admission routes2
Has abstractyes

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