Does unionization and working under collective agreements promote health?
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".