Explaining the Health Gap Experienced by Girls and Women in Canada: A Social Determinants of Health Perspective
Bibliographic record
Abstract
In the last few decades there has been a resurgence of interest in the social causes of health inequities among and between individuals and populations. This ‘social determinants’ perspective focuses on the myriad demographic and societal factors that shape health and well-being. Heeding calls for the mainstreaming of two very specific health determinants - sex and gender - we incorporate both into our analysis of the health gap experienced by girls and women in Canada. However, we take an intersectional approach in that we argue that a comprehensive picture of health inequities must, in addition to considering sex and gender, include a context sensitive analysis of all the major dimensions of social stratification. In the case of the current worldwide economic downturn, and the uniquely diverse Canadian population spread over a vast territory, this means thinking carefully about how socioeconomic status, race, ethnicity, immigrant status, employment status and geography uniquely shape the health of all Canadians, but especially girls and women. We argue that while a social determinants of health perspective is important in its own right, it needs to be understood against the backdrop of broader structural processes that shape Canadian health policy and practice. By doing so we can observe how the social safety net of all Canadians has been eroding, especially for those occupying vulnerable social locations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".