The impact of inequality on health in Canada: a multi-dimensional framework
Bibliographic record
Abstract
This paper argues that a truly critical understanding of health inequality requires an analytical approach that acknowledges the interactional and interdependent relationships between structural, institutional and everyday inequalities, and how theseinequalities are informed by the intersecting relationships between race, culture, gender, citizenship status, socio-economic status and other social factors to determine health outcomes, health access and quality of care. The influences of macro-structural forces and micro-situational events on health outcomes are issues that have largely been analysed independently in the literature. This paper presents a Canadian perspective on these issues. It argues for an analysis that characterises inequality in its circuitous, contextual, multi-layered and multi-dimensional forms by articulating health outcomes for racialised and other marginalised groups as the product of the convergence between the macro-structural forces ofdiscrimination that often occur within societal institutions and structures, and the micro-situational discriminatory events that occur between individuals in everyday life. Finally, the paper suggests that reducing and eliminating poor health outcomes for racialised groups requires inter-professional partnerships between physicians, psychiatrists and other mental health professionals, nurses, social workers, community and settlement workers and other professionals, whichwould enable professionals in diverse fields to share different skills at various levels to help clients at different points in their lives within diverse clinical and non-clinical settings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".