Race, gender, class, and sexual orientation: intersecting axes of inequality and self-rated health in Canada
Bibliographic record
Abstract
BACKGROUND: Intersectionality theory, a way of understanding social inequalities by race, gender, class, and sexuality that emphasizes their mutually constitutive natures, possesses potential to uncover and explicate previously unknown health inequalities. In this paper, the intersectionality principles of "directionality," "simultaneity," "multiplicativity," and "multiple jeopardy" are applied to inequalities in self-rated health by race, gender, class, and sexual orientation in a Canadian sample. METHODS: The Canadian Community Health Survey 2.1 (N = 90,310) provided nationally representative data that enabled binary logistic regression modeling on fair/poor self-rated health in two analytical stages. The additive stage involved regressing self-rated health on race, gender, class, and sexual orientation singly and then as a set. The intersectional stage involved consideration of two-way and three-way interaction terms between the inequality variables added to the full additive model created in the previous stage. RESULTS: From an additive perspective, poor self-rated health outcomes were reported by respondents claiming Aboriginal, Asian, or South Asian affiliations, lower class respondents, and bisexual respondents. However, each axis of inequality interacted significantly with at least one other: multiple jeopardy pertained to poor homosexuals and to South Asian women who were at unexpectedly high risks of fair/poor self-rated health and mitigating effects were experienced by poor women and by poor Asian Canadians who were less likely than expected to report fair/poor health. CONCLUSIONS: Although a variety of intersections between race, gender, class, and sexual orientation were associated with especially high risks of fair/poor self-rated health, they were not all consistent with the predictions of intersectionality theory. I conclude that an intersectionality theory well suited for explicating health inequalities in Canada should be capable of accommodating axis intersections of multiple kinds and qualities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".