Understanding the role of social identity/position in access to primary care providers: An intersectionality approach using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
Bibliographic record
Abstract
Primary care (PC) in Canada is concerned with addressing Canadian health needs, especially the most vulnerable. The roles of social identities and positions in having a PC provider has been treated primarily as independent and additive, instead of interdependent and intersecting. A quantitative intersectionality approach using multilevel analysis of individual heterogeneity and discriminatory accuracy examined whether respondents to the Canadian Community Health Survey (2015-2019) had a PC provider based on membership in intersectional strata (constructed using gender, age, immigration status, race, and income). This study found that not all between-stratum variance in the outcome could be explained by additive effects of gender, age, immigration status, race, and income. For 40 intersectional strata, the predicted probability obtained through intersectional methods differed from that obtained through additive methods. There is a need to adopt an intersectional lens to develop research tools, conduct quantitative research, and create targeted interventions to improve PC access.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".