Falling Between the Cracks: Compound Identity Discrimination Within Primary Healthcare
Bibliographic record
Abstract
Canadian primary care physicians (PCPs) are ill-equipped to support transgender and/or gender non-conforming (TGNC) people and also racialized people, with the majority of these professionals reporting little or no education about the nuances of either TGNC or racialized peoples’ health. As a result, individuals who possess both of these marginalized identities often have their healthcare needs neglected. Multiple stakeholders have called for training programs to expand medical health curricula; however, input from racialized TGNC people is necessary for the enhancement of physician education. The current study explores racialized TGNC peoples’ experiences with their PCP using qualitative interviewing. A total of 10 participants (n = 10) were asked to discuss their positive and negative experiences. The emergent themes of this study are medical mistreatment acceptance, mirrored positive and negative experiences, attribution errors placed on PCPs, and critical engagement with identity from patients. From the emergent themes, I develop a theoretical framework for PCPs to recognize the diverse lived experiences and knowledges of patients by providing a novel approach to anti-oppressive health care.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".