‘Sometimes white doctors are not very friendly or inclusive’: a Critical Race Theory analysis of racism within and beyond sexual health settings
Bibliographic record
Abstract
Many Two-Spirit, gay, bisexual, transgender, and other queer Black, Indigenous, people of colour in Canada encounter racism when testing for sexually transmitted and blood-borne infections. Our objective in this study was to understand how racism shapes testing experiences for these communities in Ontario, Canada. Four peer researchers conducted recruitment and data collection in consultation with a community advisory board. Focus groups and interviews took place with 21 participants and their narrative accounts were analysed using reflexive thematic analysis. Participants identified three interrelated issues when testing: (1) experiencing judgement and discomfort due to racism; (2) lack of community and cultural indicators in testing spaces; and (3) barriers to accessing testing centres and services. Systemic racism was linked to each of these barriers, including increased distance to testing centres due to racial segregation. Participant accounts signal the need for antiracist testing spaces and practices. Key implications include the need for antiracism training for health service providers and others working with Two-Spirit, gay, bisexual, transgender, and other queer Black, Indigenous, people of colour, and the organisations that serve them, in order to make testing spaces safer. Dismantling systemic racism is imperative to achieve health equity for members of these communities.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.033 | 0.077 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.008 |
| 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".