Disclosure, comfort, and negative experiences with HIV care providers among transgender women living with HIV in Canada: Findings from a prospective cohort study of women living with HIV in Canada
Bibliographic record
Abstract
BACKGROUND: Transgender (trans) women living with HIV often face barriers to accessing inclusive and comprehensive primary and HIV care. OBJECTIVES: We aimed to (1) describe differences in trans women's disclosure of their trans identity and comfort discussing trans identity and trans-specific healthcare needs, based on physician type, and (2) report on the prevalence of negative trans-specific experiences with HIV physicians and any provider-related factors associated with comfort. DESIGN: Data of trans participants in the Canadian HIV Women's Sexual and Reproductive Health Cohort Study (CHIWOS) were analyzed across three waves (2013-2018). METHODS: Descriptive statistics explored disclosure of trans identity and comfort discussing trans-specific healthcare needs with family physicians versus HIV physicians. Fisher's exact tests of association explored the relationship between provider-related factors and self-reported comfort. Prevalence of negative trans-specific experiences with HIV physicians was reported. RESULTS: At baseline, of 54 trans women, 39 (72.2%) had a physician who supervised their HIV care in the year preceding (HIV physician). Among this subset, 94.9% had disclosed their trans identity and 82.1% reported feeling comfortable discussing trans-specific healthcare needs with this physician. Of the 27 (50.0%) who reported having a regular family physician other than their HIV care provider, 92.6% had disclosed their trans identity and 88.9% were comfortable discussing trans-specific healthcare needs. The most prevalent negative trans-specific experience with HIV physicians reported cumulatively was being told by the physician that they did not know enough about trans-related care to provide care (16.7%). CONCLUSION: Our findings demonstrate a high prevalence of disclosure of trans identity to physicians among our trans participants. Our findings suggest trans women have similar comfort discussing trans-specific healthcare needs with their family and HIV physicians; however, negative experiences with HIV providers indicate the need for gender-affirming, trans-specific provider training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".