Sexual Orientation Disclosure in Primary Care Settings by Gay, Bisexual, and Other Men Who Have Sex with Men in a Canadian City
Bibliographic record
Abstract
PURPOSE: Sexual orientation affects individuals' health histories and is fundamental to providers' understanding of patients as a whole. Gay, bisexual, and other men who have sex with men (GB-MSM) are vulnerable to certain health conditions, including HIV. The aim of this exploratory analysis was to examine factors associated with sexual orientation disclosure and communication with providers about GB-MSM health issues and to discuss implications. METHODS: We conducted a cross-sectional internet survey of GB-MSM (n = 202) in London-Middlesex, Ontario, Canada; analyses were limited to those with a regular primary care provider (n = 173). Blockwise regression models explored demographic, psychosocial, and healthcare-related factors associated with sexual orientation disclosure and physician-patient communication about GB-MSM-related health. RESULTS: Just over seventy-one percent (71.1%) of participants reported that their primary care provider (PCP) knew their sexual orientation, and 44.5% had talked to them about GB-MSM health. Overt negative comments or being refused care based on sexual orientation occurred infrequently, although 26.6% reported their provider had assumed they were heterosexual. Being married to or living common-law with another man, more frequent experiences of homosexual prejudice, and higher quality assessment of provider's communication skills were associated with the PCP knowing respondents' sexual orientation. Greater internalized homonegativity was associated with not talking to a PCP about GB-MSM-related health issues. More frequent experiences of homosexual prejudice, higher assessment of provider communication, and having prior negative experiences with a PCP were significantly associated with talking to a PCP about GB-MSM health. CONCLUSION: The majority of our sample disclosed their sexual orientation; however, not all patients voluntarily disclose. Medical training and education in Canada, where specific rights protections exist for sexual orientation minority populations, should emphasize awareness of essential patient health information. Training should include information about GB-MSM health and building a foundation on how to speak with GB-MSM patients nonjudgmentally.
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".