Current gaps and ideological trends in physician understanding of LGBTQ+ healthcare and its delivery in London, Ontario
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Purpose Lesbian, gay, bisexual, transgender, and queer (LGBTQ+) communities have unique healthcare needs that often go unmet. LGBTQ+ patients report higher levels of satisfaction with and are more likely to seek care from providers who possess knowledge of and demonstrate comfort with LGBTQ+ healthcare issues. We sought to determine knowledge of, comfort with, and perceived barriers to providing equitable LGBTQ+ care amongst physicians in London, ON. Methods Anonymous online surveys were distributed to roughly 2400 full- and part-time physicians at the Schulich School of Medicine & Dentistry. Co-investigators independently coded forty-two surveys and conducted a theoretical thematic analysis. Results Physicians were categorized according to their beliefs about the unique health needs of LGBTQ+ populations and the degree to which they possessed corresponding knowledge. Seventeen physicians (42%) believed that LGBTQ+ populations have unique needs and possessed knowledge, sixteen (38%) believed that LGBTQ+ populations have unique needs but lacked knowledge, and nine (21%) denied the existence of unique needs. Across all respondents, competence was lacking in three domains: transgender healthcare, responding to LGBTQ+ identity disclosure, and knowledge of systemic inequities faced by LGBTQ+ communities. Conclusions These findings elucidate knowledge gaps amongst a representative sample of physicians and present opportunities for targeted educational intervention to improve LGBTQ+ 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".