Family physician perceptions of working with LGBTQ patients: physician training needs
Bibliographic record
Abstract
BACKGROUND: Medical students and physicians report feeling under-prepared for working with patients who identify as lesbian, gay, bisexual, transgender or queer (LGBTQ). Understanding physician perceptions of this area of practice may aid in developing improved education. METHOD: In-depth interviews with 24 general practice physicians in Halifax and Vancouver, Canada, were used to explore whether, when and how the gender identity and sexual orientation of LGBTQ women were relevant to good care. Inductive thematic analysis was conducted using ATLAS.ti data analysis software. RESULTS: Three major themes emerged: 1) Some physicians perceived that sexual/gender identity makes little or no difference; treating every patient as an individual while avoiding labels optimises care for everyone. 2) Some physicians perceived sexual/gender identity matters primarily for the provision of holistic care, and in order to address the effects of discrimination. 3) Some physicians perceived that sexual/gender identity both matters and does not matter, as they strove to balance the implications of social group membership with recognition of individual differences. CONCLUSIONS: Physicians may be ignoring important aspects of social group memberships that affect health and health care. The authors hold that individual and socio-cultural differences are both important to the provision of quality health care. Distinct from stereotypes, generalisations about social group differences can provide valuable starting points, raising useful lines of inquiry. Emphasizing this distinction in medical education may help change physician approaches to the care of LGBTQ women.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".