Bibliographic record
Abstract
BACKGROUND: Gay and lesbian physicians in training face considerable challenges as they become professionalized. Qualitative research is necessary to understand the social and cultural factors that influence their medical training. In this study we explored the significance of gay or lesbian identity on the experiences of medical training using naturalistic methods of inquiry. METHODS: Semi-structured interviews, focus groups and an e-mail listserv were used to explore professional and personal issues of importance to 29 gay and lesbian medical students and residents in 4 Canadian cities. Data, time, method and investigator triangulation were used to identify and corroborate emerging themes. The domains explored included career choice, "coming out," becoming a doctor, the environment and career implications. RESULTS: Gay or lesbian medical students and residents experienced significant challenges. For all participants, sexual orientation had an effect on their decisions to enter and remain in medicine. Once in training, the safety of a variety of learning environments was of paramount importance, and it affected subsequent decisions about identity disclosure, residency and career path. Respondents' assessment of professional and personal risk was influenced by the presence of identifiable supports, curricula inclusive of gay and lesbian sexuality and health issues and effective policies censuring discrimination based on sexual orientation. The need for training programs to be proactive in acknowledging and supporting diversity was identified. INTERPRETATION: Considerable energy and emotion are spent by gay and lesbian medical students and residents navigating training programs, which may be, at best, indifferent and, at worst, hostile.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".