“Completely out-at-sea” with “two-gender medicine”: A qualitative analysis of physician-side barriers to providing healthcare for transgender patients
Bibliographic record
Abstract
BACKGROUND: Members of the transgender community have identified healthcare access barriers, yet a corresponding inquiry into healthcare provider perspectives has lagged. Our aim was to examine physician perceptions of barriers to healthcare provision for transgender patients. METHODS: This was a qualitative study with physician participants from Ontario, Canada. Semi-structured interviews were used to capture a progression of ideas related to barriers faced by physicians when caring for trans patients. Qualitative data were then transcribed verbatim and analysed with an emergent grounded theory approach. RESULTS: A total of thirteen (13) physician participants were interviewed. Analysis revealed healthcare barriers that grouped into five themes: Accessing resources, medical knowledge deficits, ethics of transition-related medical care, diagnosing vs. pathologising trans patients, and health system determinants. A centralising theme of "not knowing where to go or who to talk to" was also identified. CONCLUSIONS: The findings of this study show that physicians perceive barriers to the care of trans patients, and that these barriers are multifactorial. Access barriers impede physicians when referring patients to specialists or searching for reliable treatment information. Clinical management of trans patients is complicated by a lack of knowledge, and by ethical considerations regarding treatments--which can be unfamiliar or challenging to physicians. The disciplinary division of responsibilities within medicine further complicates care; few practitioners identify trans healthcare as an interest area, and there is a tendency to overemphasise trans status in mental health evaluations. Failure to recognise and accommodate trans patients within sex-segregated healthcare systems leads to deficient health policy. The findings of this study suggest potential solutions to trans healthcare barriers at the informational level--with increased awareness of clinical guidelines and by including trans health issues in medical education-and at the institutional level, with support for both trans-focused and trans-friendly primary care models.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".