Bibliographic record
Abstract
OBJECTIVE: To explore past experiences and describe the expectations of members of the trans community regarding the delivery of primary care by their family physicians. DESIGN: Qualitative phenomenologic approach. SETTING: Kingston, Ont, which has a population of approximately 123 000. PARTICIPANTS: A convenience sample of 11 individuals older than 18 years of age who self-identified as trans was recruited through community agencies and family medicine clinics. METHODS: Semistructured interviews were recorded and transcribed verbatim, and thematic analysis of transcripts was carried out by 2 independent researchers using NVivo. MAIN FINDINGS: Eleven interviews took place between September and November 2016; 4 individuals identified as trans men, 6 as trans women, and 1 as gender nonconforming. Themes identified included perceived physician knowledge of trans identities, patient self-advocacy, discrimination, positive spaces, and expectations of ideal care. The expected role of the family physician for trans patients includes hormone assessment and prescription and referrals for gender-affirming surgeries. CONCLUSION: The trans community has several physical and mental health needs that are not being met by the current health care system. Family physicians need to be empowered to provide services such as hormone initiation and gender-affirming surgery referrals. Although other specialists might have a role for some patients, most trans people expect care to be delivered by family physicians whenever possible.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".