Models of Care and Team Activities in the Delivery of Transgender Primary Care: An Ontario Case Study
Bibliographic record
Abstract
Purpose: Transgender individuals experience barriers accessing primary care. In Ontario, primary care is delivered through a variety of delivery models. Literature supports team delivery of primary care for transgender individuals, yet little is known about care delivery in Ontario and the role of primary care teams. We intend to explore how primary care for transgender individuals is delivered within the different primary care models in Ontario and the roles primary care team members enact in care delivery, barriers, enablers, and clinical competence of practitioners in delivering transgender care. Methods: Case study methodology was used to compare transgender care across three Ontario primary care models. Key informants identified cases known to provide transgender care for case selection. Qualitative interviews were conducted. Documentary evidence and field notes were collected. Results: Practitioners clearly articulated their role and activities they were responsible for in providing care, however, they tended to work independently. In cases with an interdisciplinary team there was limited collaboration. Nurse practitioners, physicians, and counselors contributed most to the delivery of care. Key challenges included lack of service coordination within organizations, and the need for practitioner education. Continuing educational sessions, guidelines, and mentorship aided capacity building. Conclusions: Providing primary care to transgender individuals is within the scope of practice for primary care practitioners and can be part of routine care delivered in different models of care. Primary care team collaboration can be strengthened by regular team meetings. Professional training needs to include transgender education and continuing education opportunities need development.
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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 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".