“Primary care is primary care”: Use of Normalization Process Theory to explore the implementation of primary care services for transgender individuals in Ontario
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, healthcare for transgender individuals is accessed through primary care; however, there are a limited number of practitioners providing transgender care, and patients are often on waiting lists and/or traveling great distances to receive care. Understanding how primary care is implemented and delivered to transgender individuals is key to improving access and eliminating healthcare barriers. The purpose of this study is to understand how the implementation of primary care services for transgender individuals compares across various models of primary care delivery in Ontario. METHODS: A qualitative, exploratory, multiple-case study guided by Normalization Process Theory (NPT) was used to compare transgender care delivery and implementation across three primary care models. Three cases known to provide transgender primary care and represent different primary care models in Ontario, Canada (i.e., family health team, community health centre, fee-for service physician) were explored. The NoMAD survey, a tool to measure implementation processes, and qualitative interviews with primary care practitioners and allied healthcare staff were administered. RESULTS: Using the NPT framework to guide analysis, key themes emerged about successful implementation of primary care services for transgender individuals. These themes include creating a safe space for patients, identifying gaps in services, understanding practitioners' roles, and the need for more training and education in transgender care for practitioners. CONCLUSIONS: Primary care services for transgender individuals can and should be delivered in all models of primary care. Training and awareness for healthcare practitioners are needed to develop capacity in providing primary care to transgender individuals. A greater number of practitioners and organizations are needed to take on this work, embedding and normalizing transgender care into routine practice to address barriers to access and improve quality of care for transgender individuals.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".