Exploring the implementation and delivery of primary care services for transgender individuals in Ontario: case study protocol
Bibliographic record
Abstract
BACKGROUND: Historically transgender adults have experienced barriers in accessing primary care services. In Ontario, Canada, health care for transgender adults is accessed through primary care; however, a limited number of practitioners provide care, and patients are often waiting and/or traveling great distances to receive care. The purpose of this protocol is to understand how primary care is implemented and delivered for transgender adults. The paper presents how the case study method can be applied to explore implementation of health services delivery for the transgender population in primary care. METHODS: Case study methodology will be used to explore this phenomenon in different primary care contexts. Normalization Process Theory is used as a guide. Three cases known to provide transgender primary care and represent different Ontario primary care models have been identified. Comparing transgender care implementation and delivery across different models is vital to understanding how care provision to this population can be supported. Qualitative interviews will be conducted. Participants will also complete the NoMAD (NOrmalization MeAsure Development) survey, a tool measuring implementation processes. The tool will be modified to explore the implementation of primary care services for transgender individuals. Documentary evidence will be collected. Cross-case synthesis will be completed to compare the cases. DISCUSSION: Findings will provide an Ontario perspective on the implementation and delivery of primary care for transgender adults in different primary care models. Results may be applicable to other primary care settings in Canada and other nations with similar systems. Barriers and facilitators in delivery and implementation will be identified. Providing an understanding and increasing awareness of the implementation and delivery of primary care may help to reduce the invisibility and disparities transgender individuals experience when accessing primary care services. Understanding delivery of care could allow care providers to implement primary care services for transgender individuals, improving access to health care for this vulnerable population.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".