‘A void in our community’: exploring the complexities of delivering and implementing primary care services for transgender individuals in Northern Ontario
Bibliographic record
Abstract
AIM: To understand how the implementation of primary care services for transgender individuals is undertaken and delivered by practitioners in Northern Ontario. BACKGROUND: Northern Ontario, Canada, has a shortage of primary care health practitioners, and of these, there are a limited number providing transgender primary care. Transgender people in Northern Ontario must also negotiate a lack of allied and specialty services related to transgender health and travel over long distances to access those services that do exist. METHODS: A convergent mixed methods design was guided by normalization process theory (NPT) to explore transgender primary care delivery and implementation by nurses, nurse practitioners, physicians, social workers, and psychotherapists. A survey measuring implementation processes was elaborated through qualitative interviews with participants. Analysis of key themes emerging using the NPT framework informed understanding of primary care successes, barriers, and gaps in Northern Ontario. FINDINGS: Key themes included the need for more education on transgender primary care practice, increased need for training and awareness on transgender resources, identification of unique gaps and barriers to access in Northern Ontario transgender care, and the benefits of embedding and normalizing transgender care in clinical practice to practitioners and transgender patients. These findings are key to understanding and improving access and eliminating healthcare barriers for transgender people in Northern Ontario.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| 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".