Exploring the implementation and delivery of primary care services for transgender individuals: An Ontario case study
Bibliographic record
Abstract
Transgender individuals represent one of the most marginalized and underserved populations in healthcare. Issues such as discrimination, lack of practitioner experience and knowledge, and a deficiency of services have contributed to the healthcare barriers experienced by transgender individuals. There is a lack of literature demonstrating how primary care services are delivered to transgender individuals and a need for research that helps advance our knowledge about the delivery and implementation of primary care services for this population. This thesis explores how primary care services are delivered and implemented from a perspective that acknowledges the complexities of the healthcare needs of the transgender population. A multiple-case study design was used to explore the implementation and delivery of primary care for transgender individuals in Ontario within different delivery models of primary care and through diverse roles of primary care team members. Normalization Process Theory, an implementation theory and conceptual framework, was used in this study to understand and explain the dynamic processes that occur during implementation of interventions in healthcare, and guide data collection and analysis. Three cases representing different models of primary care delivery in Ontario were identified, all of which provide primary care services to transgender individuals. These models included a solo physician Fee-For-Service practice, a Family Health Team and a Community Health Centre. The multiple sources of data collection strategies used were interviews, a survey, documental evidence, and field notes. Participants included multiple members of the primary healthcare team, such as practitioners, clinical support staff and executive directors. This study helps to advance our knowledge of the delivery and implementation of primary care services for transgender individuals. Implications for nursing practice, including highlighting the need for curricula changes and understanding the nursing role, are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".