Physician perspectives on gender-affirming care for trans youth
Bibliographic record
Abstract
Background: Many trans people who seek medical transition, particularly youth, struggle to access care in a timely manner. Frequently, specialized clinics tend serve the majority of the trans population and the capacity of these clinics ends up being overburdened by demand. Yet, the provision of gender-affirming medical care is not beyond the skillset of general practitioners and if more took up prescribing it, access could be improved. Objectives: This study sought to understand physician perspectives on gender-affirming care for trans youth in the hopes that insights into how to encourage more practitioners to take up providing care could be gleaned. The study had two central research questions. 1) What are Canadian physicians’ perceptions, education, and knowledge base surrounding gender-affirming care for youth under the age of 18? 2)How does this differ between knowledgeable physicians who have experience with gender-affirming care and inexperienced physicians who do not? Methodology: An anti-oppressive queer theoretical lens was applied to this study. Individual, semi-structured, in-depth, interviews were conducted with 13 physicians who had a range of experiences providing care for trans patients. The interviews were transcribed and coded for similar themes which constructed the results. Results: Participants discussed a wide range of topics related to gender-affirming care for trans patients, both specific to youth and more general to trans healthcare broadly. Many participants argued, some with direct firsthand experience, that gender-affirming care does not need to be specialized care and can be prescribed, easily, by primary care providers. Many physicians receive little, or no education related to gender-affirming care in medical school. Despite this lack of education several participants became knowledgeable and willing to provide care to trans patients. Knowledgeable physicians frequently shared similar motivations, and accessed similar resources, in order to become willing and able to provide care. Discussion: This study proposes recommendations for improving access to gender-affirming care for trans youth, and trans patients broadly. Improving and implementing education regarding gender-affirming care in medical schools and expanding resources for currently practicing physicians who take on trans patients. As well as framing gender-affirming care to exist within primary care practice.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".