“I don’t think they thought I was ready”: How pre-transition assessments create care inequities for trans people with complex mental health in Canada
Bibliographic record
Abstract
Transgender (trans) people experience high rates of mental health issues including depression and suicidality. Improving access to transition-related medicine such as hormones and surgeries is suggested as an important mechanism to address these mental health issues. Yet clinicians experience challenges assessing and referring trans people for transition-related medicine. Standardized assessment protocols have been therefore recommended to optimize care. Although standardized protocols are purported to expand access to hormones and surgeries for trans people, it is unclear whether these tools achieve this goal. We therefore conducted an institutional ethnography to explicate how standardized readiness assessments coordinate access to hormones and surgeries in Canada. We analyzed key texts, talked with trans people, clinicians, clinician-educators, and administrators (total n = 22), and observed clinician-education workshops. In the context of determining transition readiness, standardized protocols direct clinicians to explore alternative diagnoses and assess the degree to which any complex mental health condition is “managed” prior to initiating hormones or surgeries. In response, we found that trans patients downplay or withhold mental health concerns from clinicians, or otherwise do additional work (e.g., take up unwanted psychiatric interventions) to convince providers they are “mentally ready” to transition. This phenomenon is paradoxical in that transition-related medicine is recommended to mitigate trans people’s psychosocial distress, but when patients reveal symptoms of distress they encounter significant barriers to treatment. We conclude that the logic underpinning pre-transition “mental readiness” assessments discredits the claim that standardized protocols optimize access to hormones and surgeries.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| 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".