Standardizing Injustice in Transition-related Medicine?: An Institutional Ethnography of How Assessment Protocols Coordinate Inequitable Access to Hormones and Surgeries in Canada
Bibliographic record
Abstract
Transgender (trans) people experience barriers to transition medicine such as cross-sex hormones and sex reassignment surgeries. In response, standardized care protocols, such as the World Professional Association of Transgender Health standards of care (WPATH-SOC), are recommended to improve gaps in care. To better understand how standardized care protocols shape access to transition medicine in Canada, I draw on institutional ethnography as a critical research strategy. In this dissertation I thus explicate how the main protocols used to assess trans people’s psychosocial readiness for hormones or surgeries serve a powerful coordinating function. Although significant advocacy targets the Diagnostic and Statistical Manual for Mental Disorders for pathologizing trans identity through the gender dysphoria diagnosis, my research demonstrates that the WPATH-SOC standardizes an entire model of care based in pathologization. This research project uses data obtained through talking with twenty-two trans people, clinicians, clinician-educators, and hospital administrators, observing clinician-education workshops, and analyzing pertinent assessment protocols and healthcare policy texts. My dissertation makes three substantive contributions to the areas of public health, trans studies, and health professions education research. First, I show that standardized readiness assessments previously understood as innocuous instead contribute to inequities and further pathologize trans people. Second, I argue that in the absence of trans health in the formal health professions education curricula, standardized assessment protocols serve as a form of curriculum, coordinating how health professionals learn and teach this clinical sub-specialty. Third, the results of this research hold the potential to bolster trans health advocacy efforts by identifying how assessment protocols rule health professionals’ and trans people’s resistance practices. In the spirit of activist institutional ethnography this dissertation concludes with a discussion on recommended future advocacy and systems change in the areas of policy, education, and 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.023 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".