Discontinuation of Gender-Affirming Medical Treatments: Prevalence and Associated Features in a Nonprobabilistic Sample of Transgender and Gender-Diverse Adolescents and Young Adults in Canada and the United States
Bibliographic record
Abstract
PURPOSE: This study investigated the prevalence, correlates, and reasons for discontinuing gender-affirming medical treatment (GAMT) among transgender and gender-diverse adolescents and young adults living in Canada and the United States of America. METHODS: This exploratory study used data from an online survey of sexual and gender minority adolescents and young adults aged 15-29 years living in Canada or the United States of America (March-August 2022). The analytic sample was constituted by participants who responded to questions regarding starting and stopping GAMT, as well as reasons for stopping. Correlates of discontinuing GAMT were assessed using univariate logistic regression. RESULTS: The mean age of the analytic sample (N = 3,937) was 21.1 years. Participants were predominantly nonbinary (54.2%) and assigned female at birth (80.8%). 75.5% lived in Canada and 24.5% in the United States of America. Among those who had started GAMT, 121 of 720 (16.8%) reported having ever discontinued treatment. Forty five of 121 (37.2%) who ceased GAMT reported "Yes, but I wish I hadn't." The most frequently endorsed reasons for discontinuing GAMT were health reasons (37.3%), a change in gender identity (32.0%), and cost (16.0%). Greater age; nonbinary identity, 'other' gender identity; diagnosis of or self-identifying as living with schizophrenia; residing in the United States of America (relative to Canada); and endorsing a current Christian identity were associated with discontinuation. Ninety seven of 121 (80.2%) who discontinued GAMT reported a current transgender or gender-diverse identity. DISCUSSION: Given the dearth of information about the subpopulation who discontinue GAMT, this study advances candidate factors to inform future longitudinal research to better understand the multiple reasons and contexts for stopping GAMT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".