Exploring Mediators of Mental Health Service Use Among Transgender Individuals in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: To determine if and to what degree neighbourhood-level marginalization mediates mental health service use among transgender individuals. METHODS: This retrospective cohort study identified 2,085 transgender individuals through data obtained from 4 outpatient community and hospital clinics in 3 large cities in Ontario, which were linked with administrative health data between January 2015 and December 2019. An age-matched 1:5 comparison cohort was created from the general population of Ontario. Outcome measures were analysed from March 2020 to May 2022. The primary outcome was mental health service utilization, which included mental health-related visits to primary care providers, psychiatrists, mental health- and self-harm-related emergency department visits, and mental health hospitalizations. Mediation variables included ethnic concentration, residential instability, dependency, and material deprivation at the neighbourhood level and were derived from the Ontario Marginalization Index. RESULTS: = 10,425). Overall, neighbourhood-level marginalization did not clinically mediate mental health service use. However, transgender individuals were more likely to be exposed to all forms of neighbourhood-level marginalization, as well as having higher rates of health service use across all outcome measures. CONCLUSIONS: In this study, mental health service use among transgender individuals was not clinically mediated by marginalization at the neighbourhood level. This study highlights the need to explore marginalization and mental health service use at the individual level to better understand the mental health disparities experienced by transgender individuals and to ensure that health-care services are inclusive and affirming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".