Physician Follow-up Among Transgender and Gender Diverse Individuals after Psychiatric Emergency Department Visits and Hospitalizations: A Retrospective Population-Based Cohort Study
Bibliographic record
Abstract
Purpose: The study compared physician-based outpatient mental health care follow-up between transgender and gender diverse (TGD) individuals and the general population who had a mental health-related emergency department (ED) visit or hospitalization. Methods: This retrospective population-based cohort study used linked health administrative data to examine all adult psychiatric patients discharged from an ED visit (ED cohort) or hospitalization (hospitalization cohort) between 2012 and 2018 in Ontario, Canada. TGD individuals were identified and their data linked with health administrative data. The primary outcome was the proportion of individuals with any outpatient physician visit for mental health reasons within 30 days of the index discharge. Multivariable logistic regression analyses examined follow-up outcomes, including by diagnostic category, with adjustment for sociodemographic and clinical factors. Results: There were 728 TGD and 581,708 general population individuals with a mental health-related ED visit, while 454 TGD and 217,507 general population individuals had a psychiatric hospitalization. TGD individuals had higher 30-day post-ED follow-up (adjusted risk ratio [aRR] =1.21; 95% confidence interval [CI]: 1.02-1.42), particularly with a psychiatrist (aRR = 1.27; 95% CI: 1.07-1.52) and for those with an anxiety and related disorder diagnosis (aRR = 1.63; 95% CI: 1.23-2.15). Meanwhile, TGD individuals had lower 30-day follow-up posthospitalization (aRR = 0.80; 95% CI: 0.66-0.98) after adjusting for covariates. Conclusions: TGD individuals had greater rates of post-ED mental health follow-up, particularly with a psychiatrist, but lower rates of follow-up after a hospitalization. The higher follow-up after ED visits likely reflects greater clinical need. Lower follow-up after hospitalization is concerning given the risk of adverse outcomes and requires further research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".