Experiences of Transgender Participants in Emergency Departments: Findings from the OutLook Study
Bibliographic record
Abstract
Purpose: Even in cases of medical emergency, mistreatment and negative experiences in life or in medical settings can deter trans patients from seeking necessary care. The purpose of this study was to identify factors associated with trans persons' emergency department (ED) avoidance in the mixed urban-rural Region of Waterloo, Ontario, Canada. Methods: The OutLook Study was a community-based partnership that created an online, cross-sectional questionnaire for lesbian, gay, bisexual, transgender, and other sexual and gender minority community members. Participants in this analysis were 16 years of age or older, lived, worked, or attended school in Waterloo Region, and identified as trans ( n =112). Binary logistic regression was used to test associations between sociodemographic, resilience, and risk variables, and ED avoidance. Sociodemographic variables statistically significant at p <0.05 at the bivariate level were included as controls to explore different combinations of resilience and risk factor in multivariable models. Results: Participants reporting complete or partially complete medical transitions were more likely to report ED avoidance, compared to those who had not initiated medical transition. Elevated transphobia was associated with greater likelihood of avoidance. However, increasing levels of social support decreased the likelihood of avoidance. In multivariable models, social support, support from a special person, and transphobia were always significant, regardless of controlled variables. Conclusion: Transphobia—enacted in the contexts of everyday life and health care—can deter patients from seeking care. Patient-centered care requires careful attention to trans identity and health needs, especially in emergency settings. In the absence of structural changes, providers can take steps to mitigate the erasure and discrimination trans patients experience and anticipate when accessing EDs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".