Examining Mental Health Differences Between Transgender, Gender Nonconforming, and Cisgender Young People in British Columbia
Bibliographic record
Abstract
Foundry is an integrated service network delivering services to young people across British Columbia, Canada. To better understand the needs of transgender and gender nonconforming young people accessing Foundry—this study compares rates of mental health distress between transgender and gender nonconforming young people and cisgender young people accessing services and examines the extent to which race may have amplified the association between transgender and gender nonconforming identity and mental health distress. We analyzed the difference using a two-sample t -test. We used stratified simple linear regression to test the association of race with transgender and gender nonconforming identity and mental health distress. Participants were recruited from a network of community health centers in British Columbia, Canada. The quantitative sample ( n = 727) had a mean age of 21 years (SD = 2), 48% were non-white, 51% were white, and 77% were from Metro Vancouver. Compared to cisgender young people, transgender and gender nonconforming young people reported significantly higher levels of mental health distress. Transgender and gender nonconforming youth were more distressed than cisgender youth across both race strata but non-white transgender and gender nonconforming young people were not more distressed than white transgender and gender nonconforming young people. The findings from this study emphasize the need for increased education and understanding of transgender and gender nonconforming concepts and health concerns as well as on promoting intersectoral collaboration of social services organizations beyond simply health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".