Enacted Stigma, Mental Health, and Protective Factors Among Transgender Youth in Canada
Bibliographic record
Abstract
Purpose: We aimed to assess the Minority Stress Model which proposes that the stress of experiencing stigma leads to adverse mental health outcomes, but social supports (e.g., school and family connectedness) will reduce this negative effect. Methods: We measured stigma-related experiences, social supports, and mental health (self-injury, suicide, depression, and anxiety) among a sample of 923 Canadian transgender 14- to 25-year-old adolescents and young adults using a bilingual online survey. Logistic regression models were conducted to analyze the relationship between these risk and protective factors and dichotomous mental health outcomes among two separate age groups, 14- to 18-year-old and 19- to 25-year-old participants. Results: Experiences of discrimination, harassment, and violence (enacted stigma) were positively related to mental health problems and social support was negatively associated with mental health problems in all models among both age groups. Among 14–18 year olds, we examined school connectedness, family connectedness, and perception of friends caring separately, and family connectedness was always the strongest protective predictor in multivariate models. In all the mental health outcomes we examined, transgender youth reporting low levels of enacted stigma experiences and high levels of protective factors tended to report favorable mental health outcomes. Conversely, the majority of participants reporting high levels of enacted stigma and low levels of protective factors reported adverse mental health outcomes. Conclusion: While these findings are limited by nonprobability sampling procedures and potential additional unmeasured risk and protective factors, the results provide positive evidence for the Minority Stress Model in this population and affirm the need for policies and programs to support schools and families to support transgender youth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 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) | 0.001 | 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".