Prevalence of and Risk and Protective Factors for Depression in Female-to-Male Transgender Ontarians: Trans PULSE Project
Bibliographic record
Abstract
Although depression is understudied in transgender and transsexual communities, high prevalences have been reported. This paper presents original research from the Trans PULSE Project, an Ontario-wide, community-based initiative that surveyed 433 participants using respondent-driven sampling. The purpose of this analysis was to determine the prevalence of, and risk and protective factors for, depression among female-to-male (FTM) Ontarians (n = 207). We estimate that 66.4% of FTMs have symptomatology consistent with depression. In multivariable analyses, sexual satisfaction was a strong protective factor. Conversely, experiencing transphobia and being at the stage of planning but not having begun a medical transition (hormones and/or surgery) adversely affected mental health in FTMs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".