Adverse childhood experiences and mental health of sexual and gender minorities in Canada, USA, and Japan
Bibliographic record
Abstract
BACKGROUND: Sexual and gender minorities have an increased risk for adverse childhood experiences (ACEs) and worsening mental health. However, cross-country research on varying levels of social acceptance remains limited. We compared the impact of sexual and gender minority status on ACEs and poor mental health between Canada, USA, and Japan. METHODS: This cross-sectional study included 50,628 adults in 2022. Sexual and gender minority individuals included those identifying as lesbian, gay, bisexual, transgender, or non-binary. Poor mental health was defined as experiencing not good mental health for ≥ 14 days in the past 30 days in USA, and scoring ≥ 13 on the Kessler 6-item Psychological Distress Scale in Canada and Japan. ACEs included witnessing domestic violence, experiencing physical abuse, and experiencing sexual assault. Social acceptance was assumed high in Canada, moderate in USA, and low in Japan based on Global Acceptance Index 2017-2020. RESULTS: Sexual and gender minorities reported experiencing sexual assault more frequently (odds ratio = 1.68, 95 % confidence interval = 1.17-2.42). Sexual and gender minorities in USA had a higher risk for sexual assault compared to those in Japan (1.71, 1.06-2.74). After adjusting for presence of ACEs, sexual and gender minorities reported more frequent poor mental health (1.77, 1.40-2.24). Sexual and gender minorities in Canada had a higher risk for poor mental health compared to those in Japan (1.85, 1.21-2.80). CONCLUSION: Sexual and gender minorities faced a higher risk of ACEs and poor mental health regardless of country. Further research is needed to understand the underlying mechanisms of the increased risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".