Sexual identity, child maltreatment, mental health, and substance use among emerging adults aged 18 to 23 years
Bibliographic record
Abstract
OBJECTIVES: Although past studies have identified sex differences in child maltreatment experiences and poor mental and physical health‒related outcomes, more research is needed to understand child maltreatment among sexual minorities (i.e., those who identify as other than heterosexual) and how child maltreatment and sexual identity are related to depression, anxiety, and at-risk alcohol and cannabis use among emerging adults. METHODS: Data were drawn from the longitudinal Well-Being and Experiences (WE) Study collected from 2017 (14 to 17 years) to 2022 (18 to 23 years) from Manitoba, Canada (n = 584). Descriptive statistics and logistic regression models were computed. RESULTS: Compared to heterosexual or straight sexual identity: homosexual, gay or lesbian; bisexual; and different or other identity were associated with an increased likelihood of experiencing child maltreatment, with the most robust relationships for bisexual identity and all child maltreatment outcomes. Indicating "I don't know" for sexual identity compared to heterosexual identity was associated with 7.45 increased odds of exposure to intimate partner violence in adjusted models. Bisexual identity compared to heterosexual identity had the most robust association, with increased odds of depression, anxiety, at-risk alcohol use, and at-risk cannabis use. Findings provide some evidence to suggest that trends may be worse for some mental health and substance use outcomes among sexual minorities who also experience child maltreatment. CONCLUSION: Preventing child maltreatment among all children, including youth identifying as other than heterosexual, is a public health priority. Such efforts will work towards optimizing mental health and reducing substance use in early adulthood.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".