Sexual Identity Is Associated With Adverse Childhood Experiences in US Early Adolescents
Bibliographic record
Abstract
OBJECTIVE: To determine disparities in adverse childhood experiences (ACEs) by sexual identity in a national cohort of early adolescents. METHODS: We analyzed cross-sectional data from year 2 of the Adolescent Brain Cognitive Development study (N = 10,934, 2018-20, ages 10-14 years). Disparities in ACE scores across lesbian, gay, or bisexual (LGB), not sure, and heterosexual adolescents were assessed using multinomial logistic regression analyses. Logistic regressions estimated the associations between sexual identity and each individual ACE. Analyses were adjusted for potential confounders. RESULTS: In adjusted models, LGB adolescents had a higher risk of experiencing 2, 3, or ≥4 ACEs (relative risk ratios [RRR] = 1.57, 95% Confidence Interval (CI) 1.01-2.42), 3 (RR = 1.78, 95% CI 1.100-2.88), or ≥4 ACEs (RRR = 3.20, 95% CI 1.92-5.32), and not sure adolescents had a higher risk of having ≥4 ACEs (RRR = 2.17, 95% CI 1.22-3.87), compared to heterosexual adolescents. LGB and not sure adolescents had higher risks of reporting emotional abuse ("yes" OR = 4.21, 95% CI 1.84-9.61; "maybe" OR = 6.20, 95% CI 2.91-13.19) and parent mental illness ("yes" OR = 1.95, 95% CI 1.48-2.57; "maybe" OR = 1.63, 95% CI 1.21-2.18) compared to heterosexual adolescents. CONCLUSIONS: LGB adolescents and those questioning their sexual identity were at greater risk of having higher ACE scores, with LGB adolescents experiencing the highest risk of experiencing ACEs. LGB adolescents also had higher odds of reporting emotional and parent mental illness. Recognizing this heightened risk of ACEs in early adolescence is critical for designing clinic and school-based interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".