Profiles and Predictors of Dating Violence Among Sexual and Gender Minority Adolescents
Bibliographic record
Abstract
PURPOSE: Sexual and gender minority adolescents report higher levels of dating violence compared with their heterosexual and cisgender peers. The objectives of the present study were to (1) identify latent profiles of dating violence; (2) examine if sexual and gender minority adolescents were particularly vulnerable to certain profiles of dating violence; and (3) explore how experiences of peer victimization, discrimination, and parental maltreatment explained this greater vulnerability. METHODS: High school students in Grades 9 and 11 from the 2016 Minnesota Student Survey (N = 87,532; mean age = 15.29 years, SD = 1.23) were asked about their sexual and gender identities, their gender nonconformity, their experiences of verbal, physical, and sexual dating violence victimization and perpetration, as well their experiences of childhood maltreatment, peer victimization, and gender-based and sexual minority status-based discrimination. RESULTS: Multinomial logistic regression analysis in a three-step latent class analysis procedure suggested five profiles of dating violence victimization and perpetration across the entire sample. Sexual and gender minority adolescents were generally more likely to be in classes high in dating violence victimization, perpetration, or both, compared with their heterosexual and cisgender peers. Gender nonconformity was also associated with greater risk for being in high dating violence classes. These differences, however, were generally nonsignificant when the social stressors of childhood maltreatment, peer victimization, and experiences of discrimination were accounted for. CONCLUSIONS: Although findings suggested greater vulnerability for dating violence among sexual and gender minority adolescents, they underscore the importance of how minority stressors generally accounted for this greater vulnerability for dating violence.
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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 | 0.003 |
| 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".