PW 0417 A three-step gendered latent class analysis on dating victimization profiles: correlates with family and peer contextual risk factors, injuries and mental health
Bibliographic record
Abstract
The purpose of this study was to identify patterns of teen dating victimization among boys and girls and examine their association with family and peer contextual risk factors and injuries and mental health indicators. As part of the population-based <i>Youths’ Romantic Relationships Project</i>, 8 230 high school students were questioned about their victimization experiences. Latent class analysis was used to identify classes for both girls and boys. Latent class analysis identified a best fitting model of three classes for girls: Low victimization (61% of girls), Sexual and psychological violence (27%) and Multiple victimization (12%). Similarly for boys, three classes were identified including a Low victimization (84% of boys), a Multiple victimization (9%) and an Unwanted sexual contacts and psychological violence (7%). Associations between class membership and family and peer contextual risk factors and mental health indicators revealed more distinctive features among classes for girls than for boys. Confirming our hypothesis, both genders in the Multiple violence class reported experiencing the most injuries (e.g., bruise or cut, pain the next day or need of a medical appointment). A history of childhood interpersonal traumas was significantly related to classes of dating victimization, suggesting that different forms of child abuse (neglect, exposure to interparental violence, physical or sexual abuse) are associated with a heightened risk of revictimization in the context of their first romantic relationships. Our findings suggest that child sexual abuse may act as a specific vulnerability factor for more pervasive forms of TDV for girls. The findings highlight the utility of a person-oriented approach to enhance our understanding of the diversity of victimization experiences in the context of teen romantic relationships. Results also underscore the importance of tailoring prevention efforts to efficiently tackle teen dating violence and the relevance of trauma-informed practices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".