Childhood conduct problems and adolescent sexual harassment victimization: Understanding the role of cumulative processes
Bibliographic record
Abstract
Sexual harassment victimization is prospectively associated with poorer mental health, accounting for other forms of victimization. To better understand the link between childhood conduct problems and vulnerability for sexual harassment, the three objectives of this study were to (1) examine the links between childhood conduct problems and sexual harassment victimization during early adulthood; (2) identify the mediating role of common developmental sequelae of childhood conduct problems (adolescent peer victimization, substance abuse, risky sexual behavior, and number of delinquent peers) in this association; and (3) explore whether these links differed for boys and girls. Participants were drawn from a longitudinal sample that over-recruited children with conduct problems ( N = 744; 50.5% conduct problems; age M : 8.42; 47% girls), from Quebec, Canada. Childhood conduct problems were linked to increased sexual harassment victimization in early adulthood (β = 0.08, p < .05), an association that was mediated by adolescent peer victimization (β = 0.04, p < .01; 95% confidence interval [CI]: 0.01, 0.06) and adolescent substance use (β = 0.02, p < .05; 95% CI: 0.003, 0.05). Differences were not observed across either model for boys and girls. Findings suggest the pertinence of assessing the existing prevention and intervention approaches for populations with conduct problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".