Child Sexual Abuse among a Representative Sample of Quebec High School Students: Prevalence and Association with Mental Health Problems and Health-Risk Behaviors
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to estimate the prevalence of child sexual abuse in a representative sample of Quebec high school youths and document its associations with mental health problems and health-risk behaviors. METHOD: Data were drawn from the Quebec Youths' Romantic Relationships Survey, which involved a one-stage stratified cluster sampling of 34 Quebec high schools from Grades 10 to 12. A total of 8,194 youths (mean age = 15.35) were recruited. The survey assessed child sexual abuse, mental health problems (psychological distress, post-traumatic stress symptoms, suicidality), health services utilization, and health-risk behaviors (alcohol, drug, and cannabis use). Gender-stratified multivariate analyses were used to assess associations between child sexual abuse and mental health problems and health-risk behaviors while controlling for confounding demographic variables and other forms of child maltreatment experienced in childhood. RESULTS: A total of 14.9% of girls and 3.9% of boys reported having experienced child sexual abuse. Child sexual abuse was independently associated with an increased risk of psychological distress, greater health services utilization, and increased health-risk behaviors, after controlling for other forms of childhood maltreatment experienced. CONCLUSIONS: Child sexual abuse is prevalent among youths in Quebec and is associated with an increased risk of a host of negative consequences. Continued efforts in the development of early detection strategies as well as prevention and intervention programs are warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".