Victimization Experiences and Binge Drinking and Smoking Among Boys and Girls in Grades 7 to 12 in Manitoba, Canada
Bibliographic record
Abstract
Abstract Experiencing victimization, such as cyberbullying, discriminatory harassment, or bullying in adolescence is associated with health risk behaviours. However, inconsistent findings in the literature examining the associations between different types of victimization and binge drinking and smoking exist. This study investigated the association between nine types of victimization experiences and (a) binge drinking and (b) smoking among boys and girls in grades 7 to 12. Data were from the 2012/13 Manitoba Youth Health Survey that included 475 participating schools and 64,174 students. Students in grades 7 to 12 completed the survey at school. Logistic regression models were used to examine the relationships between victimization experiences and binge drinking and smoking. All analyses were stratified by gender and grade groups. All nine types of victimization experiences among boys and girls in grades 7 to 12 were significantly associated with binge drinking and smoking. Overall, a dose-response trend was observed with increasing experiences of victimization related to greater odds of binge drinking and smoking for boys and girls in grades 7 to 9. Findings indicate that specific victimization experiences are associated with increased odds of binge drinking and smoking among adolescents. Prevention efforts to reduce victimization and to help those who have experienced victimization need to be addressed at all grade levels as it may be associated with a reduction in risky behaviours such as binge drinking or smoking among adolescents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".