Bullying Victimization and e‐Cigarette Use among Middle and High School Students
Bibliographic record
Abstract
BACKGROUND: Bullying has been associated with several adverse health outcomes, including substance use. However, little is known about the association between bullying and e-cigarette use. This study examined the association between bully victimization and the frequency of e-cigarette use. METHODS: Data from the 2016-2017 Canadian Student Tobacco, Alcohol and Drugs Survey were used (N = 49,543). The target population consists of Canadian students enrolled in grades 7-12. Multivariable logistic regression models were used to examine the association between bullying victimization status and e-cigarette use. RESULTS: Among the students included in the study, 14.1% were bullied less than once a week. Bullying victimization was statistically significantly associated with higher odds of any e-cigarette use in the last 30 days. Likewise, those bullied daily or almost daily were more likely to use e-cigarettes more frequently compared to students not bullied. We found a statistically significant difference in analysis stratified by sex, with female bullying victims having higher odds of all measures of e-cigarette use. CONCLUSIONS: Bullying victims were significantly more likely to use an e-cigarette, and findings appeared to vary by sex. Female bullying victims had a higher likelihood of e-cigarette use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".