Adverse childhood experiences (ACEs), peer victimization, and substance use among adolescents
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs) are common and related to substance use problems in adulthood. Less is known about these relationships in adolescence and if experiencing ACEs in addition to peer victimization (or bullying) would have an interaction or cumulative effect on the odds of adolescent substance use. METHOD: Data were used from the Well-Being and Experiences Study (The WE Study), a cross-sectional survey of adolescents aged 14-17 years (n = 1002) in Manitoba, Canada collected between July 2017 and October 2018. Statistical methods included descriptive statistics and logistic regression models. RESULTS: The prevalence of experiencing any of the 12 ACEs was 75.1 %. The prevalence of any peer victimization (monthly or more often) was 24.1 %. All individual ACEs were associated with increased odds of substance use. No significant interaction effects between ACEs and peer victimization on substance use were found. Significant cumulative effects were found, indicating that experiencing both ACEs and peer victimization, compared with experiencing ACEs only, significantly increased the odds of substance use among adolescents. CONCLUSION: The odds of substance use becomes significantly greater if the adolescent with a history of ACEs also experiences peer victimization. Further research aimed at effective prevention of ACEs, peer victimization, and substance use is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".