Protective Factors for Decreasing Nicotine, Alcohol, and Cannabis Use Among Adolescents with a History of Adverse Childhood Experiences (ACEs)
Bibliographic record
Abstract
Abstract Adolescents who have experienced adversity have an increased likelihood of using substances. This study examined if individual-, family-, school-, and community-level protective factors were associated with a decreased likelihood of substance use. Data from the Well-Being and Experiences Study (the WE Study) collected from 2017 to 2018 were used. The sample was adolescents aged 14 to 17 years (N = 1002) from Manitoba, Canada. Statistical methods included descriptive statistics and logistic regression models. The prevalence of past 30-day substance use was 20.5% among boys and 29.2% among girls. Substance use was greater among adolescent girls compared to boys. Protective factors associated with an increased likelihood of not using substances included knowing culture or language, being excited for the future, picturing the future, sleeping 8 to 10 h per night (unadjusted models only), participating in non-sport activity organized by the school, having a trusted adult in the family, frequent hugs from parent, parent saying “I love you” (unadjusted models only), eating dinner together every day, mother and father understanding adolescent’s worries and problems, being able to confide in mother and father, feeling close to other students at school, having a trusted adult at school, feeling a part of school, having a trusted adult in the community (unadjusted models only), volunteering once a week or more, and feeling motivated to help and improve one’s community. Knowledge of protective factors related to decreased odds of substance use may help inform strategies for preventing substance use and ways to foster resilience 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".