Factors associated with alcohol and substance use in adolescents aged 12–15: A rapid review
Bibliographic record
Abstract
Adolescents can often adopt behaviors that put them at risk and compromise their health. Among these behaviors, the most prominent is the use of psychoactive/addictive substances (alcohol, tobacco, and other illicit drugs). In recent years, there has been an increasing utilization trend of these substances in adolescents and within the peer group. The objective of this paper is to identify the factors associated with alcohol and substance consumption in adolescents aged 12–15. We followed the Cochrane recommendations for this rapid review. We used the CINAHL database through EBSCO to search for articles. Covidence software was used to screen the title and abstract, as well as the full text. Data extraction was carried out according to a table created by two reviewers. Quality and risk of bias were assessed using the Joanna Briggs Institute tool. Ten articles published between 2020 and 2024 and originating in the United States and Canada were included in the study. The studies included adolescents aged 11–19, encompassing the 12–15 age group targeted in our study, and examined substances such as alcohol, tobacco, cannabis, cocaine, prescription drugs, and over-the-counter drugs. There are several contextual, relational, and personal factors that are positively and negatively associated with substance use in adolescents, and these are considered risk factors and protective factors, respectively. The findings of this rapid literature review contribute to the formulation of health policies and substance use prevention programs at global, European, national, and community levels.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".