Mapping the Landscape: A Scoping Review of Evaluated Substance Use Harm Reduction Programs for Youth
Bibliographic record
Abstract
Youth are particularly vulnerable to adverse outcomes from substance use, and there is limited evaluated drug education targeting youth in Canada or the United States of America (US). This scoping review identified and synthesized existing literature on evaluated harm reduction substance use education programs for school-aged youth in Canada and the US. Following the methodological framework outlined by Arksey and O’Malley, a database search identified relevant articles published between 2012 and 2025 through MEDLINE, Scopus, APA PsycInfo, ERIC, Academic Search Complete, Social Work Abstracts, and Embase. Of 1912 unique citations, 20 studies met our inclusion criteria; of these, 18 programs were implemented in the US and two in Canada with various target populations: high school ( n = 4), middle school ( n = 6), elementary school ( n = 2), at-risk youth ( n = 3) and other ( n = 5). Most programs focused on reducing substance use frequency ( n = 16) and used quantitative evaluation methods ( n = 16). The results highlighted a shortage of evaluated harm reduction programs for school-aged youth in Canada and the US. These findings will support the development and evaluation of a drug education strategy incorporating harm reduction principles for school-aged youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".