Developing harm reduction in the context of youth substance use: insights from a multi-site qualitative analysis of young people’s harm minimization strategies
Bibliographic record
Abstract
BACKGROUND: Youth substance use programming and educational strategies are frequently informed by prevention approaches that emphasize abstinence goals, which often do not resonate with youth in their lack of acknowledgment of young people's social context and how young people perceive positive effects of substance use. Further, approaches to drug prevention have been critiqued as adopting a one-size-fits-all approach and therefore inadequate in addressing substance use in the context of population variation and inequities. In response to the limitations of current approaches to prevention, programming informed by harm reduction principles that aims to minimize harms without requiring abstinence is emergent in school settings. However, youth perspectives informing harm reduction are limited in both research and program development. METHODS: This paper draws on data from the Researching Adolescent Distress and Resilience (RADAR) study, which utilized an ethnographic approach to bring youth voice to the literature on mental health and substance use. Qualitative data collection included individual interviews (n = 86) with young people aged 13-18 across three communities-representing urban, suburban, and rural geographies-in British Columbia, Canada. A multi-site qualitative analysis of interview data was conducted to identify themes across and within each research site. RESULTS: Across all three sites, young people's individual experiences of substance use were shaped by geographic, socio-cultural, and political contexts, with youth describing their use in relation to the nature of substance use in peer groups and in the broader community. To manage their own substance use and reduce related harms, youth employed a variety of ad hoc harm minimization strategies that were reflective of their respective contexts. CONCLUSIONS: The findings from this study suggest the importance of harm reduction approaches that are contextually relevant and responsive to the lived experiences of youth. Youth perspectives in the development of harm reduction programming are needed to ensure that approaches are relatable and meaningful to young people, and effective for promoting the minimization of substance-related harms.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".