Facilitators of and obstacles to practitioners’ adoption of harm reduction in cannabis use: a scoping review
Bibliographic record
Abstract
BACKGROUND: Cannabis use can generate potential avoidable harms, hence the need for effective preventive measures and treatment. Studies show the efficacy of harm reduction (HR) in minimizing undesirable consequences associated with this use. Despite its proven efficacy, HR in cannabis use remains poorly applied by many health and social services (HSS) practitioners, especially with young people. However, knowledge regarding the underlying reasons for this is limited. To fill this gap, we aimed to identify facilitators of and obstacles to HSS practitioners' adoption of HR in cannabis use across OECD countries. METHODS: We conducted a scoping review, guided by Arksey and O'Malley's model. The search strategy, executed on health databases and in the grey literature, captured 1804 studies, of which 35 were retained. Data from these studies were extracted in summary sheets for qualitative and numerical analysis. RESULTS: Facilitators and obstacles were grouped into four themes: stakeholders' characteristics (e.g., education, practice experience); clients' characteristics (e.g., personal, medical); factors related to HR (e.g., perceived efficacy, misconceptions); factors related to the workplace (e.g., type of workplace). Data were also extracted to describe the populations recruited in the selected studies: type of population, clientele, workplace. CONCLUSION: Several factors might facilitate or hinder HSS practitioners' adoption of HR in cannabis use. Taking these into consideration when translating knowledge about HR can improve its acceptability and applicability. Future research and action should focus on this when addressing practitioners' adoption of HR.
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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 | 0.040 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".