Mixed-methods study on professionals’ attitudes toward harm reduction in cannabis use and the development of a knowledge translation plan
Bibliographic record
Abstract
Several factors limit the adoption of harm reduction in cannabis use (HR-c). A knowledge translation (KT) process can help optimize its adoption. This study aims to: (1) identify the attitudes toward HR-c of health and social services (HSS) practitioners working among young people in Quebec; and (2) develop a KT plan to enhance its adoption. Two conceptual frameworks guided the study: the Knowledge-to-Action model and the Consolidated Framework for Implementation Research. Managers and practitioners working among young people in difficulty in Quebec were recruited. Mixed methods tools were used, involving consultations (N = 14) and questionnaires (N = 167). Qualitative data underwent thematic analysis, while descriptive and inferential statistics were executed to analyze quantitative data. Participants presented positive attitudes toward HR-c (M = 44.79), negative attitudes toward abstinence-based treatments (M = 9.68), and moderate perceived levels of training in HR-c (M = 12.3). Their needs and contextual factors that might influence HR-c adoption were identified. Based on these findings, a KT plan was developed to optimize HR-c adoption by HSS practitioners. Despite some negative factors impacting its applicability, HR-c is generally accepted and implemented by practitioners. This study represents the pre-implementation phase of the KT plan, which will then guide the effective implementation of a KT process for HR-c adoption.
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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 | 0.041 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".