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Record W4415731664 · doi:10.1186/s12961-025-01411-y

Implementation and evaluation of a knowledge translation process to optimize the adoption of harm reduction in cannabis use by practitioners working with youth in Quebec: a mixed-methods study

2025· article· en· W4415731664 on OpenAlexafffundabout
R. Haddad, Jean‐Sébastien Fallu, Christophe Huỳnh, Laurence D’Arcy, Song Yuan, Christian Dagenais

Bibliographic record

VenueHealth Research Policy and Systems · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité du Québec à Trois-RivièresCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de Montréal
FundersFonds de Recherche du Québec-Société et CultureMinistère de la SantéFonds de Recherche du Québec - SantéMinistère de la Santé et des Services sociaux
KeywordsHarm reductionProcess (computing)Health services researchKnowledge translationCannabisHarmPublic healthHealth administration

Abstract

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BACKGROUND: Cannabis use initiation is highly common among youth. Harm reduction in cannabis use (HR-c) has proven effective in minimizing the potential harms of the substance. However, its adoption by health and social services (HSS) practitioner's remains limited owing to several obstacles. This study marks the final phase of a broader research initiative. It aims to: (1) present the implementation of a knowledge translation (KT) plan developed to enhance HR-c adoption and formulate actionable strategies to support its applicability; and (2) evaluate its immediate and short-to-medium-term effects. METHODS: Ziam et al.'s (2024) evaluation framework guided our description of the KT plan implementation and our evaluation of its effects. Using a non-probabilistic sampling method, we recruited managers and practitioners from four residential facilities for youth in Quebec (N = 19). Two KT strategies - policy briefs and deliberative dialogues - were implemented, during which participants co-developed final actions to optimize HR-c adoption. A mixed-methods evaluation followed, involving a questionnaire with five scales and semi-structured interviews. Data were analyzed using post-parallel analysis, combining descriptive statistics and thematic analysis to assess the KT plan's implementation and effects. Cronbach's alpha of the subscales was calculated to assess their internal consistency. RESULTS: The final actions formulated with participants addressed HR-c, youth and organizations. Quantitative findings revealed: (1) a high appreciation for the deliberative dialogues; (2) positive attitudes toward HR-c; (3) negative attitudes toward abstinence-based treatments; (4) participants' favourable perception of their training level in HR-c; and (5) a strong intention to implement the proposed actions. The qualitative findings revealed that participants were using the transferred knowledge (e.g., HR-c strategies applicable by youth) and planned to disseminate the formulated actions within their teams to enhance practices. CONCLUSIONS: A systematic and multidirectional KT process was implemented to optimize HR-c adoption among HSS practitioners working with youth in Quebec, serving as a model for similar interventions. The study reinforced HR-c applicability, facilitated its adoption and contributed to the formulation of concrete implementation actions. Future research should examine the long-term impact of KT initiatives on HR-c adoption and explore strategies to support practitioners in applying the transferred knowledge.

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 imitation

Not 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.

metaresearch head score (Codex)0.107
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1070.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.453
GPT teacher head0.598
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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