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Record W4407982726 · doi:10.1080/09687637.2025.2470129

Barriers and facilitators to implementing a first managed alcohol program in Montreal, Canada

2025· article· en· W4407982726 on OpenAlexaffabout
Rossio Motta‐Ochoa, Stéphanie Marsan, Natalia Incio-Serra, Esmé-Renée Audéoud, Annie Talbot, Manuela Mbacfou Temgoua, Monika Andrea Barbe-Welzel, Mark Alsop, Christie Chuprun, Jorge Flores-Aranda

Bibliographic record

VenueDrugs Education Prevention and Policy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversité de Montréal
Fundersnot available
KeywordsIndigenousPsychological interventionAlcoholPolitical scienceEnvironmental healthGerontologyPsychologyNursingMedicine

Abstract

fetched live from OpenAlex

Background Indigenous people experiencing homelessness, are disproportionately affected by the health risks associated with alcohol use. Managed alcohol programs (MAPs) are interventions that address alcohol use and homelessness by providing housing and regulated doses of alcohol. While research has shown MAPs to be beneficial, little is known about their implementation. Our paper addresses this gap by exploring the barriers and facilitators to implementing a MAP for Indigenous men in Montreal, Canada.Methods The study follows a qualitative approach. Semi-structured and informal interviews along with questionnaires were conducted with the program’s residents, staff and medical team members. The collected data were thematically analyzed.Results Participants identified barriers including resistance to adhering to the individualized alcohol plan, hostile behaviours and conflicts, loss of purpose and boredom, and staff workload and turnover. The facilitators reported were teamwork, flexibility, cultural safety, and trusting relationships.Conclusion Our findings about certain barriers, particularly hostile behaviours and conflicts, and staff workload and turnover, remain undocumented in the literature. Certain facilitators including flexibility, trusting relationships, and cultural safety merit further exploration. In offering a detailed description of the implementation of a MAP, we hope to inform policies supporting the dissemination of similar programs in other contexts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.433
Teacher spread0.416 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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