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Record W4413558934 · doi:10.2196/72457

Evaluation of the Implementation of an Outreach Clinic for Opioid Use Disorder: Protocol for a Participatory Cocreation and Implementation Study

2025· article· en· W4413558934 on OpenAlexaffvenueabout
Andrée-Anne Paré-Plante, Laurence Fortin, David-Martin Milot, Catherine C. Langlois, Charlotte Payette-Toupin, Bao Duyen Angéline Nguyen, Sophie Poulin, Karine Bertrand, Caroline Leblanc, Lara Maillet, François Racine-Hemmings, Isabelle Wilson, Christine Loignon

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsÉcole Nationale d'Administration PubliqueUniversité de Sherbrooke
Fundersnot available
KeywordsOutreachPreprintProtocol (science)Participatory action researchCitizen journalismCommunity-based participatory researchMedicineComputer scienceWorld Wide WebAlternative medicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The opioid overdose crisis currently affecting Canada has resulted in thousands of deaths, and the COVID-19 pandemic has exacerbated the consequences of this crisis, particularly through the instability of the unregulated drug market. The province of Quebec is observing a similar pattern: the opioids consumed are more dangerous, and the number of overdoses is rising. Opioid use disorder (OUD) therefore represents a major public health issue. Offering appropriate interventions, such as opioid agonist therapy integrated into primary care, is one strategy to reduce the risk of death from overdose. OBJECTIVE: The aim of this research is to evaluate the implementation of an outreach clinic offering a low-threshold treatment program for OUD in Quebec. The secondary objective is to identify the factors that foster the participation in primary care research of people who are socially excluded and have current or past lived experience of OUD. METHODS: This study is being conducted in the Montérégie region of Quebec and comprises 3 phases: exploratory, photovoice, and participatory evaluation. The qualitative research adopts a participatory approach by involving people who are socially excluded and targeted by the outreach clinic's services (eg, people experiencing homelessness and living with OUD). A committee of peer researchers, made up of experts with current or past lived experience of OUD, will be set up and will hold 10 meetings at various stages of the research. Two participant profiles will be involved: (1) health care professionals and community workers, who will take part in semistructured interviews; and (2) people with current or past lived experience of OUD, who will take part in the photovoice sessions or peer researcher committee meetings. RESULTS: The peer researcher committee was formed in winter 2024, and 10 meetings had been held as of June 2025. As of August 2025, 4 photovoice sessions had been conducted, and 14 health care professionals and community workers had participated in the semistructured interviews. This study was funded in September 2022, with funding available through March 2025. Data were collected from September 2022 through June 2025. The analysis was finished in spring 2025. Results of the study are expected to be published in winter 2026. CONCLUSIONS: The anticipated outcome is the establishment of an outreach clinic for OUD outside a major urban center, with a range of services tailored to the needs of people who are socially excluded and living with OUD. The coconstruction of this clinic in collaboration with people with current or past lived experience of OUD will enable an adequate response to the targeted population's overall health needs and help reduce the barriers to access that they may face in conventional care structures. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72457.

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)0.178
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.178
Threshold uncertainty score0.939

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1780.087
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.004
Science and technology studies0.0090.005
Scholarly communication0.0050.004
Open science0.0070.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0400.006

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 head0.661
GPT teacher head0.764
Teacher spread0.103 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes3
Has abstractyes

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