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Record W4413358614 · doi:10.5334/ijic.nacic24220

Establishing a Comprehensive Substance Use Health Hub: An Integrated Approach to Reducing Substance Use-Related Harms in Guelph-Wellington

2025· article· en· W4413358614 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsSubstance useIntegrated careSubstance abuseNursingHealth careBusinessMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Background: In response to the escalating substance use crisis in Guelph-Wellington, the Guelph Community Health Centre (Guelph CHC) supported by many core partners through the Guelph-Wellington Ontario Health Team (GW OHT) developed the Substance Use Health Hub (SUHH) to provide comprehensive, wraparound care to individuals with complex, high acuity needs. This population frequently interacts with costly systems such as hospitals and correctional institutions but does not receive adequate support through traditional healthcare services. The SUHH aims to bridge this gap by offering non-stigmatizing, interdisciplinary care tailored to the unique needs of individuals who use drugs, are experiencing homelessness, and have co-occurring physical and mental health challenges. A key element of the SUHH is the active involvement of peers and people with lived and living experience (PWLLE), ensuring that services are both relevant and responsive to the community needs. PWLLE are integrated into the project as peer workers, support coordinators, and a dedicated Peer Lead who provides mentorship and support to enhance the program effectiveness and inclusivity. Audience: This session is designed for healthcare professionals, policymakers, community organization leaders, and researchers interested in integrated care models for substance use and mental health, with a particular focus on the involvement of PWLLE in program design and delivery. Approach: The 60-minute workshop will be structured as follows:- Introduction and presentation (20 min): Overview of the substance use crisis in Guelph-Wellington and history of how our project began, including the role of building relationships with core partners. We will also share a detailed explanation of our methodology for segmenting the population to help clarify the SUHH model, including best practices, evidence-based approaches, and co-designing services with PWLLE.- Case Studies (0 min): Presentation of different case studies and contexts to illustrate the SUHH's impact, highlighting the contributions of PWLLE.- Group Work (5 min): Interactive session where participants will work in groups to discuss and propose solutions to different scenarios based on real-life challenges faced, incorporating the elements shared in our presentation.- Feedback (0 min): Group presentations and feedback session to share insights and strategies with the larger group.- Closing (5 min): Summary of key takeaways and closing remarks. Outcomes: Participants will leave with a clear understanding of:- The critical need for integrated, wraparound care models in addressing substance use and mental health crises.- Practical insights into the structure and implementation of the SUHH.- Strategies for applying similar models in their own communities to improve care for individuals with complex health and social needs.- The importance and benefits of involving PWLLE in the design, implementation, and evaluation of substance use health programs and practical ways of doing this.- Enhanced awareness of how PWLLE contribute to creating a supportive and effective care environment.

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)0.002
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.849

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.004
Scholarly communication0.0020.002
Open science0.0030.010
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.086
GPT teacher head0.426
Teacher spread0.340 · 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
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 routes1
Has abstractyes

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