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Record W4409337193 · doi:10.5334/ijic.icic24277

A Community-Based Approach to Assessing the Success of London, Ontario’s Whole of Community System Response to Health and Homelessness

2025· article· en· W4409337193 on OpenAlexaboutno aff
Matthew Meyer, C. Nadine Wathen, Heather Lokko

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careHousing FirstNursingSociologyHealth careMedicineGerontologyPolitical scienceMental healthPsychiatryMental illness

Abstract

fetched live from OpenAlex

Introduction: Homelessness, and it’s associated challenges, have reached crisis levels across Canada and in many places around the world. The lack of safe, supportive, and affordable housing has led to unprecedented numbers of people experiencing homelessness and housing precarity. Coupled with under-resourced social support services and an over-stretched healthcare system, this has led to avoidable death and suffering. In London, Ontario, Canada local protests by providers and caring citizens in July 2022 resulted in development of a Whole of Community System Response to Health and Homelessness that is garnering national and international interest. As part of this response, partners have come together to establish a coordinated research/evaluation strategy. Objective: To describe the methods used to establish a coordinated community-based approach to evaluation of our Health and Homelessness plan and share early findings Methods: As part of the Whole of Community System Response to Health and Homelessness, a System Foundations table was formed with mandates to inform 1) evaluation/research 2) foundational processes and 3) policy development. Table membership was created via open call to all 70+ partner organizations. Evaluation framework development began with defining core values (including a trauma- and violence-informed, anti-oppressive and equity-promoting approach) and adopting commonly-used evaluation strategies. An open call to local researchers, analysts, decision support, and evaluation experts was used to form 9 ‘evaluation teams’ and 1 ‘evaluation oversight’ team. A University-Community Research Centre (the Centre for Research on Health Equity and Social Inclusion) was nominated to provide arms’ length coordination and facilitation of these activities. Results: The emerging oversight team will serve as a neutral facilitator to support review of meeting documents, attend other working group meetings, and engage community to identify key questions. The Quintuple Aim of Health System Improvement and Donabedian’s Triad were endorsed as the evaluation framework core along with a commitment to mixed-methods approaches and 'Now, Next, Later' framework to inform timing of evaluation and research deliverables. Nine evaluation teams are, therefore, exploring 1) outcomes and experiences within London’s highest-need homeless; 2) outcomes and experiences within London’s otherwise homeless and precariously housed population; 3) outcomes and experiences within London’s general population; 4) experiences of the workforce; 5) health equity; 6) cost of care; 7) processes; 8) structures; 9) overall project review. Facilitated by the Oversight team, evaluation/research relevant questions will be prioritized and brought to the appropriate Evaluate Teams who will coordinate appropriate processes for answering them now and over time to track collective impact. Research and evaluation teams will leverage existing resources wherever possible and be purposefully built represent different disciplines, traditions and backgrounds to ensure a wholistic research and evaluation approach (including indigenous research, evaluation, and pedagogy). Care will be taken to reflect the complexity of the system and ensure fairness and equity in all research activities. Conclusion: London’s Whole of Community System Response to Health and Homelessness is, we understand, a unique strategy requiring equally unique evaluation and research. This presentation aims to describe how this evaluation approach is being designed and implemented, and share early findings as available.

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)0.013
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.098
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0100.004
Scholarly communication0.0040.001
Open science0.0030.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.424
Teacher spread0.372 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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