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Record W4409337845 · doi:10.5334/ijic.9487

Co-Creating a Lower Limb Preservation Change Initiative in an Ontario Health Team

2025· article· en· W4409337845 on OpenAlexaboutno aff
Brianna Orava

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careNursingMedicineProcess managementPublic relationsBusinessPolitical science

Abstract

fetched live from OpenAlex

People with diabetes and peripheral vascular diseases are at an increased risk for lower limb amputations. These amputations can have a significant impact on quality of life and overall health. In primary health care, diabetes management is comprehensive but there remains a care gap in publicly-funded foot care that can provide an upstream and preventative approach to lower limb preservation. This care gap is particularly pronounced for vulnerable populations in the community, particularly Indigenous and under/precariously-housed people. The Barrie and Area Ontario Health Team (BAOHT) prioritized this population health need by developing and implementing a change initiative that was people-centered and collaborative across sectors. In Ontario, Canada, Ontario Health Teams (OHTs) are a newer model of intersectoral health care designed to provide integrated care. Through the OHT model, community organizations coordinate with OHTs as a unified team to provide integrated and cohesive care with a focus on population health. As such, the BAOHT was an ideal change agent to propose and implement a lower limb change initiative. The Barrie and Area OHT engaged with policymakers to establish the need for this change initiative. An environmental scan revealed a high rate of amputations along with the care gap of publicly-funded foot care for at-risk individuals who have diabetes and peripheral vascular disease, particularly those who do not have a primary care provider, are under or precariously-housed, and Indigenous populations. There was also an identified lack of health human resources in the service area, particularly for chiropodists with different organizations in the region across sectors having difficulty with recruitment and retention. The BAOHT developed a collaborative capacity among organizational partners across health care sectors (i.e. acute, primary health care, and community partners) with co-creation of the program including patients, the Indigenous community and those with lived experience. The goal was to improve the population’s health through improved access to care and reducing health inequities. As a result of this innovative work, a foot care program that is coordinated around the needs of the people in the community, particularly vulnerable and at-risk populations was developed and implemented. This program includes two foot care nurses, a consulting vascular specialist, primary health care consultation and education, and community partner support. The foot care nurses hold clinics in rotating locations across sectors and population needs, including in Indigenous clinics, homeless shelters, and primary health care settings. Significant learning has included the importance of rigorous indicators and evaluation methodology, sustainable funding models, advocating for dynamic timelines that look towards long-term upstream change, and innovative collaborative governance across sectors that is adaptive to organizational and community needs. Evaluation of the program is currently being done with the goal of expanding the program to other OHT populations that are marginalized and/or at high risk for amputations. Programs like this are an example of innovative people-centered collaboration and can be a model for integrated care that focuses on population health at a national and international scale.

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)0.015
metaresearch head score (Gemma)0.015
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: none
Teacher disagreement score0.161
Threshold uncertainty score0.698

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.015
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0210.006
Scholarly communication0.0060.003
Open science0.0030.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.027
GPT teacher head0.365
Teacher spread0.338 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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