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Record W4409337190 · doi:10.5334/ijic.icic24571

The Diabetes Connections Initiative: Co-designing models of care for diabetes with First Nations through empowerment, autonomy, and ownership for health

2025· article· en· W4409337190 on OpenAlexaboutno aff
Sumeet Sodhi, Jessica Pace, Janet Gordon, Ariel Root, Camille M. Smith, Katie L. Johnson, Elizabeth Shepherd, Katie Wantoro, Candi Edwards, Terri Farrell

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentAutonomyDiabetes mellitusPatient EmpowermentMedicineNursingHealth careIntegrated careBusinessEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Introduction: Diabetes has a high prevalence and high morbidity among First Nations communities, exacerbated by a complex history of intersectional determinants of health, including intergenerational trauma from colonization and structural racism. Thus, our team chose a co-design approach to developing a model of care to help to reorient diabetes care in our region to support community empowerment, autonomy, and ownership. Background: Diabetes has reached a high prevalence with high morbidity among Indigenous people living in Canada today, with a complex history of intersectional determinants of health, including intergenerational trauma from colonization and structural racism at its root. This is exemplified through Indian Residential School experiences of physical and sexual abuse, disconnection from land and traditional lifestyles, and resultant mental health and addictions challenges, as well as the socioeconomic factors of decreased access to adequate and affordable housing, nutrition, education & employment options, and accessible & culturally safe health care. The ‘60’s Scoop’ and overrepresentation in child welfare and justice systems have also resulted in further family separation. Ongoing colonization and marginalization also play a critical role, as many interventions aimed at Indigenous communities fail due to limitations of Western health system structures to adapt and respond to the cultural contexts of Indigenous people. Objectives: The purpose of our work is to improve diabetes prevention and treatment for First Nations communities in the Sioux Lookout area by strengthening the capacity at a local level to help achieve a shared vision of “community-empowered diabetes care that is wholistic and sustainable”. We do this in partnership with community members, leaders, local health authorities, and health care providers, as well as provincial government and academic collaborators. Methods: Our team co-created the Diabetes Connections Initiative, an integrated, evidence-based, customized, community-owned model of diabetes care and support, which we are scaling up implementation among 33 on-reserve First Nations communities in the Sioux Lookout area in Northwestern Ontario (population ~40,000). Our approach to developing the model of care prioritized collaborative community engagement, which centres on building reciprocal trust, maintaining relationships over time, and consistent open dialogue. Results: Our model of care includes four strategic areas: 1) Supporting community health workers to have the capacity to assist with the delivery of diabetes prevention and care close to people’s own homes as valued members of a primary health care team; 2) Improving the quality of care throughout a person’s life course with a focus on trauma-informed and culturally-appropriate care; 3) Enhancing integration of health information systems and community-relevant data sharing to support evidence based decision making; and 4) Increasing community ownership through integrated knowledge translation. Conclusion: This approach to co-developing a model of care, which includes collaborative community engagement at its core, has potential to have great impact on not just diabetes, but also for addressing other chronic conditions including cardiovascular disease, cancer, and reproductive health, especially where access to care is limited through geographic, sociocultural and socioeconomic, or other structural barriers.

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)0.021
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0030.013
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.023
GPT teacher head0.350
Teacher spread0.327 · 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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