The First Nations Community Experiences With the SOAR Research Program: Improving Type 2 Diabetes Prevention and Management
Bibliographic record
Abstract
OBJECTIVES: Indigenous peoples in Canada are considered the highest risk populations for type 2 diabetes mellitus (T2DM). Quality improvement (QI) strategies may be considered effective interventions for improving T2DM health outcomes. The purpose of this study was to understand experiences associated with implementation of the SOAR QI program to improve prevention and management of T2DM. METHODS: A qualitative study was conducted, and in-depth, semistructured interviews were held with QI team members and key contacts, in person and through videoconference with 2 First Nations communities. Interviews were audio-recorded and transcribed for data analysis. RESULTS: Ten interviews were conducted and emerging themes from the data analysis were organized into 2 categories: facilitators and barriers. Four subthemes were identified. Two subthemes emerged under the category of facilitators (cultural relevance and partnership building) and 2 subthemes emerged under the category of barriers (workload burden, role ambiguity). CONCLUSIONS: This study highlights the necessity of implementing diabetes QI strategies that foster cultural sensitivity and provide opportunities for partnership building to strengthen community relationships. We also highlight the importance of diminishing role ambiguity and increased workload burdens, which can hinder the successful implementation of QI programs long term. Our findings can be used to improve future adaptations of SOAR and other diabetes First Nations-focussed QI strategies to benefit Indigenous people in acquiring optimal outcomes relative to T2DM care. Findings can also inform the design, practices, and policies of such QI interventions in support of the spread and sustainability of the intervention long term.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".