From compliance to connection: How primary care providers can support diabetes care in a good way
Bibliographic record
Abstract
This integrative review, “From Compliance to Connection: How Primary Care Providers Can Support Diabetes Care in a Good Way,” explores how primary care providers (PCPs) can more effectively support diabetes self-management among Indigenous adults in Canada. Type 2 Diabetes Mellitus (T2DM) disproportionately affects Indigenous communities, a disparity rooted in colonialism, intergenerational trauma, and systemic inequities. Historically, Western medicine has pathologized Indigenous bodies and dismissed Indigenous knowledge, reinforcing narratives of “non-compliance” and undermining trust in care relationships. Using an integrative literature review methodology, 11 studies were identified through CINAHL, Google Scholar, and hand searching. These were appraised using the Critical Appraisal Skills Programme (CASP) and an adapted Aboriginal and Torres Strait Islander Quality Appraisal Tool. Findings were analyzed through a culturally grounded qualitative synthesis. The review revealed that diabetes is often experienced not only as a physiological condition, but also as a relational and psychological disruption. Barriers to effective management included biomedical dominance, lack of cultural safety, ineffective diabetes education, and the framing of resistance as non-compliance. However, promising practices emerged: the integration of traditional medicine, use of culturally resonant strategies like peer support and talking circles, and the alignment of biomedical concepts with Indigenous worldviews. This review urges PCPs to move from authority-driven models toward relational, culturally-grounded care. It concludes with implications for practice, education, and research, centring Indigenous-led approaches in all aspects of diabetes care.,
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".