Exploring quality improvement for diabetes care in First Nations communities in Canada: a multiple case study
Bibliographic record
Abstract
BACKGROUND: Indigenous peoples in Canada experience higher rates of diabetes and worse outcomes than non-Indigenous populations in Canada. Strategies are needed to address underlying health inequities and improve access to quality diabetes care. As part of the national FORGE AHEAD Research Program, this study explores two primary healthcare teams' quality improvement (QI) process of developing and implementing strategies to improve the quality of diabetes care in First Nations communities in Canada. METHODS: This study utilized a community-based participatory and qualitative case study methodology. Multiple qualitative data sources were analyzed to understand: (1) how knowledge and information was used to inform the teams' QI process; (2) how the process was influenced by the context of primary care services within communities; and (3) the factors that supported or hindered their QI process. RESULTS: The findings of this study demonstrate how teams drew upon multiple sources of knowledge and information to inform their QI work, the importance of strengthening relationships and building relationships with the community, the influence of organizational support and capacity, and the key factors that facilitated QI efforts. CONCLUSIONS: This study contributes to the ongoing calls for research in understanding the process and factors affecting the implementation of QI strategies, particularly within Indigenous communities. The knowledge generated may help inform community action and the future development, implementation and scale-up of QI programs in Indigenous communities in Canada and globally.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".