Identifying strategies to improve diabetes care in Alberta, Canada, using the knowledge-to-action cycle
Bibliographic record
Abstract
BACKGROUND: Strategic clinical networks, a recent development in the health system in Alberta, have been charged with bringing together front-line clinicians, researchers and policy-makers to identify variation in clinical care, and to propose standards, pathways and innovative solutions to improve access and quality of care. Here, we describe a collaborative workshop held between researchers and the Obesity, Diabetes and Nutrition Strategic Clinical Network to describe barriers to and facilitators of care for people with diabetes and to identify quality improvement interventions that should be prioritized. METHODS: Through collaboration between health researchers and the strategic clinical network, and using principles of the knowledge-to-action cycle, we identified barriers to and facilitators of diabetes care using data from a patient survey and a provider focus group (5 primary care physicians and 1 diabetes educator). In addition, we identified best evidence from a systematic review of quality improvement initiatives in diabetes. This information was reviewed at a multistakeholder workshop where potential quality improvement initiatives were considered at various service levels. RESULTS: A pilot survey involving 59 patients with diabetes and a focus group of primary care and allied health care providers identified several important barriers to optimal outcomes in diabetes care, including patient-level financial barriers to care and difficulty navigating the health system. Our collaborative discussion using the knowledge-to-action cycle prioritized feasible, evidence-based interventions to improve outcomes for patients with diabetes, including enabling care by allied health care providers and creating clear care maps and processes for system navigation. INTERPRETATION: We identified important barriers to achieving optimal outcomes in diabetes that may be overcome through the use of evidence-based quality improvement interventions. As recommended within the knowledge-to-action cycle, future research is required to determine whether program implementation improves outcomes and is cost-effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".