A knowledge translation intervention to improve cholesterol management in diabetes in remote Aboriginal communities
Bibliographic record
Abstract
Objectives. (1) To explore the barriers to implementation of diabetes clinical practice guidelines in remote aboriginal communities. (2) To test the feasibility and effectiveness of prioritizing care processes and audit and feedback in improving cholesterol management in diabetic patients in reserves in Northern Ontario. Conclusion. Simplified and prioritized key points and audit and feedback are an appropriate and effective method to increase statin prescription rates in diabetic patients with elevated cholesterol in remote aboriginal settings, but not diabetic patients in general. Physicians and nurses both have priority indecision regarding diabetes care, but respond differently to reducing complexity of care. Results. Providers identified a range of factors leading to indecision in prioritization of processes of diabetes care. Prioritized key points derived from this were well received (though poorly understood) by nurses, but rejected by doctors because they de-individualized care. The audit and feedback was accepted by all providers, and they felt that following up patients did not significantly increase their workload. The 2 intervention communities included 153 patients and the 2 controls had 158 patients, who were comparable at baseline. The intervention did not increase statin prescription rates in diabetic patients overall, but there was a significant increase in the subset of patients with elevated cholesterol from 46% to 56% (p=0.046) with no significant change in the control group (from 47% to 50%, p=0.25). Methods. A sequential, mixed method study was conducted among health care providers in Oji-Cree reserves in Sioux Lookout Zone, Ontario. A range of primary care providers participated in in-depth interviews and focus groups to explore barriers to diabetes care. These results informed a controlled before-after intervention trial. Two communities were randomized to receive an interactive education workshop intervention and audit and feedback identifying diabetic patients with elevated cholesterol, while the other 2 communities had usual care. The primary outcome measure was statin prescription rate and the secondary outcome was statin prescription in patients with elevated cholesterol. Outcomes were assessed by chart review at baseline and 10 months post-intervention. Providers in the intervention group were interviewed to assess the acceptability, feasibility and degree of implementation of the intervention.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".