Controlled Trial of a Multifaceted Intervention for Improving Quality of Care for Rural Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Despite good evidence and clinical practice guidelines, studies document that treatment of type 2 diabetes is less than optimal. Lack of resources or limited access may put patients in rural communities at particular risk for suboptimal care. RESEARCH DESIGN AND METHODS: We conducted a prospective, before/after study with concurrent controls to assess the effectiveness of a multidisciplinary diabetes outreach service (intervention) for improving the quality of care for rural patients with type 2 diabetes. Our intervention consisted of six monthly visits by a traveling team of specialist physicians, nurses, dieticians, and a pharmacist. The core of this service was specialist-to-rural primary care physician academic group detailing. Two comparable regions in Northern Alberta were randomly allocated to control or intervention. Data were collected before and 6 months after intervention in a representative volunteer sample. The primary outcome was a 10% improvement in any one of the following: blood pressure, total cholesterol, or HbA(1c). RESULTS: Our analysis included 200 intervention and 179 control subjects; 14 subjects were at all three primary outcome targets at baseline. The intervention was associated with a trend toward improvement in primary outcome at 6 months (44% intervention vs. 37% control; odds ratio 1.32, 95% CI 0.87-1.99). The intervention was associated with a significant improvement in blood pressure (42% intervention vs. 25% control, P = 0.004); however, there were only small, nonsignificant changes in cholesterol or HbA(1c). The intervention was associated with a significant increase in satisfaction with diabetes care. Multivariate adjustment for baseline differences between intervention and control subjects did not affect any of the main results. CONCLUSIONS: A diabetes outreach service has the potential to improve the quality of diabetes care for rural patients. Future studies need to involve longer timelines and larger sample sizes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".