Generalizability and Persistence of a Multifaceted Intervention for Improving Quality of Care for Rural Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Most quality improvement efforts for type 2 diabetes have neglected cardiovascular risk factors and are limited by a lack of information about generalizability across settings or persistence of effect over time. RESEARCH DESIGN AND METHODS: We previously reported 6-month results of a controlled study of an intervention that improved cardiovascular risk factors for rural patients with type 2 diabetes. We subsequently provided the identical intervention to the control region after the main study was completed. The primary outcome was 10% improvement in systolic blood pressure, total cholesterol, or HbA(1c). We compared the previously reported 6-month effect of the original intervention with the effect of the crossed-over intervention to the former control region and remeasured outcomes in the original intervention region 12 months later. RESULTS: Our analysis included 200 original intervention and 181 crossed-over intervention subjects. The age of the population was 62.4 +/- 12.4 years (mean +/- SD), and 54.3% were women. A similar proportion of patients in the crossed-over intervention group achieved improvement in the primary composite outcome compared with the original intervention group (38 vs. 44%, respectively; P = 0.29). In adjusted analyses, we observed less improvement in blood pressure (adjusted odds ratio 0.40 [95% CI 0.17-0.75]) but greater improvements in total cholesterol (1.86 [0.93-3.7]) with the crossed-over intervention compared with the original intervention. We observed sustained improvements in total cholesterol and HbA(1c) levels in the original intervention group, whereas previous large gains in control of blood pressure diminished over time. CONCLUSIONS: We found that our intervention was generalizable across settings, and its effect persisted over time. Nevertheless, without ongoing intervention or reinforcement, we noted some loss of the original benefits that had accrued. Future translational work should incorporate interventions such as ours into ongoing systems of rural 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 | 0.033 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".