The effectiveness of diabetes education in rural clinical practice
Bibliographic record
Abstract
Objective: Type 2 Diabetes affects approximately 10% of the population in the United States. Diabetes is associated with acute and long-term complications are more severe. Studies are providing a correlation between better self-care actions and a reduction of undesired diabetes outcomes. The purpose of this study was to evaluate the implementation of a diabetes self-management education (DSME) program on glycemic control that was expected to improve staff knowledge and diabetes outcomes.Methods: This study conducted a quality improvement design. Providers and nursing staff in three primary care clinics were recruited. Diabetes Knowledge Test (DKT) and HbA1c were measured pre and post intervention.Results: Data from 15 staff participants were analyzed. The mean score for the pre-test was 81% while the mean score for the post-test was 87%. A paired t-test revealed t = 1.533, df = 3.998 and p = .160. The HbA1c percentage mean over 6 months decreased by 0.02% and subsequently in 3 months by 0.17%. The Friedman rank sum test was used to compare the differences, χ2(2) = 14.79, p < .001. Post-hoc analysis identified a statistical significance in the HbA1c from implementation to post implementation.Conclusions: There was an increase in the percent score in the provider and nursing staff knowledge after implementation of the DSME program. A decrease in percent change of the HbA1c was identified over the three- month implementation period. This study demonstrated that the implementation of a DSME program may contribute to improved glycemic control.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".