Impact of a Culturally Tailored Diabetes Education and Empowerment Program in a Mexican American Population Along the US/Mexico Border: A Pragmatic Study
Bibliographic record
Abstract
BACKGROUND: The study purpose was to deliver a diabetes education program under real world conditions and evaluate its effect on diabetes-related clinical, self-management and psychosocial outcomes among Mexican Americans residing along the US/Mexico border. METHODS: A pragmatic study was conducted among adult patients with diabetes in three primary care clinics located along the US/Mexico border. A bilingual culturally tailored diabetes education program incorporating hands-on participatory techniques was delivered in 4 - 8 weekly group sessions. Clinical, self-management and psychosocial outcomes were evaluated pre- and post-intervention with surveys and medical record review. RESULTS: A total of 209 participants were enrolled; mean age was 58.9 years (range 23 - 94, standard deviation: 11.2); 68.4% were female; 91.1% were Hispanic. Significant improvements were observed in glycated hemoglobin (-1.1%, P < 0.001, n = 79), total cholesterol (-17.2 mg/dL, P = 0.041, n = 63), glucose self-monitoring (+1.3 times a week, P = 0.021, n = 115), exercise less than once a week (-18.2%, P < 0.001, n = 129), nutritional behavior (+2.23, P < 0.001, n = 115), knowledge (+1. 83, P < 0.001, n = 141) and diabetes-related emotional distress (-7.32, P = 0.002, n = 111). Benefits were observed with attendance rates as low as 50%. CONCLUSION: A clinic-based culturally competent diabetes education/self-management program resulted in significant improvements in outcomes among Hispanic participants. Experimentally tested culturally appropriate interventions adapted for real world situations can benefit Mexican American diabetic patients even when attendance is imperfect.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".