Scale-up of the Kerala Diabetes Prevention Program (K-DPP) in Kerala, India: implementation evaluation findings
Bibliographic record
Abstract
The cluster-randomized controlled trial of the Kerala Diabetes Prevention Program (K-DPP) demonstrated some significant improvements in cardiometabolic risk factors and other outcomes. We aimed to refine and improve K-DPP for wider implementation in the Kerala state of India. The specific objectives of the scale-up program were (a) to develop a scalable program delivery model and related capacity building in Kerala and (b) to achieve significant improvements in cardiometabolic risk factors in the target population. A total of 118 key trainers of a large women's organization trained 15,000 peer leaders in three districts of Kerala. Each of these peer leaders was required to deliver 12 monthly sessions to ~25 people, reaching an estimated total of 375,000 adults over 12 months. We evaluated the number of sessions conducted, the participation of men, and program reach. We also assessed the effectiveness of the program in a random sample of 1,200 adults before and after the intervention and performed a biochemical evaluation on a subsample of 321. Of the 15,222 peer leaders who were trained, 1,475 (9.7%) returned their evaluation forms, of which, 98% reported conducting at least 1 session, 88% ≥6 sessions, and 74% all 12 sessions. Tobacco use among men reduced from 30% to 25% (p = .02) and alcohol use from 40% to 32% (p = .001). Overall, mean waist circumference reduced from 89.5 to 87.5 cm (p < .001). Although there were some study shortcomings, the approach to scale-up and its implementation was quite effective in reaching a large population in Kerala and there were also some significant improvements in key cardiometabolic risk factors following the 1 year 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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".