Process evaluation of a multi-institutional community-based program for diabetes prevention among First Nations
Bibliographic record
Abstract
Epidemic rates of diabetes among Native North Americans demand novel solutions. Zhiiwaapenewin Akino'maagewin: Teaching to Prevent Diabetes was a community-based diabetes prevention program based in schools, food stores and health offices in seven First Nations in northwestern Ontario, Canada. Program interventions in these three institutions included implementation of Grades 3 and 4 healthy lifestyles curricula; stocking and labeling of healthier foods and healthy recipes cooking demonstrations and taste tests; and mass media efforts and community events held by health agencies. Qualitative and quantitative process data collected through surveys, logs and interviews assessed fidelity, dose, reach and context of the intervention to evaluate implementation and explain impact findings. School curricula implementation had moderate fidelity with 63% delivered as planned. Store activities had moderate fidelity: availability of all promoted foods was 70%, and appropriate shelf labels were posted 60% of the time. Cooking demonstrations were performed with 71% fidelity and high dose. A total of 156 posters were placed in community locations; radio, cable TV and newsletters were utilized. Interviews revealed that the program was culturally acceptable and relevant, and suggestions for improvement were made. These findings will be used to plan an expanded trial in several Native North American communities.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".