Development of an integrated diabetes prevention program with First Nations in Canada
Bibliographic record
Abstract
Type 2 diabetes mellitus is a major cause of morbidity and mortality among First Nations in Canada. We used multiple research methods to develop an integrated multi-institutional diabetes prevention program based on the successful Sandy Lake Health and Diabetes Project and Apache Healthy Stores programs. In-depth interviews, a structured survey, demonstration and feedback sessions, group activities, and meetings with key stakeholders were used to generate knowledge about the needs and resources for each community, and to obtain feedback on SLHDP interventions. First Nations communities were eager to address the increasing epidemic of diabetes. Educating children through a school prevention program was the most popular proposed intervention. Remote communities had poorer access to healthy foods and more on-reserve media and services than the smaller semi-remote reserves. While the reserves shared similar risk factors for diabetes, variations in health beliefs and attitudes and environmental conditions required tailoring of programs to each reserve. In addition, it was necessary to balance community input with proven health promotion strategies. This study demonstrates the importance of formative research in developing integrated health promotion programs for multiple communities based on previously evaluated studies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".