Sharing a Vision of Hope for Diabetes Care and Prevention Among American Indian and Alaska Native Communities
Bibliographic record
Abstract
The National Diabetes Prevention Center (NDPC) is an emerging model for public health practice and partnership. It is rooted in a "promising practices" framework, one that looks at what works for community diabetes prevention, care, and treatment practices. Working with national and local partners to explore new approaches to diabetes prevention invites us to move beyond traditional models of community public health partnerships. Traditional community partnership models emphasized the technical assistance in research, surveillance, and program development that can be provided by partners from outside the community. While not diminishing the importance of these activities, the NDPC seeks to provide an environment for meaningful language and discourse that adequately honors the innovative and culturally rich approaches to diabetes prevention already being developed within many American Indian and Alaska Native communities, which have some of the highest rates of diabetes in the world. The NDPC strives to provide common ground for the emergent discussions around the power and practice of solid evaluation frameworks, new information technologies, capacity-building philosophies, health systems, and collaborative approaches.
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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 | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.027 | 0.034 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.005 | 0.011 |
| 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".