Teachers as Partners in the Prevention of Childhood Obesity
Bibliographic record
Abstract
This paper presents a community-school-higher education partnership approach to the prevention of childhood obesity. Public elementary school personnel, primarily teachers, participated in the design and delivery of a curriculum targeting primary caregivers of 8-9-year-old children. Theoretical framework and methodological approaches guided the development of a cognitive behavioral lifestyle intervention targeting childhood obesity prevention in the Commonwealth of the Northern Mariana Islands (CNMI), a U.S. commonwealth. This project demonstrated that in populations with health disparity, teachers can be a valuable and accessible resource for identifying key health issues of concern to communities and a vital partner in the development of parent and child interventions. Teachers also benefited by gaining knowledge and skills to facilitate student and parent learning and impact on personal and familial health. Successful community-school-higher education partnerships require consideration of local culture and community needs and resources. Moreover, within any community-school–higher education partnership it is essential that a time sensitive and culturally appropriate feedback loop be designed to ensure that programs are responsive to the needs and resources of all stakeholders, and that leaders and policymakers are highly engaged so they can make informed policy decisions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".