Evaluation of an After-School Obesity Prevention Program for Children
Bibliographic record
Abstract
Dissemination of obesity prevention programs in different settings is needed. Moreover, new outreach tools to teach parents healthy eating and exercise lessons provided in these programs are important to develop. The pilot studies presented in this paper examined the implementation of the Children’s Healthy Eating and Exercise Program in two different after school programs in 2015 and 2016. Participants were elementary school-age children and their parents. Eight lessons were presented at each school. Child perceptions of healthy eating and exercise goals were examined as well as child knowledge retention and perceptions of behavior change. Parent perceptions of the program were analyzed. Results indicated that children reported improved knowledge and behaviors. Parents reported satisfaction with the program, but remained hard to reach. Children recalled key components of the healthy eating lessons at long-term follow-up assessments. In the second pilot study, children served as health coaches for teaching parents about family goals. Children believed they were successful at coaching parents, but they requested help in developing family eating and exercise goals. Improving outreach to parents and involving siblings remains a goal for future studies as does beginning to examine changes in eating and physical activity using food diaries and accelerometry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".