Results of a Rural School-Based Peer-Led Intervention for Youth: Goals for Health
Bibliographic record
Abstract
BACKGROUND: School-based interventions are critical for enhancing the health of youth. The Goals for Health (GFH) school-based project was a goal-setting and life-skills intervention conducted in rural areas to increase self-efficacy, knowledge, and positive behaviors related to healthy eating. The intervention was peer-led with high school students teaching health and life skills to sixth-grade students. The purpose of this study was to examine the impact of the GFH school-based program on healthy eating outcomes related to self-efficacy, attitudes, knowledge, and behavior, and to examine the impact of quality of program implementation on the above outcomes. METHODS: Twenty-three rural schools in Virginia (15) and New York (8) participated in the study. Twelve of the schools were intervention schools that received the 12-week GFH program. The remaining 11 were wait-list control schools. Sixth graders (n = 2120 baseline) from all schools were surveyed at 4 time points (preintervention, postintervention, 1 and 2 year follow-up). RESULTS: Results included significant change patterns across the 4 assessment points in the predicted direction for healthy eating-related self-efficacy and fat and fiber knowledge. No significant change patterns were found at follow-up for fat, fiber, or fruit and vegetable intake. Results also indicated differences across gender and ethnicity and significant findings related to quality of implementation. CONCLUSIONS: Future interventions need to provide opportunities to practice healthy living skills over an extended period of time, include components that focus on contextual change in the school and the family, and monitor program implementation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".