From "best practice" to "next practice": the effectiveness of school-based health promotion in improving healthy eating and physical activity and preventing childhood obesity
Bibliographic record
Abstract
BACKGROUND: In 2005, we reported on the success of Comprehensive School Health (CSH) in improving diets, activity levels, and body weights. The successful program was recognized as a "best practice" and has inspired the development of the Alberta Project Promoting active Living and healthy Eating (APPLE) Schools. The project includes 10 schools, most of which are located in socioeconomically disadvantaged areas. The present study examines the effectiveness of a CSH program adopted from a "best practice" example in another setting by evaluating temporal changes in diets, activity levels and body weight. METHODS: In 2008 and 2010, we surveyed grade 5 students from approximately 150 randomly selected schools from the Canadian province of Alberta and students from 10 APPLE Schools. Students completed the Harvard Youth/Adolescent Food Frequency Questionnaire, questions on physical activity, and had their height and weight measured. Multilevel regression methods were used to analyze changes in diets, activity levels, and body weight between 2008 and 2010. RESULTS: In 2010 relative to 2008, students attending APPLE Schools were eating more fruits and vegetables, consuming fewer calories, were more physically active and were less likely obese. These changes contrasted changes observed among students elsewhere in the province. CONCLUSIONS: These findings provide evidence on the effectiveness of CSH in improving health behaviors. They show that an example of "best practice" may lead to success in another setting. Herewith the study provides the evidence that investments for broader program implementation based on "best practice" are justified.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".