Dairy Foods Intake among Female Iranian Students: A Nutrition Education Intervention Using a Health Promotion Model
Bibliographic record
Abstract
INTRODUCTION: This aim of this study was to increase dairy consumption in students following an education intervention based on Pender's Health Promotion Model (Pender's HPM) variables. METHODS: The study was done during September 2014-April 2015 in Savojbolagh, Alborz, Iran. The study sample included 142 middle-school female students who were allocated to either the intervention (n=71) or the comparison group (n=71). Pender's HPM variables and the daily servings of dairy foods consumed were measured in both groups by a self-administered questionnaire and a 3 d record before the intervention and 4 weeks later. The 4-week intervention was conducted for the intervention group. The data was analyzed through analysis of covariance and paired t tests. RESULTS: Compared to the comparison group, there were significant differences in Pender's HPM variables (except for the negative feelings, perceived barriers and competing demands), the daily servings of dairy foods consumed, and intakes of Calcium, riboflavin, and vitamin A in the intervention participants following the conducted intervention program. CONCLUSION: Developing theory-driven nutrition education programs may increase student's dairy foods intake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".