LET’S TALK FOOD: ELEMENTARY SCHOOL STUDENTS’ PERCEPTIONS OF SCHOOL AND HOME FOOD ENVIRONMENT AND THE IMPACT OF THE HARVEST OF THE MONTH PROGRAM ON THEIR DIETARY ATTITUDES AND BEHAVIORS
Bibliographic record
Abstract
This study aimed to further knowledge about elementary school students’ views on food environment, and the effects of the Harvest of the Month (HOTM) program on their dietary attitudes and behaviors. Three focus groups were conducted with a total of 24 fourth, fifth, and sixth grade students from low-income schools in northern California who received the National School Lunch Program and HOTM during the school year. Focus groups were tape-recorded, transcribed, and coded for specific themes. Following the intervention, participants expressed a desire for more healthy food options in the school cafeteria and wanted to receive more school and family support for healthy eating. The HOTM program created a positive environment that appeared to influence their dietary attitudes and behaviors, peer and family perceptions of healthy eating, and participants’ attitudes toward their schools. Specifically, cooking demonstrations, tasting activities, and take-home recipes provided them with a means to share with their parents what they had learned about fruits and vegetables. School food policy interventions may become more effective if they are combined with interventions based on nutrition education. Future research should focus on exploring effective and synergistic ways of implementing both types of interventions among children.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".