Small prizes increased healthful school lunch selection in a Midwestern school district
Bibliographic record
Abstract
As obesity has become a pressing health issue for American children, greater attention has been focused on how schools can be used to improve how students eat. Previously, we piloted the use of small prizes in an elementary school cafeteria to improve healthful food selection. We hoped to increase healthful food selection in all the elementary schools of a small school district participating in the United States Department of Agriculture Lunch Program by offering prizes to children who selected a Power Plate (PP), which consisted of an entrée with whole grains, a fruit, a vegetable, and plain low-fat milk. In this study, the PP program was introduced to 3 schools sequentially over an academic year. During the kickoff week, green, smiley-faced emoticons were placed by preferred foods, and children were given a prize daily if they chose a PP on that day. After the first week, students were given a sticker or temporary tattoo 2 days a week if they selected a PP. Combining data from the 3 schools in the program, students increased PP selection from 4.5% at baseline to 49.4% (p < 0.0001) during an intervention period of 2.5 school weeks. The school with the longest intervention period, 6 months, showed a PP selection increase of from 3.9% to 26.4% (p < 0.0001). In conclusion, giving small prizes as rewards dramatically improves short-term healthful food selection in elementary school 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".