Outcome Evaluation of a Pilot Study Using “Nudges”
Bibliographic record
Abstract
Background: Every school day, over 31 million U.S. children eat school lunches. Unfortunately, students often do not choose the healthy options in the school cafeteria. This paper describes outcome results of a pilot study using “nudges” to improve elementary school students’ fruits and vegetables selections. Methods: A pilot study was conducted from January to May 2012, in six intervention schools and 2 control schools. A behavioral economics-based intervention was conducted using “nudges” or cues from the cafeteria (staff encouragement to select fruit and vegetables, food labels, “Harvest of the Month” posters), school (morning announcement messages, prompts regarding cafeteria food selections), and parents (school newsletter articles, parent listserve messages) to promote students’ selection of fruits and vegetables in the school cafeteria. The serving data from the point-of-service machine provided fruits and vegetables served per student per day. Results: There were no significant differences in the number of servings of fruits and vegetables served per student per day, averaged over the study period. Process data revealed low implementation of the intervention components, which may partially explain results. Conclusions: Low implementation of nudges led to non-significant results in this pilot study. However, providing environmental cues are important and warrant further research with full implementation. Starting 2012, the new meal pattern includes two vegetables and a fruit serving for lunch; and two fruit servings for breakfast. Minimal cost interventions should be explored to facilitate successful implementation of new school meal guidelines.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".