School Nutrition Stakeholders Find Utility in MealSim: An Agent-Based Model
Bibliographic record
Abstract
OBJECTIVE: To obtain feedback from school nutrition stakeholders on an agent-based model simulating school lunch to inform model refinement and future applications. DESIGN: Qualitative study using online discussion groups. SETTING: School nutrition professional stakeholders across the US. PARTICIPANTS: Twenty-eight school nutrition stakeholders. PHENOMENON OF INTEREST: Perceptions and applicability of MealSim for school nutrition stakeholders to help reduce food waste. ANALYSIS: Deductive approach followed by inductive analysis of discussion group transcripts. RESULTS: Stakeholders appreciated the customizability of the cafeteria characteristics and suggested adding additional characteristics to best represent the school meal system, such as factors relating to school staff supervision of students during meals. The perceived utility of MealSim was high and included using it to train personnel and to advocate for policy and budgetary changes. However, they viewed MealSim as more representative of elementary than high schools. Stakeholders also provided suggestions for training school nutrition administrators on how to use MealSim and requested opportunities for technical assistance. CONCLUSIONS AND IMPLICATIONS: Although agent-based models were new to the school nutrition stakeholders, MealSim was viewed as a useful tool. Application of these findings will allow the model to meet the intended audience's needs and better estimate the system.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".