Preferences for vegetables among university foodservice users
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the meal selection and potential vegetable substitution preferences in a sample of university students, to inform design of planned nudge interventions for increasing vegetable intake in on-campus cafeterias. The setting was a public university in southern Ontario, Canada. Design/methodology/approach An online survey was disseminated via multiple channels, and 686 undergraduate students responded. The frequency of purchasing specific meals on campus was queried first to set context, and then preferences for meal types (wraps, pasta, etc.), followed by preferences for vegetables to be added within meal types. Findings For portable meal options such as sandwiches, pitas and wraps, preferred vegetables for modification were cucumbers, spinach, tomatoes and bell peppers, and having vegetable toppings and raw cauliflower or broccoli as sides with pizza. For burgers or hotdogs, preferred sides were garden salad, cucumber slices and carrot sticks. Broccoli was the most preferred vegetable addition and substitution for sit-down meals, such as meals of chicken, beef, pork or fish with a side of potatoes or rice. Practical implications The findings can be used to design nudge interventions in university cafeterias by incorporating preferred vegetables into composite meals frequently purchased by students. Originality/value Few nudge studies to date have incorporated more vegetables into existing composite meals and offering them as the new default. Stated preferences are a reasonable starting point for the design of such interventions.
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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 | 0.003 |
| 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 | 0.000 |
| 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".