Customer support for nudge strategies to promote fruit and vegetable intake in a university food service
Bibliographic record
Abstract
BACKGROUND: Diverse nudges, also known as choice architectural techniques, have been found to increase fruit and vegetable (FV) selection in both lab and field studies. Such strategies are unlikely to be adopted in mass eating settings without clear evidence of customer support; confirmation in specific contexts is needed. Inspired by the Taxonomy of Choice Architecture, we assessed support for eight types of nudging to increase the choice of FV-rich foods in a university food service. We also explored whether and to what extent nudge support was associated with perceived effectiveness and intrusiveness. METHODS: An online survey was conducted with students who used on-campus cafeterias. Multiple recruitment methods were used. Participants were given 20 specific scenarios for increasing FV selection and asked about their personal support for each nudge, as well as perceived intrusiveness and effectiveness. General beliefs about healthy eating and nudging were also measured. Results were assessed by repeated measures ANOVA for the 8 nudge types. RESULTS: All nudge scenarios achieved overall favourable ratings, with significant differences among different types of nudging by the 298 respondents. Changing range of options (type B3) and changing option-related consequences (type B4) received the highest support, followed by changing option-related effort (type B2) and making information visible (type A2). Translating information (type A1), changing defaults (type B1) and providing reminders or facilitating commitment (type C) were less popular types of nudging. Providing social reference points (type A3) was least supported. Support for nudge types was positively associated with the belief that food services have a role in promoting healthy eating, perceived importance of FV intake, trustworthiness of the choice architect and female gender. Lastly, support for all types of nudges was positively predicted by perceived effectiveness of each nudge and negatively predicted by perceived intrusiveness above and beyond the contribution of general beliefs about healthy eating and nudging. CONCLUSIONS: Findings from the current study indicate significant differences in support for nudge techniques intended to increase FV selection among university cafeteria users. These findings offer practical implications for food service operators as well as public health researchers.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".