Campus food service users’ support for nudge strategies for fruit and vegetable-rich items: findings from a large Canadian national sample
Bibliographic record
Abstract
Abstract Although customer support is critical to the wider uptake of nudging strategies to promote fruits and vegetables (FV) in institutional food service (FS) settings, empirical research is sparse and typically based on small convenience samples. An online survey was conducted to assess support, perceived effectiveness and intrusiveness of nine nudge types drawn from Münscher et al.'s Taxonomy of Choice Architecture. We focused on the setting of campus FSs across Canada. A national sample of post-secondary students regularly using campus FSs was used (N 1057). Support for changing the range of options (B3) was the highest, closely followed by changing option-related effort (B2) and changing option-related consequences (B4). Facilitating commitment (C2), changing default (B1) and providing a social reference point (A3) received lowest support. Furthermore, we extracted three clusters of respondents based on perceived effectiveness and intrusiveness of nudge types. Characterised by a relatively low level of perceived effectiveness and moderately high level of intrusiveness, Cluster 1 (61⋅7 % of the sample) reported the lowest support for nudges. Cluster 2 (26⋅6 %), characterised by intermediate effectiveness and low intrusiveness of nudging, reported a high level of support for nudges. Lastly, Cluster 3 (11⋅7 %), characterised by high perceived effectiveness of as well as high perceived intrusiveness, reported the highest level of support for nudges. Findings confirm overall support for FV nudging, with significant differences across nudge types. Differences in customers’ acceptance and perception across nudge types offer campus FS operators initial priors in selecting nudges to promote FV.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".