Post-Secondary Food Service Manager Perspectives on Fruit and Vegetable Nudging Strategies: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Changing the choice architecture in post-secondary food service contexts to "nudge" customers to choose more fruits and vegetables (FV) shows promise in intervention studies to date. If such approaches are to become more widely adopted, they must be feasible and acceptable to food service managers. Among possible early adopters, managers of food services in post-secondary education institutions may have unique insights on implementation of such approaches, as they have dual mandates to support student health and maintain profitability. OBJECTIVE: The goal of this exploratory study was to examine current knowledge, practice, facilitators, and barriers to uptake of nudge strategies promoting FV in a sample of post-secondary food service managers. METHODS: = 10 institutions), recruited from a national professional organization. One or more representatives from each institution completed the interview. Interviews were audio-recorded, transcribed, and underwent framework descriptive and interpretative content analysis in NVivo (QSR International). Münscher's Taxonomy of Choice Architecture and the Ottawa Model for Research Use guided development and analysis. RESULTS: Managers from 9 universities and 1 technical college participated. Local context, governance, and resources varied widely. Eight of 10 institutions used some form of FV nudging as part of their marketing and health promotion, most commonly to reduce the effort associated with choosing FV. Nudging strategies aimed at increasing the range and composition of FV offerings, providing a social reference (opinion leaders) for choosing FV, and changing consequences with loyalty cards were also common. Other nudging strategies were used infrequently. Cost, operational ease of implementation, and students' privacy and choices were critical issues in adoption. CONCLUSIONS: The results can inform development and testing of locally adapted nudge interventions. It is critical that managers be involved from the outset of any planned academic implementation study.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".