Adding More Plant-Forward Dishes to Menus: Findings from a Survey of Post-Secondary Campus Food Services Across Canada
Bibliographic record
Abstract
Post-secondary campus food services are one of the best contexts for trying new ideas for promoting plant-forward foods for health and sustainability. Two main approaches can be discerned in the literature: increasing offerings of meat-free plant-based dishes and either fully or partially substituting plant-based ingredients for meat in existing meat-based dishes. It is now timely to determine what proportion of menu offerings offered in post-secondary institutions across Canada are plant-forward dishes. Post-secondary campus food services are promising contexts for promoting plant-forward diets, yet little information exists on adoption across Canada. The extent of adoption, facilitators and barriers to implementation, and characteristics of plant-forward entrées were identified by food service representatives. The online survey included variables from the inner and outer setting and implementation process domains of Damschroder et al.’s (2022) Consolidated Framework for Implementation Research. Thirty-four Canadian university and college food service units participated. The percentage of plant-forward to overall entrée offerings varied widely, with a median of 17% in 2018-2020, doubling to 35% in 2023-2024. When the sample was divided into three subgroups: no-meal-plan group (n=8), late adopters (adopted 1-4 years ago: n=13) and early adopters (adopted 5+ years ago: n=12), the percentage of offerings had significantly risen among early and late adopters despite remaining low for the no-meal-plan group. While meat-free entrées were commonly available across all the groups, a larger number of variants with partial meat substitution were available among early adopters compared to the other groups. Late adopters reported significantly more barriers to implementation than the other two groups on some measures while the no-meal plan group reported the lack of infrastructure compared to other groups.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".