Two observational studies examining the effect of a social norm and a health message on the purchase of vegetables in student canteen settings
Bibliographic record
Abstract
There is some evidence from laboratory-based studies that descriptive social-norm messages are associated with increased consumption of vegetables, but evidence of their effectiveness in real-world settings is limited. In two observational field studies taking an ecological approach, a vegetable-related social norm (e.g. "Did you know that most students here choose to eat vegetables with their meal?"), and a health message (e.g. "Did you know that students who choose to eat vegetables have a lower risk of heart disease?") were displayed in two different student canteens. Purchases were observed during three stages: baseline, intervention (when the posters were displayed) and immediate post-intervention (when the posters had been removed). Study 1 (n = 7598) observed the purchase of meals containing a portion of vegetables and Study 2 (n = 4052) observed the purchase of side portions of vegetables. In Study 1, relative to baseline, the social-norms intervention was associated with an increase in purchases of vegetables (from 63% to 68% of meals; OR = 1.24, CI = 1.03-1.49), which was sustained post-intervention (67% of meals; OR = 0.96, CI = 0.80-1.15). There was no effect of the health message (75% of meals at baseline, and 74% during the intervention; OR = 0.98, CI = 0.83-1.15). In Study 2, relative to baseline, there was an effect of both the social norm (22.9% of meals at baseline, rising to 32.5% during the intervention; OR = 1.62, CI = 1.27-2.05) and health message (rising from 43.8% at baseline to 52.8%; OR = 0.59, CI = 0.46-0.75). The increase was not sustained post-intervention for the social norm intervention (22.1%; OR = 0.59, CI = 0.46-0.75), but was sustained for the health intervention (48.1%; OR = 0.83, CI = 0.67-1.02). These results support further testing of the effectiveness of such messages in encouraging healthier eating and indicate the need for larger-scale testing at multiple sites using a randomised-controlled design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".