The Effects of Liking Norms and Descriptive Norms on Vegetable Consumption: A Randomized Experiment
Bibliographic record
Abstract
There is evidence that social norm messages can be used to promote the selection of fruit and vegetables in low habitual consumers of these foods but it is unclear whether this effect is sustained over time. It is also unclear whether information about others' liking for a food (liking norm) could have the same effect. Using a 2 × 5 × 2 experimental design we investigated the effects of exposure to various messages on later intake from a food buffet and whether any effects were sustained 24 h after exposure in both low and high consumers of vegetables. There were three factors: delay (immediate food selection vs. food selection 24 h after exposure), message type (liking norm, descriptive norm, health message, vegetable variety condition, and neutral control message), and habitual consumption (low vs. high). The buffet consisted of three raw vegetables, three energy-dense foods, and two dips. For vegetables and non-vegetables there were no main effects of message type, nor any main effect of delay. There was a significant message × habitual vegetable consumption interaction for vegetable consumption; however, follow up tests did not yield any significant effects. Examining each food individually, there were no main effects of message type, nor any main effect of delay, for any of the foods; however, there was a message × habitual vegetable consumption interaction for broccoli. Consumption of broccoli in the health message and descriptive norm conditions did not differ from the control neutral condition. However, habitually low consumers of vegetables increased their consumption of broccoli in the vegetable variety and liking norm conditions relative to habitual low vegetable consumers in the neutral control condition (p < 0.05). Further, investigation of the effects of the liking norm and vegetable variety condition on vegetable intake is warranted. This trial is listed as NCT02618174 at clinicaltrials.gov.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".