An examination of the effect of exposure to calories on menus on body-related self-conscious emotions: Continuing the investigation beyond body dissatisfaction
Bibliographic record
Abstract
Mandatory calorie labelling on restaurant menus has been implemented in several Western countries. The purpose of the present study was to examine direct and indirect effects of the exposure to calorie information on menus on body-related shame, guilt, and hubristic pride. Self-compassion was examined as a moderator, and self-objectification was examined as a mediator. Men and women (N = 359, mean age = 42 years) were recruited on Prolific for a study examining "self-perceptions and menu choices". Participants were randomly assigned to view a menu with or without calorie information and select a hypothetical meal. Exposure to calories on menus statistically significantly predicted higher body-related shame, but not body-related guilt or body-related hubristic pride. Self-objectification did not mediate the relationship between exposure to calories on menus and body-related shame, guilt, or hubristic pride. Self-compassion moderated the relationship between exposure to calories on menus and body-related shame. Overall, individuals who scored low on self-compassion experienced higher body-related shame after exposure to a restaurant menu with calories. These findings outline potential harmful effects of mandatory calorie labelling that should be taken into account in the mandating and evaluation of such policies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".