Development and Preliminary Validation of a Self‐Report Measure of Sensory and Moral Disgust Toward Perceived Unhealthy Food
Bibliographic record
Abstract
BACKGROUND: Disgust toward perceived unhealthy food has been largely overlooked in understanding the development and persistence of disordered eating, partly due to a lack of measurement tools. To address this gap, we developed new measures of disgust toward perceived unhealthy food. METHOD: We evaluated the psychometric properties of a new self-report tool for measuring disgust toward perceived unhealthy food. Participants in Sample 1 (undergraduate students; n = 352) and Sample 2 (community members; n = 549) were either actively following a healthy eating plan or believed they were maintaining a healthy lifestyle. They completed questionnaires on disgust toward perceived unhealthy food, eating disorder symptoms, and psychopathology. To assess convergent and discriminant validity, in Sample 2, participants also completed trait measures of food-related, moral, sexual, and pathogen disgust, as well as self-disgust. RESULTS: Exploratory structural equation modeling revealed four types of disgust across both samples: disgust for the sensory aspects of unhealthy food, disgust toward people who eat unhealthy food, disgust for unhealthy food due to health risks, and disgust toward unhealthy food promotion. All types, except for promotion-related disgust, were linked to eating disorder symptoms and psychopathology above and beyond trait food disgust sensitivity. Sensory disgust for unhealthy food was associated with other disgust measures, excluding moral disgust sensitivity. Health-risk and promotion-related disgust were linked to other disgust measures but not self-disgust. CONCLUSION: Findings extend knowledge on the types of disgust toward perceived unhealthy food in relation to eating disorder symptoms and psychopathology. Results are discussed in terms of future validation research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".