Orthorexia nervosa tendencies and risk of eating disorders among culinary arts students: A comparative study with dietetics students
Bibliographic record
Abstract
BACKGROUND: Students pursuing food-related academic fields such as dietetics have higher orthorexia nervosa (ON) tendencies and are at a greater risk of developing eating disorders (EDs). However, there is limited research available on ON tendencies and on the risk of EDs in the culinary arts field, which also revolves around food. The present study explored ON tendencies and the risk of EDs among culinary arts students and compared them with those of dietetics students. METHODS: A cross-sectional study was conducted in France, using the eating habits questionnaire (EHQ) to measure ON tendencies, and the SCOFF as well as the eating disorders examination questionnaire (EDEQ) to evaluate the risk of EDs and ED symptoms. The study also included questions about sports practice. In total, 267 students participated in the study, including 144 culinary arts students (51 women, 92 men and one other) and 123 dietetics ones (106 women, 15 men and two others). RESULTS: Culinary arts students exhibited similar means of EHQ scores as dietetics ones (32.99 vs. 33.34) but higher SCOFF scores (52.8% vs. 39.8%). In addition, a gender difference was evidenced: women in culinary arts showed higher EHQ, SCOFF and EDEQ scores than men in culinary arts. Linear regression models showed no effect of the type of academic field (culinary arts vs. dietetics) on the EDEQ score but revealed effects of gender, body mass index and hours of sports practice. CONCLUSIONS: The study emphasises the need to monitor culinary students and implement interventions to prevent EDs. It also suggests a correlation between food-related education and the risk of EDs, which requires further 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".