Latent Profile Analysis of Eating Disorders and Emotional Well-being in College Students
Bibliographic record
Abstract
Abstract Background: University students face a unique combination of academic pressures, transitional life stages, and potential social isolation, which can contribute to the development of unhealthy eating patterns and emotional problems. Objectives: To explore the association between quality of life, emotional problems, and eating disorders (ED) among university students, using Latent Profile Analysis (LPA) to identify distinct eating profiles. Methods: This cross-sectional exploratory study involved a total of 1,798 university students from Brazil and Canada (mean age= 21.2; women = 78.4%). The instruments used included the Addiction-like Eating Behaviour Scale (AEBS), Depression, Anxiety, and Stress Scale (DASS-21), WHOQOL-bref, Yale Food Addiction Scale 2.0 (YFAS), Difficulties in Emotion Regulation Scale (DERS), and Impulsive Behavior Scale (UPPS-P). Results: The LPA, conducted based on the AEBS raw scores due to the absence of diagnostic criteria, identified three eating behavior profiles: Profile 1 with lower scores in AEBS and YFAS, indicating a healthy eating pattern; Profile 2 with high scores, reflecting eating compulsion; and Profile 3 with intermediate characteristics. Profile 2, associated with higher levels of anxiety, depression, and stress, showed a significantly reduced quality of life. No differences were detected regarding nationality, gender, type of university, or housing. Conclusion: The results highlight the complex relationships between eating behavior, mental health, and quality of life in university students, underscoring the need for targeted interventions to improve their well-being. Thus, ED and their consequent impact on mental health and quality of life are increasingly recognized as critical issues among university students, a group uniquely vulnerable to such challenges due to transitional life stages and academic pressures. Keywords: Latent Profile Analysis, Eating Disorders, University Students, Mental Health, Cross-Sectional Study.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".