The role of anxiety and alexithymia in predicting disordered eating behaviors among students:implication for health promotion
Bibliographic record
Abstract
Background and objective: Research results show that undesirable emotion are related to disordered eating behaviors and when eating is in response to negative emotions, it is more likely that a person loses his health. The aim of this study was to investigate the role of anxiety and alexithymia in predicting disordered eating behaviors among student. Materials and Methods: This cross-sectional study on 477 student of Allameh Tabataba’i University who were selected by multiple cluster sampling, was conducted. Participants responded to the questionnaires of demographic characteristics, anxiety of Costello and Comrey (1967), Twenty-item Toronto Alexithymia and disordered eating behaviors of Garner and colleagues (1982). Data were analyzed using Pearson correlation coefficient and stepwise regression. Results: Results showed that there was significant internal correlation among anxiety, alexithymia and disordered eating behaviors (p<0.01). Also, stepwise regression analysis indicated that anxiety and alexithymia significantly predicted, respectively, 40% and 29% of the variance of disordered eating behaviors (p<0.01). Conclusion: This study results suggest the importance of anxiety and alexithymia in predicting disordered eating behaviors and these factors can explain the high degree of variability of this behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".