The Mediator Role of Emotion Regulation Difficulties in Relationship between Alexithymia and Disordered Eating Behaviors among Students Allameh Tabataba’i University, Iran.
Bibliographic record
Abstract
Abstract Background and Objectives: Emotion regulation difficulties predicts disordered eating, but how emotion regulation difficulties lead to disordered eating remains an unanswered question. In this research, the role of alexithymia and emotion regulation difficulties was investigated in the prediction of disordered eating behaviors among students. Methods: This descriptive study was performed on 264 students of Allameh Tabataba’i University, who were selected by multiple cluster sampling. Data were collected using Toronto Alexithymia Scale, Eating Attitudes Test, and Emotion Regulation Questionnaire. Analysis of data was performed using Pearson correlation coefficient and stepwise regression. The significance level was considered to be p<0.01. Results: Results showed that there was a significant correlation among emotion regulation difficulties, alexithymia, and disordered eating behaviors (p<0.01). The results of stepwise regression analysis indicated that alexithymia and emotion regulation difficulties significantly predicted disoredered eating behaviors (p<0.01) and emotion regulation difficulties has a mediator role in the relationship between alexithymia and disordered eating behaviors (p<0.01). Conclusion: The results of this study is indicative of the importance of emotion regulation difficulties and alexithymia in the prediction of disordered eating behaviors, and these factors can explain the high rate of disordered eating behaviors variance.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".