Prediction of Eating Disorders on the Basis of Alexithymia’s Components
Bibliographic record
Abstract
Background and purpose: Eating disorders are groups of mental disorders caused by several factors that result in different problems for patients and their families. This study investigated the prediction of eating disorders on the basis of alexithymia's components. Material and Methods: The research population included 234 people attending Tehran's health houses, chosen through sampling method. For data collection, Ahwaz eating disorders scale and Toronto alexithymia scale were used. Data was analyzed using descriptive statistics and regression analysis in SPSS ver.21. Results: The results showed that alexithymia's components significantly predict anorexia nervosa, bulimia nervosa and total eating disorders scale. In anorexia nervosa and total score of eating disorders, the role of difficulty in identifying feeling and externally oriented thinking were significant, also in prediction of bulimia nervosa only difficulty identifying feelings had a significant role. Conclusion: The results of current study can be used for better understanding on characteristics of patients with eating disorder.
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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 | 0.003 |
| 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 | 0.000 | 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".