Bibliographic record
Abstract
AIM: This observational study was aimed to analyze the prevalence of alexithymia and dissociation in eating disorders, to investigate the existence and the extent of a possible correlation between the two constructs, and their relationship with Eating Disorders (ED) symptoms and traits. METHOD: The sample was composed of ED patients (n=30) AN (N=19), BN (N=6) and BED (N=5) recruited from a residential care clinic for eating disorders. The psychometrics level were measured with self-report questionnaires. Alexithymia and Dissociation were assessed with the Rome Alexithymia Scale (SAR – Scala Alessitimica Romana) and the Dissociative Experiences Scale-II (DES-II), respectively. Data related to disordered eating psychopathology was collected using the Eating Disorders Inventory-3 (EDI-3) and the Body Uneasiness Test (BUT). RESULTS: The majority of the subjects was alexithymic (66.67%) and reported to have moderately frequent to very frequent dissociative experiences (63.34%). Alexithymia and dissociation showed significant correlation (p=<0,05). Difficulties in empathizing and in identifying, expressing and communicating emotions were also associated to emotional dysregulation, interoceptive deficits, risk of developing an eating disorder, and body image-related discomfort. Possible interpretations for these results are discussed. DISCUSSION AND CONCLUSION: The presence and prevalence of both alexithymia and dissociative state in our sample, and the relationship between the two constructs suggest that they might share a common origin; this study’s findings highlight the importance of specific assessment and selective treatment for alexithymia when dealing with patients with ED.
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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 | 0.001 | 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".