The relationship between alexithymia, shame, trauma, and body image disorders: investigation over a large clinical sample
Bibliographic record
Abstract
BACKGROUND: The connections between eating disorders (EDs) and alexithymia have not been fully clarified. This study aims to define alexithymia's connections with shame, trauma, dissociation, and body image disorders. METHODS: We administered the Dissociative Experience Scale-II, Trauma Symptom Inventory, Experience of Shame Scale, Toronto Alexithymia Scale-20, and Body Uneasiness Test questionnaires to 143 ED subjects. Extensive statistical analyses were performed. RESULTS: The subjects showed higher scores on alexithymia, shame, dissociation, and traumatic feelings scales than the nonclinical population. These aspects are linked with each other in a statistically significant way. Partial correlations highlighted that feelings of shame are correlated to body dissatisfaction, irrespective of trauma or depressed mood. Multiple regression analysis demonstrates that shame (anorexic patients) and perceived traumatic conditions (bulimic and ED not otherwise specified) are associated with adverse image disorders. CONCLUSION: Shame seems to hold a central role in the perception of an adverse self-image. Alexithymia may be interpreted as being a consequence of previous unelaborated traumatic experiences and feelings of shame, and it could therefore be conceptualized as a maladaptive-reactive construct.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".