Alexithymia, Emotional Empathy, and Self-Regulation in Anorexia Nervosa
Bibliographic record
Abstract
BACKGROUND: Individuals with anorexia nervosa (AN) who are starved have poor awareness (alexithymia), reduced understanding of others' mental states (cognitive empathy), and difficulty regulating personal emotions (self-regulation). Despite its important role in social interaction, sympathy for others (emotional empathy) has not been measured in AN. Furthermore, it is unknown how restoring weight affects the relationship among alexithymia, empathy, and self-regulation in AN. METHODS: Women with AN were tested longitudinally during their starvation period (N = 26) and after weight was restored (N = 20) and compared with 16 age-matched healthy women. Alexithymia, empathy, and self-regulation were assessed with the Toronto Alexithymia Scale, the Interpersonal Reactivity Index, and items measuring self-regulation from the Minnesota Multiphasic Personality Inventory-2, respectively. RESULTS: Relative to comparison participants, individuals with AN during both starvation and weight restoration reported greater alexithymia and emotional empathy in one domain, personal distress (vicarious negative arousal to others' suffering). Among AN participants, personal distress was positively correlated with alexithymia and negatively correlated with self-regulation, when accounting for depression. CONCLUSIONS: High levels of alexithymia and personal distress may be persistent features of AN because they do not resolve upon weight restoration. Greater personal distress in AN may be a function of poor emotional awareness and regulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".