Exploring changes in alexithymia throughout intensive dialectical behavior therapy for eating disorders
Bibliographic record
Abstract
OBJECTIVE: Alexithymia is proposed as a prominent clinical feature of eating disorders (EDs). However, despite theoretical reason to believe that alexithymia could interfere with the success of treatments, few studies have tested whether alexithymia changes over the course of treatment. The goals of the current study were to evaluate (a) changes in alexithymia over the course of intensive Dialectical Behaviour Therapy (DBT) for EDs, and (b) associations between alexithymia and ED symptoms over time. METHOD: A mixed-diagnostic group of patients with EDs (N = 894) completed the Eating Disorders Examination-Questionnaire (EDE-Q) and the Toronto Alexithymia Scale (TAS-20) throughout intensive treatment and at various lengths of follow-up (6, 12, 24 months). RESULTS: Results suggested that even after controlling for relevant covariates, there were significant decreases in alexithymia from intake to discharge and discharge to follow-up. Models exploring changes in self-reported ED symptoms indicated that TAS-20 scores significantly related to ED symptoms across timepoints, such that greater alexithymia was associated with greater severity of symptoms. CONCLUSIONS: Altogether, findings support an association between alexithymia and ED symptoms over treatment and suggest that emotion-focussed therapies like DBT may result in decreases in alexithymia. Future research should explore whether this effect is consistent across therapies without an emotional focus.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".