The Relationship between Dimensions of Alexithymia and Dissociation
Bibliographic record
Abstract
BACKGROUND: The study investigated the following hypotheses: (1) Alexithymia is significantly associated with dissociation. (2) Pathological versus nonpathological dissociation is associated with alexithymic traits. METHODS: Psychiatric in- and outpatients (n = 173) and nonclinical subjects (n = 38) were investigated with the Toronto Alexithymia Scale (TAS-20), the FDS (German version of Dissociation Experience Scale) and the Symptom Check List (SCL-90-R; GSI). Correlation analyses followed by MANOVA and logistic regression were performed. RESULTS: Significant correlations and partial correlations, controlling for GSI, were observed between dissociation and alexithymia. The MANOVA demonstrated significantly higher scores for the two TAS-20 dimensions 'difficulty identifying feelings' and 'difficulty expressing feelings' in the group with pathological dissociation. On the basis of the TAS-20 subscores, logistic regression analysis correctly classified 72.5% of the cases into the pathological and the nonpathological dissociation group. CONCLUSIONS: These results support our hypothesis that pathological traits of dissociation are highly associated with alexithymia. A model is discussed in which alexithymic characteristics may contribute to the development of pathological dissociation and stress-related disorders such as posttraumatic stress 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".