Alexithymia, Ego-Dystonicity, and Obsessive-Compulsive Symptoms: A Path Modeling Analysis
Bibliographic record
Abstract
AIMS: This cross-sectional study aimed to test the path relations between alexithymia, ego-dystonicity, anxiety, depression and obsessive-compulsive (OC) symptoms in obsessive-compulsive disorder (OCD) and healthy individuals. METHODS: Fifty-eight patients with OCD (mean age 35.5 years) and 54 healthy participants (mean age 33.5 years) completed an assessment via a structured clinical interview. All of them completed the Toronto Alexithymia Scale (TAS-20), the Vancouver Obsessive-Compulsive Inventory (VOCI), the Self-Consistency and Congruence Scale (SCCS), the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS). The data were analyzed using partial least squares structural equation modeling (PLS-SEM). RESULTS: In the OCD patients, alexithymia (a linear combination of difficulty identifying and describing emotions in the self) was associated with the OC symptoms either with or without the presence of ego-dystonicity (a profile of self-inconsistency and self-stereotype). In the heathy participants, alexithymia was associated with the OC symptoms only through ego-dystonic experiences. CONCLUSION: This study provides evidence that ego-dystonicity partially affects the association between alexithymia and obsessive-compulsions. Alexithymia and ego-dystonicity have a synergistic effect on the symptoms of OCD. Alexithymia in healthy participants associates to the OC symptoms only through ego-dystonicity. Targeting ego-dystonicity dimensions in psychotherapy would help improve the symptoms of OCD.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".