Alexithymia and Personality Disorder Functioning Styles in Paranoid Schizophrenia
Bibliographic record
Abstract
OBJECTIVES: Personality disorder functioning styles might contribute to the inconclusive findings about alexithymic features in schizophrenia. We therefore studied the relationship between alexithymia and personality styles in paranoid schizophrenia. METHODS: We administered the Chinese versions of the Toronto Alexithymia Scale (TAS-20), the Parker Personality Measure (PERM), the Positive and Negative Syndrome Scale as well as the Hamilton Anxiety and Depression Scales to 60 paranoid schizophrenia patients and 60 healthy control subjects. RESULTS: Patients scored significantly higher on the Positive and Negative Syndrome Scale, TAS 'difficulty identifying feelings' and 'difficulty describing feelings', Hamilton Depression Scale and most PERM scales. In healthy subjects, difficulty identifying feelings predicted the PERM 'dependent' style, and the Hamilton Anxiety Scale predicted difficulty identifying feelings and difficulty describing feelings. In patients, difficulty identifying feelings nonspecifically predicted all the PERM scales; by contrast, the PERM 'antisocial' style predicted difficulty identifying feelings, the 'avoidant' style predicted difficulty describing feelings, and the 'histrionic' and 'paranoid (-)' styles predicted 'externally oriented thinking'. CONCLUSIONS: Personality disorder functioning styles - instead of anxiety, depression, psychotic symptoms or disease duration - were specifically associated with alexithymia scales in our patients, which sheds light on a cognitive-personological substrate in paranoid schizophrenia on the one hand, and calls for a longitudinal design to discover how premorbid or postacute residual personality styles contribute to the sluggish disorder on the other.
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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 | 0.001 | 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) | 0.002 | 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".