Alexithymia and the Temperament and Character Model of Personality
Bibliographic record
Abstract
OBJECTIVE: In our study we explored the associations between alexithymia (Toronto Alexithymia Scale 20, TAS-20) and the dimensions and subscales of Cloninger's theoretically based and empirically validated psychobiological model of personality to further clarify the relationship between alexithymia and personality traits. METHODS: Psychiatric in- and outpatients (n = 254) were investigated with the TAS-20, the Temperament and Character Inventory (TCI) and the Symptom Check List SCL-90-R to control for the severity of current psychopathology. Correlation and regression analyses were performed. RESULTS: The regression analysis identified the TCI dimensions low self-directedness (SD), low reward dependence (RD) and to a minor degree harm avoidance (HA) as independent predictors for alexithymia. At the level of subscales, interpersonal detachment (RD3), low resourcefulness (SD3), low responsibility and blaming (SD1) and shyness with strangers (HA3) were predictors for alexithymia. The degree of explained variance of the TAS-20 scores by the TCI dimensions and subscales ranged between 43 and 45% whereas the inclusion of the general severity index into the regression models accounted for an additional 5% of the variance. CONCLUSIONS: Alexithymia is best explained by a mixture across different dimensions and subscales within Cloninger's psychobiological model of personality. However, alexithymia is captured only partly by current concepts of personality, and additional contributing psychological and biological factors need to be identified to understand alexithymia more extensively.
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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 | 0.002 |
| 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 | 0.000 |
| 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".