Alexithymia and temperament and character model of personality in alcohol‐dependent Turkish men
Bibliographic record
Abstract
AIMS: Alexithymia, a personality trait characterized as having problems identifying, describing, and working with one's own feelings, often marked by a lack of understanding of the feelings of others, is only partly described within the context of personality. The aim of the present study was therefore to study the prevalence of alexithymia among male alcohol-dependent inpatients and investigate the relationship between alexithymia and the dimensions of Cloninger's psychobiological model of personality. METHODS: The Turkish version of the Toronto Alexithymia Scale (TAS-20) and the Turkish version of the Temperament and Character Inventory (TCI) were administered to 111 male alcohol-dependent inpatients. RESULTS: TAS-20 scores correlated positively with harm avoidance and self-transcendence and negatively with self-directedness and cooperativeness. Regression analysis identified high harm avoidance and self-transcendence and low self-directedness as independent predictors of alexithymia. Also harm avoidance and self-transcendence predicted alexithymia in a logistic regression model. CONCLUSIONS: Alexithymia can be explained by specific dimensions within Cloninger's psychobiological model of personality in alcohol-dependent Turkish men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".