History of trauma and relationship with alexithymia, temperament and character dimensions in male alcohol dependent inpatients
Bibliographic record
Abstract
Objective: To evaluate the relationship of trauma history with alexithymia and personality dimensions in male alcohol dependent inpatients. Method: Participants were 156 consecutively admitted male alcohol dependents. Patients were investigated with the Toronto Alexithymia Scale (TAS-20), the Temperament and Character Inventory (TCI) and the Traumatic Experiences Checklist (TEC). Results: Among alcohol dependent inpatients, 49.4% (n=77) were considered as the group having trauma history according to definition of DSM-IV, although this rate was 92% according to TEC. Rate of being single and alexithymic were higher, whereas age for regular alcohol use was lower in traumatic group. Current age, employment status, and duration of education did not differ between groups. Mean scores of difficulty in identifying feelings, alexithymia total score and personality dimensions of novelty seeking, harm avoidance and self-transcendence were higher in traumatic group whereas self-directedness (S) and cooperativeness (C) were lower in this group. Lower S score was determinant for trauma history (according to the DSM-IV) in Forward Logistic Regression model, whereas lower C was determinant for number of trauma types (according to the TEC) in Stepwise Linear Regression model. Conclusions: Alexithymia, temperament and character may all be related with trauma history in male alcohol dependent inpatients. Finding low S score which predicts the presence of trauma history and low C score which predicts the severity of trauma suggest that lifetime trauma experience is related with higher probability of negative personality characteristics, although causal relationship is not clear.
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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 | 0.000 |
| 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".