Childhood Abuse and Neglect as a Risk Factor for Alexithymia in Adult Male Substance Dependent Inpatients
Bibliographic record
Abstract
The prevalence of childhood abuse and neglect (CAN) histories and their associations with alexithymia among male substance-dependent inpatients were studied. Participants were 159 consecutively admitted male substance dependents (115 alcohol and 44 other drugs). Substance dependence was diagnosed by means of the Structured Clinical Interview for DSM-IV (SCID-I), Turkish version. Patients were investigated with the Toronto Alexithymia Scale (TAS-20) and Childhood Abuse and Neglect Questionnaire. Among substance-dependent patients, 57.0% had at least one type of CAN and 45.3% were considered as alexithymic since they had a score greater than 60 on the TAS-20. Rate of unemployment, low educational status, emotional abuse and history of suicide attempts were higher in alexithymic substance dependent patients. Those who had histories of two or more types of childhood abuse or neglect had also higher mean score on TAS-20, particularly on the item "difficulty in identifying feelings-DIF." Also, the number of childhood trauma types was positively correlated with TAS-20 and DIF and the "difficulty in describing feelings-DDF" items of TAS-20. History of childhood emotional abuse was the only determinant for alexithymia. Childhood emotional abuse might be a risk factor for alexithymia among inpatient substance dependents.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".