Alexithymia, Depressive Experiences, and Dependency in Addictive Disorders
Bibliographic record
Abstract
Alexithymia, depressive feelings, and dependency are interrelated dimensions that are considered potential "risk factors" for addictive disorders. The aim of this study was to investigate the relationships between these dimensions and to define a comprehensive model of addiction in a large sample of addicted subjects, whether affected by an eating disorder or presenting an alcohol- or a drug use-related disorder. The participants in this study were gathered from a multicenter collaborative study on addictive behaviors conducted in several psychiatric departments in France, Switzerland, and Belgium between January 1995 and March 1999. The clinical sample was composed of 564 patients (149 anorexics, 84 bulimics, 208 alcoholics, 123 drug addicts) of both genders with a mean age of 27.3 +/- 8 years. A path analysis was conducted on the 564 dependent patients and 518 matched controls using the scores of the Toronto Alexithymia Scale, the Depressive Experiences Questionnaire, and the Interpersonal Dependency Inventory. Statistical analyses showed good adjustment (Goodness of Fit Index = 0.977) between the observable data and the assumed model, thus supporting the hypothesis that a depressive dimension, whether anaclitic or self-critical, can facilitate the development of dependency in vulnerable alexithymic subjects. This result has interesting clinical implications because identifying specific patterns of relationships leading from alexithymia to dependency can provide clues to the development of targeted strategies for at-risk subjects.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".