Alexithymia in patients with substance use disorders
Bibliographic record
Abstract
Introduction Alexithymia is considered as a deficit in emotion processing. It includes difficulty to identify and describe feelings as well as discriminate between feelings and physical sensations. Alexithymia may be a risk factor for substance use (SUD). Objectives The objective of this work is to identify the prevalence and correlates of alexithymia among patients with SUD. Methods This study concerns 40 subjects who were hospitalized in a rehabilitation center in Sfax. The subjects completed a form investigating sociodemographic and drug use characteristics. Alexithymia was assessed using the Toronto Alexithymia Scale TAS-20 a. The TAS-20 have three factors: difficulty in identifying feelings (F1), difficulty in describing feelings (F2), and externally oriented thinking (F3). Results The mean age of 30.86 ± 8.07 years. The mean score of alexithymia was 65.39 ± 9.65 (42→83). The scores of its dimensions were 25.3 ± 6.10 for F1, 17.16 ± 3.3 for F2 and 23.16 ± 3.18 for F3. The prevalence of alexithymia was 62.8% among addicts. High alexithymic patients did not differ from low or moderate alexithymic patients in terms of, employment, education or the type of substance. TAS-20 was correlated to socio-economic status (P = 0.002). No correlation was observed between age and alexithymia (total TAS-20) when measured as a continuous variable (P = 0.802). High alexithymic patients exhibited a higher preference for poly-substance use compared with no alexithymic patients (P = 0.05). Conclusion Findings suggest that alexithymia is frequent in SUD patients. It should be noted in clinical practice that many patients with SUD may have a reduced capacity to identify and describe feelings during detoxification. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 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) | 0.003 | 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".