Alexithymia in Patients With Substance Use Disorders and Its Relationship With Psychiatric Comorbidities and Health-Related Quality of Life
Bibliographic record
Abstract
Background: Alexithymia frequently correlates with several psychiatric disorders, including substance use disorder (SUD). However, most studies reporting the associations between alexithymia and psychiatric disorders have been performed in populations without SUD. This research, therefore, evaluates alexithymia in Spanish patients with SUD and the relationship among alexithymia, psychiatric comorbidities, psychological symptoms/traits, SUD variables, and health-related quality of life (HRQoL). Methodology: A cross-sectional study was conducted with 126 Spanish outpatients with SUD (75.4% males; mean age 43.72 ± 14.61 years), correlating their alexithymia levels (using the Toronto Alexithymia Scale 20 [TAS-20]) to their psychiatric comorbidities, psychological symptoms/traits, SUD variables, and HRQoL. Results: Alexithymia was significantly higher in patients who had cannabis use disorder. Higher alexithymia scores were also related to higher levels of depression, anxiety, impulsivity, and lower HRQoL. After multivariate analysis, trait anxiety, impulsivity, and the physical component summary of the HRQoL were found to be independently related to alexithymia. Conclusions: SUD patients with higher alexithymia levels have more frequently psychiatric comorbidities, present specific psychological features, and have worse HRQoL. Hence, it is important to evaluate these factors and offer more accurate psychotherapeutic approaches for this patient population.
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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 | 0.000 |
| 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".