Alexithymia, emotion regulation, psychological well-being, and internet addiction among individuals with substance dependence
Bibliographic record
Abstract
Background: Substance dependence (SuD) has emerged as a focal point of concern among healthcare professionals, particularly those specializing in mental health. Hence, exploring the psychological factors linked to SuD is crucial. Aim: To evaluate the role of alexithymia, emotion regulation strategies (ERS), psychological well-being (PSW), and internet addiction (IA) in individuals with SuD and those without SuD. Materials and Methods: The study employed a cross-sectional design, enrolling 75 male individuals diagnosed with SUD and 75 male participants without SuD from the general population, aged between 18 and 40 years, selected using a purposive sampling technique. The assessment tools utilized in this study included the Toronto Alexithymia Scale, Drug Abuse Screening Tool (DAST), Emotion Regulation Questionnaire, and Ryff's Psychological Well-Being Scale. Results: The findings indicated that alexithymia was found to be more prevalent in individuals with SuD in comparison to those without SuD. Those with SuD tended to utilize expressive suppression to regulate their emotions, while individuals without SuD tended to employ cognitive reappraisal as an ERS. PSW was lower in individuals with SuD as opposed to those without SuD. Individuals with SuD exhibited higher levels of IA compared to their non-SuD counterparts. Additionally, the results indicated that among all variables, Alexithymia significantly positively predicted the severity of SuD, which was measured using the DAST accounting for 70% of the variance in severity of SuD among individuals with SuD. Conclusions: The research emphasizes significant psychological distinctions between individuals with and without SuD, with alexithymia serving as a crucial predictor of the severity of dependence.
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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 | 0.000 | 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) | 0.001 | 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".