The role of alexithymia, impulsivity and emotional intelligence in predicting relapse of druge abuse disorder
Bibliographic record
Abstract
Background: Recurrence in substance abusers is high, but alexithymia, impulsivity and emotional intelligence can play a role in predicting it.Aims: The purpose of this study was to investigate the role of alexithymia, impulsivity and emotional intelligence in predicting recurrence of substance abuse disorder. Method: Descriptive-correlational research method and among those who referred to addiction treatment centers, 120 were selected by purposeful. For information gathering, we used the predicted scales Wright at al( 2001, Toronto Alexithymia Scale-20 (FTAS-20) (1994), Barratt Impulsiveness Scale-11 (BIS-11) (2004), and Bradbury and Graves' emotional intelligence(2005) . Data were analyzed using Pearson correlation and regression. Results: The results showed that there is a significant positive relationship between alexithymia, difficulty in identifying feelings, total score of impulsivity and the dimensions of motor impulsivity and cognitive impulsivity with relapse. There was a significant negative relationship between the impulsiveness of non-planning , emotional intelligence and dimensions with self-awareness, self-management, social awareness and relationship management of emotional intelligence components and recurrence of drug use (p<0/01). impulsivity in non-planning,impulsivity and management of relationships could explain the recurrence of drug use (p<0/01). Conclusion: So it can be concluded that with increasing alexithymia and general impulsivity, increased recurrence of drug abuse, and with the increase in emotional intelligence, decreased recurrence of drug abuse.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".