Psychopathological Features and Drop-Out Predictors in a Sample of Individuals with Substance Use Disorder Under Residential Community Treatment.
Bibliographic record
Abstract
OBJECTIVE: Several studies have been conducted to investigate the relationship between addiction and crimes, but little is known about the treatment of individuals with substance use disorder (SUD) with criminal records. This study aimed to assess the treatment progress of a group of individuals with SUD who underwent treatment within a residential community, and to analyze their personality profiles to identify drop-out predictors. METHOD: We evaluated 49 subjects using the Psychopathic Personality Inventory-Revised (PPI-R), the Psychological Treatment Inventory (PTI), the Barratt Impulsiveness Scale-11 (BIS-11), and the Toronto Alexithymia Scale (TAS-20) and carrying out various statistical analyses, including the t-test, Cohen's d, analysis of variance (ANOVA), and discriminant analysis. RESULTS: Results are discussed within the context of previous studies on this topic. Our results showed that variables such impulsiveness, cold-heartedness, alexithymia, and psychopathic traits influenced the premature treatment abandonment of individuals with SUD and criminal records. CONCLUSIONS: This study provides a further piece for the understanding of subjects with SUD and criminal records, suggesting the importance of a psychodynamic integrated approach, and showing the impact of some psychopathological features on treatment drop-out.
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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 | 0.001 | 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".