Relationship of drug-addicted patients’ personality disorders to social problem-solving changes during the rehabilitation process
Bibliographic record
Abstract
BACKGROUND: Drug-addicted patients exhibit various personality disorders that interfere with their adaptation to society, as well as their ability to participate in the rehabilitation process. The Latvian Rehabilitation Programme for drug addicts includes social problem-solving training to help patients reintegrate into society. However, the role of personality disorders has not been investigated in relation to this process. AIMS: The aim of the study is to assess whether personality disorders predict changes in dimensions of social problem-solving after 6 months of rehabilitation for drug-addicted patients. METHODS: The sample of this study consists of 31 drug-addicted patients from the Latvian rehabilitation centres aged 21-35 (females 21%, males 79%). Two inventories are used: the Social Problem-Solving Inventory--Revised (SPSI-R) and Millon(TM) Clinical Multiaxial Inventory--III (MCMI-III) adapted into Russian. RESULTS: Results of the study indicated that some MCMI-III personality disorders (Schizoid and Histrionic) negatively predicted SPSI-R Positive problem orientation, and narcissistic disorder positively predicted SPSI-R Avoidance style after 6 months in the Latvian Rehabilitation Programme. The other personality disorders did not predict social problem-solving dimensions. CONCLUSIONS: The results of the study suggest that some personality disorders are related to changes in social problem-solving dimensions for drug-addicted patients. Hence, it is important to consider the implications of particular personality disorders to facilitate the implementation of social problem-solving rehabilitation programmes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".