Socio-Psychological Factors in the Development of Emotional Intelligence of Drug Addicts
Bibliographic record
Abstract
Drug addiction is a current global problem, which causes significant damage to the individual and society as a whole. Drug addicts have numerous disorders, among which the emotional sphere occupies an important place. Identification of social and psychological factors affecting the development of emotional intelligence of drug addicts will make it possible to optimize their psychological rehabilitation programmes. The aim of the study is based on establishing the influence of social and psychological factors on the development of the emotional intelligence level in drug addicts. Methods: The research programme uses standardized psychometric diagnostic methods (MSPSS, Self-Monitoring Scale, EQ-test, Self-esteem test). Descriptive statistics, the Kruskal-Wallace H test, and Pearson’s linear correlation coefficient were used. The Results: The study showed that a low level of emotional intelligence prevails among the surveyed drug addicts (M=37.63±13.38). At the same time, people with a low level have pronounced signs of low social self-control (Н=67.64, р≤0.001), social support (Н=67.76, р≤0.001), and self-esteem (Н=89.12, р≤0.001). Correlation analysis revealed a close direct relationship between emotional intelligence and social self-control (r=0.681, p≤0.001), social support (r=0.632, p≤0.001), and self-esteem (r=0.726, p≤0.001). Conclusions: The study found that the development of emotional intelligence of drug addicts is influenced by such social and psychological factors as social self-control, social support, and self-esteem. These factors determine the ability to manage emotions, adequately perceive them and objectively express them. Prospects: The identified results can be used when building a system of psychological rehabilitation for persons with drug addiction. In particular, to develop emotional intelligence, and improving general emotional well-being.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".