The Role of Emotional Intelligence in the Rehabilitation of the Former Prisoners of War
Bibliographic record
Abstract
The emotional state of military personnel engaged in the extensive operations taking place in Ukraine is undoubtedly impacted. This especially applies to former prisoners of war (POWs) who have experienced mental and physical trauma. Optimizing their subjective well-being and life satisfaction can contribute to the development of emotional intelligence during the rehabilitation process. The purpose of the study is to identify the importance of emotional intelligence in enhancing the subjective well-being of former POWs. Methods. The following psychometric tests were used for diagnosis: EQ-Test, the Scale of subjective well-being, and Satisfaction with life scale (SWLS). During the statistical analysis, descriptive statistics and correlation analysis were utilized. Results. The study found that the subjects have low emotional intelligence (M=39.31, SD=12.85), low subjective well-being (M=50.19, SD=11.06), and an average level of life satisfaction (M= 17.05, SD=8.96). Correlation analysis established a direct relationship between emotional intelligence and subjective well-being (r=0.483, p≤0.01) and life satisfaction (r=0.723, p≤0.01). Conclusions. The study statistically confirmed that emotional intelligence is an essential factor in the process of rehabilitation of former prisoners of war, as it contributes to their attainment of subjective well-being and life satisfaction. This aids in reinstating the individual's psychological balance after captivity, enhances the medical and physical recuperation process of the body, and ensures full reintegration. Prospects. The obtained results contribute to the rehabilitation system of rehabilitation for former prisoners of war, as they elaborate a comprehensive emotional intelligence approach to ensure their subjective well-being and life satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".