Psycho-Emotional Burnout of the Personality in the Conditions of War
Bibliographic record
Abstract
A common cause of health disorders in war is psychotraumatic situations that lead to psycho-emotional burnout of military personnel. The purpose of the article is to conduct an empirical study of the psycho-emotional burnout of military personnel. Tasks are set: to determine the emotional reactions and states of military personnel; to assess the level of manifestation of emotional burnout of military personnel. Research Methods: The study used valid methods, "Diagnosis of the level of emotional burnout", "Determination of mental "burnout", and «Burnout Syndrome» in the professions of the «man-man» system, for the non-parametric data correlation variables, the Spearman coefficient. Results: The study revealed the military personnel's inability to manage their emotions, which leads to depersonalization, personal alienation, and distance. The inadequate selective emotional response has 94.0%, which is an indisputable "sign of burnout". The peculiarities of emotion management of military personnel and their role in the genesis of emotional response in professional activity are revealed. It is established that the "economic manifestation of emotions" of military personnel can be manifested both in an excessively cheerful mood and aggression, which is frustrating behavior. Conclusion: In order to prevent psycho-emotional burnout, it is necessary to develop anti-stress skills, skills of controlling the manifestation of emotions based on critical analysis of the situation, and the ability not to throw out negative emotions on other people. To work with the military, it is necessary to substantiate the content of psychosocial counseling within the framework of a comprehensive system of psychological rehabilitation of servicemen.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".