The study of the psychological health of Ukrainian educators during the russian-Ukrainian War
Bibliographic record
Abstract
Under wartime conditions, Ukraine faced a critical decline in the psychological well-being of its citizens. Anxiety, fear, depression, and aggression were identified as indicators of a deteriorated psychological state. This trend also affected the educational community, whose members were expected to remain calm and bear responsibility for children. The aim of the article was to present the results of an empirical study examining the psychological health of educators of various age groups working during wartime. Empirical data analysis methods were employed, including psychodiagnostic testing, statistical averaging, and percentage ratio analysis. The article introduced a model of psychological health consisting of spiritual-meaningful, social, mental, and psychosomatic components, the development level of which determined overall psychological health status. The study revealed that one-third of respondents (37.4%) demonstrated a high level of psychological health; one quarter (27.5%) showed an above-average level; while 17.6% exhibited a below-average psychological condition. A relatively high proportion (17.6%) of educators were found to have a low level of psychological health, negatively affecting their professional performance. Specific manifestations of psychological health across different age groups were identified. Educators aged 19-24 demonstrated the lowest psychological well-being, hindering their professional self-fulfilment during wartime. Those aged 25-39 showed adaptive resilience, with good psychological health enabling productive work. The psychological health of educators aged 40-66 was generally above average and remained relatively stable despite the war. It was established that higher-order psychological health components (spiritual-meaningful and social) were sufficiently developed among modern educators, while the most vulnerable components were the psychosomatic and particularly the mental ones.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".