Mental health and well-being of staff in business organizations under conditions of uncertainty
Bibliographic record
Abstract
Introduction. Situations of uncertainty in which staff of business organizations find themselves during the war between the Russian Federation and Ukraine often negatively affect staff's mental health and well-being. Therefore, finding out the levels of staff's mental health and well-being in conditions of uncertainty and staff's tolerance to uncertainty as determinants of their mental health and well-being are of great relevance. Aim: to determine the levels of staff's mental health and well-being and their relationship with staff's tolerance to uncertainty in business organizations. Methods. The research was carried out using a set of instruments "Mental Health in Conditions of War", which included diagnostic tools and a questionnaire-passport to analyze various aspects of staff's mental health and well-being in business organizations. Results. About a quarter of the respondents had a high level of positive mental health and psychological well-being, which indicates a significant potential for the development of these characteristics in the staff of business organizations. Almost every fifth respondent had a high general indicator of tolerance to uncertainty in war conditions. Conclusions. A connection was established between indicators of staff's tolerance to uncertainty and their positive mental health and psychological well-being in business organizations in wartime conditions.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".