Emotional Intelligence as a Factor against Burnout in Female Students and Teachers
Bibliographic record
Abstract
A joint analysis of the concepts of "emotional intelligence" and "burnout" allows finding new ways to protect against the adverse effects of chronic stress. It is known that emotional competencies are determined by gender and gender, but this aspect needs to be clarified. A longitudinal study was conducted in a female sample (575 students and 96 teachers from different regions of Ukraine) to determine the dynamics of burnout in calm and stressful periods and trace the correlation of symptoms with emotional abilities. The structural components of emotional intelligence (reflection, self-regulation, empathy, expressiveness, and acceptance of one's own emotions) and manifestations of burnout were measured; three diagnostic sections were made at the beginning of the academic year, after the winter and spring examination sessions. Significant growth of all burnout indicators during the annual training cycle (MANOVA) was recorded. The effect of the accumulation of fatigue (exhaustion) was robust in teachers and graduate students. At the same time, after the session, students grew a sense of self-efficacy, compensating for the resources spent. Comparison of means in six subsamples of students in grades 1-5 and teachers (ANOVA) showed that structural changes in emotional competencies describe the adaptive potential of a certain age period. The developed empathy and self-regulation are the main signs of women's emotional maturity after graduation. The structure of correlations between burnout parameters and emotional competencies differed in different groups. The most significant contribution to preventing burnout in students is made by reflection and self-regulation, in teachers — by self-regulation, empathy, and acceptance of one's feelings. The negative correlation between emotional abilities and symptoms of burnout is exacerbated during times of stress. It is concluded that there are two mechanisms of the protective influence of emotional intelligence: direct, which prevents exhaustion by controlling and regulating negative emotions, and indirect, through a sense of self-efficacy resulting from the successful overcoming of professional challenges.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".