Emotional Intelligence and Burnout of Teachers of Higher Education Institutions
Bibliographic record
Abstract
Emotional intelligence is an important resource for overcoming professional stress in members of socionomic professions. The research objective is to determine the role of its components in the development of emotional burnout. A natural experiment was conducted, which determined the manifestations of emotional burnout of 56 university teachers at the end of the academic year. The author used the questionnaire. Two experimental groups were identified in the general sample: teachers with burnout and those resistant to burnout (16 and 30 people, respectively). At the end of the academic year, signs of burnout were detected in one-third of university teachers. The leading symptoms are emotional exhaustion and depersonalisation, with no reduction in professional achievement. The dynamics of emotional life during the annual professional cycle are shown. The integrated indicator of emotional intelligence (EI) remains at the same level, but there are structural changes in the components of intrapersonal intelligence. At the end of the year, teachers' attention to their emotional states, work roles, and communication increase significantly. At the same time, there is a decrease in the ability to manage their own emotions. Resistance to burnout is accompanied by a high ability to realise and control their own emotions with a relatively vague focus on the emotional states of others. It was concluded that individual components of EI (intrapersonal and interpersonal, understanding and management) have different effects on burnout symptoms.
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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 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".