Investigating the Views of Directors of Secondary Education Units About the Burnout Syndrome During the Three-Year Term (2017-2020)
Bibliographic record
Abstract
Background and aim: Τhe phenomenon of professional burnout has begun to be studied systematically in the field of education, particularly in those who serve in a managerial role in order to determine how the multilevel role affects the effectiveness and self-perception of individuals. This study was conducted with the aim of recording/investigating the symptoms of the phenomenon of burnout in the directors of school units serving in the Secondary Education of the Regional Units of Evros and Rodopi in Greece. Methodology: Literature review documented by sources of theoretical content guided the acquisition of empirical findings through quantitative research, a sample of which was responding 50 directors from 78 schools in urban, semi-urban and rural areas of the above regional units. Statistical data analysis was done using the google drive form and the possibilities provided by the excel program. Results: Data were analyzed according to variables of sex, type of residential area, number of students and years of experience. Discussion: Results indicated that secondary school directors do not experience burnout to a significant degree. Greater difficulty is reported in managing the time they spend due to their duties and bureaucracy Further research is required to shed light on the aspects of the phenomenon and explore the psychological consequences and its management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".