Burnout Experience among Iranian Teachers during the COVID-19 Pandemic
Bibliographic record
Abstract
This study represents the first attempt to explore teachers’ burnout experience during one of the most critical phases of the COVID-19 pandemic in Iran. The main goals were to estimate the prevalence of burnout in Iranian men and women teachers and analyse the association of sociodemographic variables on burnout levels. A total of 125 Iranian teachers participated in this study. Results suggest that Iranian teachers perceive high levels of burnout during the COVID-19 pandemic, with 24% of participants reporting high levels of overall burnout. Furthermore, 32% of the sample reported high levels of physical fatigue, 24.8% high levels of cognitive weariness, and 17.6% high levels of emotional exhaustion, suggesting that a considerable number of Iranian teachers are already struggling to deal with their job-related stressors. Significant differences were found for sex, with men reporting higher exhaustion than women. No significant differences were found between other sociodemographic characteristics and burnout. We analyse the results from a cultural perspective and discuss its implications for future research and psychological interventions in schools. Future studies explore how school contextual variables can mediate or moderate the effect of sociodemographic characteristics on teachers’ burnout. Intervention programmes should consider local schools’ characteristics and be sensitive to teachers’ individual needs and consider the added weight of the pandemic to teachers’ daily job-related stressors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".