Changes in Teacher Burnout and Self-Efficacy During the COVID-19 Pandemic: Interrelations and Variables Related to Change
Bibliographic record
Abstract
Although the reciprocal relationship of teacher burnout and teacher self-efficacy (TSE) is well documented, the literature still lacks studies investigating their (latent) changes and interrelations of change over time. By applying a latent change regression model in our study, we aimed to contribute to this research gap by examining changes in burnout and their relations to changes in TSE during the COVID-19 pandemic—a very challenging time for teachers. As the implementation of digital learning material played a major role during the pandemic, we were also interested if attitudes and self-efficacy toward e-Learning were related to changes in burnout and TSE. Our sample consisted of 92 German in-service teachers who completed a questionnaire twice during the 2019–2020 school year. Our main findings are that the burnout components depersonalization and lack of accomplishment significantly increased from the pre- to post-COVID-19 outbreak, whereas emotional exhaustion did not. Changes in burnout were negatively correlated to changes in TSE, but we found little evidence for relations of change in burnout and TSE with variables concerning e-Learning. Our findings indicate that the challenge was not the work overload but rather a lack of resources. Implications for research and practice are discussed.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".