Professional Crisis or Temporary Burnout? Teacher’s Experiences Towards the End of the Covid-19 Pandemic
Bibliographic record
Abstract
Various studies were conducted during the Covid-19 pandemic on teachers’ experiences and feelings during the abrupt shift to remote learning; however, the prolonged effects of the pandemic on teachers in Israel have not been examined. The present study was conducted towards the end of the pandemic, during the last (and, so far, final) wave of the Covid-19 pandemic in Israel. This wave was characterized by the resumption of in-person classroom teaching or hybrid teaching and refraining from imposing lockdowns. For this study, interviews were conducted with 58 elementary school teachers. Analysis of the interviews reveals a profound rupture in the teachers’ sense of efficacy, stemming from numerous changes, an upsurge in emotional and social problems among children, and teachers’ skills being only partially suited to the situation. As stated by the teachers, it seems that role frustration and a rising tendency to leave the education system express the need for long-term change in the teaching profession. An analysis of findings is provided from the perspective of theories of organizational engagement and employee competence. Recommendations are proposed concerning teachers’ future professional development processes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".