“My teaching job is not what it used to be, and I'm looking for a way out:” A Mixed-Methods Examination of Teacher Stress Levels Following the COVID-19 Pandemic
Bibliographic record
Abstract
Education following the COVID-19 pandemic is characterized by teacher stress, burnout, and turnover, leaving U.S. schools desperately short of teachers required to produce quality educational outcomes for students. This study aims to unpack current drivers of teacher stress and to provide options for educators looking to support K-12 teachers. A mixed-methods approach was utilized in surveying K-12 teachers (N = 295) within multiple school districts throughout Western Pennsylvania. Results show that time management, student discipline, and increased workloads are primary drivers of teacher stress, with insights that changes in student motivation, the volume of teachers’ work, lack of administrative support, and teachers’ struggles with mental health, led to increased stress. Suggestions for ways to support teachers experiencing stress are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".