Administration and K-12 Teachers Promoting Stress Adaptation and Thriving: Lessons Learned from the COVID Pandemic
Bibliographic record
Abstract
Lessons learned from the effects of the COVID-19 pandemic on the well-being of teachers reveal how school administrators can promote teacher stress adaptation and thriving, even in highly disruptive work environments. In a mixed-methods study within a single school district in Canada, consisting of a survey of 65 K-12 teachers and interviews with 10 administrators and teachers, the results showed the degree to which teachers were coping, had job satisfaction, and demonstrated thriving. Interviews yielded information on the limitations of the education system response and how school district administration could provide additional key resources that would strengthen individual stress coping and resiliency, create a culture of safety and community, and lay the foundations for teacher thriving, even in challenging and disruptive conditions
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".