Thriving after crisis: Mixed-method research of teacher resilience after COVID-19
Bibliographic record
Abstract
The aftermath of COVID-19 continues to significantly affect K-12 educators, resulting in heightened workloads and diminished commitment, well-being, and health. School leaders are still grappling with persistent concerns regarding post-pandemic teacher burnout and attrition. To address these challenges, some researchers advocate fostering resilience to help teachers. This paper delves into the post-pandemic resilience of K-12 teachers in Alberta, Canada. Using an explanatory sequential mixed-method research design, Wagnild’s Resilience Survey and Resilience Supporting Questionnaires yielded quantitative data (N=71) for descriptive statistical analysis, followed by in-person semi-structured interviews (N=6), providing qualitative insights into correlated teacher behaviors. For example, resilient teachers regularly use their time and skills to help and encourage others, learn new things, and take their responsibilities seriously. Findings revealed that highly resilient teachers emphasized maintaining balance, engaging meaningfully with others, and taking personal responsibility, suggesting that resilience emerged from coping strategies and purpose-driven behaviors. This study concludes that resilience is a multifaceted process, and fostering it requires coordinated efforts between individual practices and systemic administrative support. The results offer insights and recommendations to guide administrative decisions that strengthen personal and programmatic support, encouraging behavior that fortifies resilience and increases teacher commitment and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".