Teacher mental health and workplace well-being in a global crisis: Learning from the challenges and supports identified by teachers one year into the COVID-19 pandemic in British Columbia, Canada
Bibliographic record
Abstract
The COVID-19 pandemic and related school disruptions have led to increased concerns for the mental health of teachers. This study investigated how the challenges and systemic supports perceived by teachers during the COVID-19 pandemic were associated with their mental health and workplace well-being. This cross-sectional, survey-based study was conducted in February 2021, just prior to the third wave of the pandemic in British Columbia (BC), Canada (N = 1,276). Four multivariable linear regression models examined the associations between teachers' pandemic-related challenges (pandemic-related personal stressors, teacher workload, difficulty implementing safety measures, meeting students' needs), systemic supports (education system mental health and well-being support), and four mental health (psychological distress, and quality of life) and workplace well-being outcomes (job-related positive affect, turnover intentions), adjusting for sociodemographic and school characteristics. The Pratt index (d) was used to assess the relative importance of each predictor. A thematic qualitative analysis was conducted on teachers' open-ended responses. Teachers' workplace well-being (job-related positive affect and turnover intentions) was predominantly associated with their perceptions of education system support for their mental health and well-being (d = 46%, d = 41%, respectively). The most important predictor of general mental health (psychological distress and quality of life) was the number of COVID-19 related personal stressors teachers reported (d = 64%, d = 43%, respectively). The qualitative analyses corroborated and expanded upon the quantitative findings. Understanding pandemic-related challenges and supports impacting teacher mental health and workplace well-being equips us to make evidence-informed policy decisions to support teachers now and in future school disruptions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".