The psychosocial work environment among educators during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The education sector has been heavily impacted by COVID-19. While the impact on school-aged children has received much attention, less attention has focused on the experiences of educators. AIMS: To compare various dimensions of the psychosocial work environment and health outcomes between educators engaged in online learning to those engaged in in-person learning in the Canadian province of Ontario. METHODS: Responses from 5438 educators engaged in either online or in-person learning were collected between 23 November and 21 December 2020; three months after the start of the 2020/21 academic year in September 2020. Psychosocial outcomes included quantitative demands, work pace, predictability, role conflicts, and social support from supervisors and co-workers; assessed using an abbreviated version of the Copenhagen Psychosocial Questionnaire. Secondary outcomes included burnout and sleep troubles. Ordinary Least-Squares regression models examined adjusted mean differences in the levels of outcomes for respondents in in-person versus online learning, after adjustment for a variety of covariates. RESULTS: Compared to respondents engaged in in-person learning, respondents engaged in online learning reported less predictability, higher role conflicts and less support from supervisors and co-workers. Statistically significant differences in work pace, burnout and sleep troubles were also observed across learning modes, although these differences did not exceed previously suggested thresholds for minimum important differences. CONCLUSIONS: Important differences in the psychosocial work environment were observed between respondents engaged in in-person learning versus online learning. Addressing these differences is required, given the potential continued importance of online learning within the context of the COVID-19 pandemic and beyond.
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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 | 0.002 | 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) | 0.009 | 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".