Understanding Education Workers’ Stressors after Lockdowns in Ontario, Canada: A Qualitative Study
Bibliographic record
Abstract
Understanding the experiences and stressors of education workers is critical for making improvements and planning for future emergency situations. Province-specific studies offer valuable information to understand the stressors of returning to the workplace. This study aims to identify the stressors education workers experienced when returning to work after months of school closures. This qualitative data is part of a larger study. Individuals completed a survey including a questionnaire and some open-ended questions in English and French. A total of 2349 respondents completed the qualitative portion of the survey, of which most were women (81%), approximately 44 years of age, and working as teachers (83.9%). The open-ended questions were analyzed using thematic analysis. Seven themes emerged from our analysis: (1) challenges with service provision and using technology; (2) disruption in work-life balance; (3) lack of clear communication and direction from the government and school administration; (4) fear of contracting the virus due to insufficient health/COVID-19 protocols; (5) increase in work demands; (6) various coping strategies to deal with the stressors of working during the COVID-19 pandemic; (7) lessons to be learned from working amid a global pandemic. Education workers have faced many challenges since returning to work. These findings demonstrate the need for improvements such as greater flexibility, training opportunities, support, and communication.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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 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".