Canadian educators' post-pandemic recovery and students’ unmet needs: Who is left behind?
Bibliographic record
Abstract
We investigated post-pandemic recovery in education sector employees by role in 2024. The frameworks of the job demands-resources model and ecological systems theory were employed. Canadian educators ( N = 243) completed surveys exploring their mental health (well-being, resilience, recovery), intention to leave their jobs, and their perceptions of students' current and post-pandemic needs. Quantitative findings revealed educators who intended to leave their jobs had poorer levels of mental health. Also, they were not meeting their students’ needs adequately. The qualitative data showed that students with complex needs were disproportionally under-served. Theoretical, practical, and policy implications on equitable education are discussed. • This Canadian study of 243 educators investigated the relationship between educators' mental health and intention to leave their jobs. • Educators' perceptions of students' post-pandemic needs as well as their ability to meet them were examined. • The vast majority of educators perceived greater post-pandemic academic, social and behavioural student needs and over a third of educators never or rarely met these needs. • Across roles, 20%–45.5% of educators were planning to leave their jobs. • Educators who intended to leave their jobs indicated poorer levels of well-being and resilience. They also indicated significantly higher perceptions of post-pandemic levels of students' academic needs, and indicated that they were meeting their students' needs significantly less often. • The qualitative data indicated that students with disabilities in particular were disproportionally under-served.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".