Commitment Under Pressure: The Paradox of Post-Pandemic Workforce Recovery in Canadian Education
Bibliographic record
Abstract
This study examines how the education sector in one Canadian province has navigated post-pandemic recovery between 2023 and 2025, drawing on cross-sectional survey data from a convenience sample of sector employees in the 2023–2024 (n = 1411) and 2024–2025 (n = 742) school years. The findings revealed selective improvement over time, including increased organizational commitment among teachers and other education workforce members, alongside reduced perceptions of students’ academic, social, and behavioural needs. Teachers indicated no corresponding increases in their perceived frequency of meeting students’ needs, whereas other educator sector employees indicated improvement in this area. In contrast, no year-over-year differences were found in the education workforce members’ and teachers’ stress, coping, well-being or connectedness, and job search behaviours remained high, ranging from 14 to 43%. Guided by the Job Demands–Resources (JD-R) model, these findings suggest that post-pandemic recovery reflects the establishment of a new and complex baseline. The coexistence of improved commitment with persistent job strain signals a paradox of stability amid depletion. Qualitative responses reinforced this interpretation, revealing how educators adapt to ongoing organizational resource constraints and shifting student needs. Together, the findings extend JD-R-informed understandings of post-crisis workforce adaptation and identify implications for leadership and policy aimed at supporting organizational health and workforce sustainability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".