Work Intensification, Work–Life Conflict and Turnover Intentions in the Teaching Profession: Evidence From School Teachers in Quebec, Canada
Bibliographic record
Abstract
ABSTRACT Teachers worldwide are expected to adapt to increasingly complex demands. Meanwhile, there is a shortage of qualified teachers in the profession. In this context, our paper explores the role of work intensification (WI) as a predictor of teacher turnover intention, an important antecedent that has never been explored amongst school teachers. The role of work–life conflict (WLC) is also considered, given the salience of this issue according to teacher unions. We distributed an online questionnaire to teachers from various sectors (preschool, primary, secondary, adult training, professional training and special education) through union listings and got 405 valid responses. We ran statistical analyses using PROCESS Macro v.4.2 for SPSS, and our results indicate a direct, significant and positive relationship between WI and intention to leave (p ≥ 0.001; R2 = 0.179). Moreover, we found that WLC interacts with WI in its impact on intention to leave (p ≥ 0.001; R2 = 0.191). Theoretical contributions are made using the job demands‐resources and conservation of resources theories, and practical implications for government and school leaders are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".