Predicting Teaching Efficacy through Occupational Stress and Job Satisfaction in the Canadian Context: A Multiple Group Analysis Based on Career Stage
Bibliographic record
Abstract
Teachers’ perceptions of their occupational stress, job satisfaction, and teaching efficacy may vary over stages of their careers. Using a subsample of teachers from the 2018 Teaching and Learning International Survey (n = 982), we used a multiple group structural regression model to test perceptions of occupational stress and job satisfaction as predictors of teaching efficacy and to examine differences among early-, mid-, and late-career teachers. Results indicated that while both occupational stress and job satisfaction predicted teaching efficacy in the early-career group, only job satisfaction predicted teaching efficacy in the mid-career group, and neither occupational stress nor job satisfaction predicted teaching efficacy in the late-career group. Tests for moderation revealed only that the link between job satisfaction and teaching efficacy was significantly stronger in the early-career group compared to the other groups. Early-career teachers also reported lower efficacy and higher stress compared to later-career teachers. Limitations and implications for research and intervention 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".