Predicting Teacher Anxiety, Depression, and Job Satisfaction
Bibliographic record
Abstract
This study investigates predictors of anxiety, depression, and job satisfaction in teachers in northern Ontario. Using data from self-report questionnaires, factor analysis and multiple linear regression were performed to determine which sources of stress predict stress-related symptoms among teachers and to explore job satisfaction as predicted by: stress, depression, anxiety, years of teaching experience, gender, grade level assignment and position (part-time vs. full-time). The results indicate that workload and student behaviour were significant predictors of depression in teachers in the study. Workload, student behaviour, and employment conditions were significant predictors of anxiety. In addition, stress and depression had a significant and negative impact on job satisfaction. Years of teaching experience was a significant and positive predictor of job satisfaction. Anxiety, gender, grade level, and position were not statistically significant predictors of teacher job satisfaction. Therefore, efforts made to improve workload, student behavior, and employment conditions may lead to reduced stress among teachers and thus lower levels of depression and anxiety. These results may provide guidance for teachers and administrators, as well as inform teacher retention efforts and attempts to improve teacher job satisfaction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".