Predicting Teacher Anxiety, Depression, and Job Satisfaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it