School Environment Factors as Predictors for Teachers' Teaching Efficacy, Teacher Stress and Job Satisfaction
Bibliographic record
Abstract
The purpose of this study is identify how teachers’ perceptions of school environment factors, and the extent to which these predict outcome variables - teaching efficacy, job satisfaction and teacher stress. It also investigates if teaching efficacy affects job satisfaction, and teacher stress impacts teaching efficacy and job satisfaction. The sample included 387 Vietnamese junior high school teachers. Participants completed one questionnaire for four sections about school environment, teaching efficacy, teacher stress, and job satisfaction. The results obtained from statistical analyses show that teachers had highly favourable and positive perceptions of school environment, teaching efficacy, job satisfaction, and highly negative stress. The results obtained from multiple regression analyses also indicated that the factors of school environment as the predictors for teachers’ teaching efficacy, teacher stress and job satisfaction. Of the seven school environment factors investigated, teachers’ perception of principal leadership, mission consensus, professional interest, affiliation and student support had the most powerful effect on outcome variables. Among the outcome variables, sense of teaching efficacy positively related to job satisfaction, while both types of teacher stress negatively related to job satisfaction and teaching efficacy. The findings of the present study have educational implications.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".