Job Satisfaction and Mental Wellbeing Among High School Teachers in Malaysia
Bibliographic record
Abstract
Job satisfaction plays an important role in regard to teachers’ continuation in the teaching profession. School-based factors such as relations with colleagues, parents, and student behavior are important factors that contribute to teachers’ fulfillment in their workplace. This study examined the relationship between job satisfaction and wellbeing among high school teachers in Malaysia. A total of 111 full-time high school teachers (99 females, 12 males) from two schools located in Kuala Lumpur, Malaysia, completed measures of the Teacher Job Satisfaction Scale and Short Warwick-Edinburgh Mental Wellbeing Scale. Data were analyzed using correlation coefficient and regression analysis. The results indicated significant positive correlation between teachers’ job satisfaction and wellbeing. More specifically, teachers’ satisfaction with students’ behavior and students’ parents were significant predictors of mental wellbeing. This study highlighted job satisfaction that teachers themselves may have for positive personal relationship with co-workers, students, and student’s parents, providing further understanding of the contribution of job satisfaction to teachers’ mental wellbeing. This study helps complement previous studies by providing a further understanding on the contribution of job satisfaction to teachers’ wellbeing in the Malaysian context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".