Occupational Stress and Job Satisfaction: Unveiling Challenges in Teachers' and Doctors' Work Environments
Bibliographic record
Abstract
This study explores occupational stress and job satisfaction among schoolteachers and doctors in Oman, addressing systemic and profession-specific challenges. Stratified random sampling was used to select 238 participants (150 teachers, 88 doctors) from various regions in 2024. Data was collected using a structured survey instrument, including validated measures of occupational stress, job satisfaction, and workplace challenges. A quantitative approach employing Principal Component Analysis (PCA), correlation analysis, and Structural Equation Modeling (SEM) revealed workload and administrative tasks as primary stressors, explaining 27.08% of the variance in stress levels. Teachers faced higher stress from student behavior, while doctors experienced stress from patient care demands. SEM results showed workload (β=0.72, p<0.001\beta = 0.72, p< 0.001 β=0.72, p<0.001) and administrative responsibilities (β=0.63, p<0.01\beta = 0.63, p < 0.01 β=0.63, p<0.01) significantly impacted stress and job satisfaction. Recommendations include systemic reforms to reduce workload, behavioral training for teachers, and resilience programs for doctors, fostering well-being and improving performance in education and healthcare sectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".