Bibliographic record
Abstract
Teachers are believed to be a profession which brings relatively high job satisfaction as well as high level of stress in their job settings because of various reasons such as heavy workload, long teaching hours, large class size, students’ disciplinary problems, cramped classrooms, excessive administrative work and so on. To examine what the main stressors are and whether gender and teaching experiences will make a difference on how teachers perceive job-related stress, this study has designed a questionnaire called Stress and Job Satisfaction Scale for Teacher (SJSST) to explore the issues. Results showed that school teachers faced moderate level of job-related stress. The main stressors were ‘demands from job’, ‘work-life balance’ and ‘control over work’. It was also found that male teachers had higher level of stress in general. ‘Psychosocial work environment’, ‘health & well-being’, and ‘relations at work’ were found to have significant difference between male and female teachers. According to the results of ANOVA, years of teaching experience were significant for all stressors. Teachers with more than 30 years of teaching experience received highest level of stress from ‘demands from job’ and ‘work-life balance’ among other groups of teachers. Teachers with 11-20 years of experience had highest level of stress from ‘control over work’ and ‘psychosocial work environment’. While teachers with 6-10 years of experience, they suffered highest level of stress from ‘health and well-being’, ‘future and change’, ‘relations at work’, and ‘physical environment’.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".