A Comparative Study to Determine the Occupational Stress Level and Professional Burnout in Special School Teachers Working in Private and Government Schools
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Healthy work environment is required to provide high quality teaching. Few studies regarding occupational stress and burnout in Indian schools have been conducted. The study aims to determine and compare the occupational stress level and professional burnout in teachers working in private and government schools. 120 private school teachers and 120 government school teachers recruited for this study.METHODS: Chi square test, Student independent t test and ANOVA used for data analysis have been used.RESULTS: Stress level and burnout level are varies significantly between male and female Indian school. (Chi square value 26.27 and p Value 0.001 in male and chi square value 38.06 and p value 0.001 in female). Government schools (Least Likely 5±0.0, More Likely 10.82±1.99, Most Prone Stress 16.15 ±1.84 have significant difference among them) have higher stress level than Private schools (Least Likely 4.00±1.41, More Likely 9.86±2.29, Most Prone Stress 15.66±1.34 have significant difference among them). Governmental schools having more stress (14.33±3.24) compare to Private schools (13.34±3.51), (t=2.26, p =0.023). Similarly the burnout also having the more in government schools (59.43±11.78) compare to private schools (48.61±11.94), (t=7.07, p=0.001).CONCLUSIONS: The government teachers have more occupational stress compared to the private school teachers in India. Leaders and decision makers required to make early identification and counseling about different factors that influence stress level in private and Government school teachers.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".