Exploring the Factors Influencing Work-Life Balance: A Study on the Perception of Work-Life Balance Among Teachers in Rural Areas
Bibliographic record
Abstract
This research strives to create sustainable mechanisms, which will enhance work-life balance (WLB) among teachers in Uttarakhand, India.A quantitative research design was applied using a structured questionnaire to capture data of 194 rural teachers who were selected using stratified random sampling between January and March 2024.The study investigated the connection between workload, teaching environment perceptions, and demographic variables and WLB.Correlation, regression, and factor using SPSS were used in statistical analysis.The findings indicated that there was a significant negative relationship between workload and WLB (r = 0.732, p < 0.001); strong positive relationship between teaching environment perceptions and WLB (r = 0.821, p < 0.001).Age, gender, and marital status played off as significant predictors (R 2 = 0.510, p < 0.001) as well.These results contextualize the necessity of specific measures (workload control, flexible time, and support systems in institutions) to support the well-being and retention of rural teachers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".