Empirical Investigation of Work-Family Conflict and Teachers’ Happiness Through Employee Assistance Programs in Yunnan Middle Schools
Bibliographic record
Abstract
This study aims to (1) explore the influence of work-family conflict on middle school teachers’ happiness and (2) investigate whether the implementation of the Employee Assistance Program (EAP) affects middle school teachers’ happiness. A quantitative research approach with a sample size of 242 teachers from public and private middle schools in Yunnan province, China, was used, as determined by the Krejcie and Morgan’s (1970) table. The research instruments were two questionnaires: (1) The Work-Family Conflict Scale and (2) the Employee Assistance Program Scale. The results presented that (1) the influence of work-family conflict on middle school teachers’ happiness was mean=3.41 and S.D.=1.13. It was the rating of “moderate,” (2) implementation of the Employee Assistance Program (EAP) had a moderating effect on middle school teachers’ happiness was at the level of mean =3.20 and S.D.=1.10. It was the rating of “moderate” that work-family conflict significantly impacts middle school teachers’ happiness, and employee assistance programs significantly moderate middle school teachers’ happiness. However, this study’s limitations arise from its focus on middle school teachers and the lack of extensive analysis of various EAP dimensions and work-family conflict aspects. Additionally, the findings may not apply to teachers in other educational stages, such as primary schools or universities. Future research could investigate the specific dimensions and implementation items of the EAP for middle school teachers, offering insights into their most pressing needs concerning work, life, and health. Such research would contribute to developing a better working environment and learning atmosphere for teachers and students.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".