Ascertaining the Effect of Teachers Self-Needs on Workplace Happiness in Mainland and China
Bibliographic record
Abstract
This mixed-methods research explored the factors influencing teachers’ workplace happiness. The study investigated the mediating role of teachers’ work pressure and teaching engagement in the relationship between teachers’ self-needs and workplace happiness. It examined the moderating roles of teachers’ generation and teaching experience on the associations between teachers’ self-needs, work pressure, and teaching engagement and enhanced teachers’ workplace happiness in mainland China. A questionnaire survey was conducted with 1067 teachers from 294 undergraduate colleges and universities in mainland China, complemented by in-depth interviews with 21 teachers. The collected data were analyzed using Structural Equation Modeling (SEM). The research findings indicate that teachers’ self-needs positively influence their level of teaching engagement, subsequently impacting their workplace happiness. Conversely, higher levels of self-needs were associated with lower work pressure, suggesting a negative correlation between the two. Moreover, the study reveals that work pressure negatively affects teachers’ workplace happiness. Teaching engagement not only enhances teachers’ workplace happiness but also acts as a mediator between teachers’ self-needs and workplace happiness. This indicates that when teachers’ self-needs are fulfilled, they are more likely to be engaged in teaching and learning, leading to increased satisfaction and happiness in the workplace. Furthermore, the relationship between self-needs, teaching engagement, work pressure, and workplace happiness is significantly influenced by teachers’ generation and teaching experience. It is essential to recognize that teachers from different generations and varied levels of teaching experience may exhibit distinct levels of engagement and responses to work pressure. These factors moderate the impacts of self-needs and work pressure on teachers’ workplace happiness, ultimately enhancing teaching engagement and improving overall workplace happiness among teachers in mainland China.
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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".