Daily challenge-hindrance stress and work engagement in preschool teacher: the role of affect and mindfulness
Bibliographic record
Abstract
BACKGROUND: The engagement of preschool teachers in their work is pivotal for maintaining teaching quality, ensuring teacher well-being, and fostering children's development. Despite its significance, there is limited knowledge regarding the daily fluctuations in work engagement and the underlying factors influencing it. This study, guided by the Job Demands-Resources model and Affect Event Theory, utilized an experience sampling methodology to investigate the impact of challenge and hindrance stressors on daily work engagement, as well as the mediating role of affect and the moderating effect of mindfulness. METHODS: Utilizing an experience sampling method, this study collected data from 220 preschool teachers in Shanghai over five consecutive workdays, conducting surveys once daily. Data analysis was performed using multilevel linear models. RESULTS: The results from multilevel regression indicated that: (1) daily challenge stressors were positively related to work engagement, (2) daily hindrance stressors were negatively related to work engagement, (3) daily positive affect mediated the relationship between challenge stressors and work engagement, (4) daily negative affect mediated the relationship between hindrance stressors and work engagement, and (5) daily mindfulness played a crucial moderating role by alleviating the adverse effects of hindrance stressors on daily negative affect. CONCLUSIONS: This study provides valuable insights into the daily experiences of preschool teachers and the factors that influence their work engagement. Understanding the impact of stressors, affect, and mindfulness on work engagement can inform the development of interventions and strategies to improve teacher well-being and work engagement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".