The Mediating Effect of Social Support on the Relationship Between Job Stress and the Psychological Well-Being of Early Childhood Education Teachers
Bibliographic record
Abstract
The purpose of this study was to examine the mediating effect of social support on the relationship between the sense of psychological well-being of early childhood education teachers and their job stress. The data were collected through questionnaires administered to 176 Korean kindergarten teachers. The instruments used in this study were the Kindergarten Teachers` Job Stress Scale (4 factors, 34 items), Social Support Scale (4 factors, 24 items), and Psychological Well-Being Scale (6 factors, 48 items). The data were analyzed using descriptive statistics, Pearson`s correlation analysis, multiple regression analysis, and Baron and Kenny`s method, using SPSS ver. 20.0 for Windows. This study followed the mediated effect model, and discovered the following: First, there was a moderately negative correlation between job stress and psychological well-being but the negative influence was statistically significant in path model 1. There was a moderately positive correlation between social support and psychological well-being. In path model 2, assessment support, one of the social support sub-scales, was statistically significant in the area of influence on psychological well-being. Second, there was a mediating effect of social support on job stress and psychological well-being in the regression model. These results will be of great avail to future studies on the improvement of early childhood education teachers` psychological well-being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".