The effects of instructional leadership on teacher well-being: the mediating roles of professional learning community and teacher self-efficacy
Bibliographic record
Abstract
This study was prompted by the scarcity of empirical investigation regarding the relationship between instructional leadership and teacher well-being through the mediating roles of professional learning communities and teacher self-efficacy in Chinese school settings. Data from 1158 primary and secondary school teachers in South China were collected for analysis. Partial least squares structural equation modelling was employed for data analysis. The results indicated a positive relationship between principal instructional leadership and teacher well-being after controlling for teacher demographic factors. Instructional leadership has a substantial impact on the professional learning community. Both professional learning communities and teacher self-efficacy emerged as significant serial mediators in the structural model. Principal instructional leadership, professional learning communities, and teacher self-efficacy collectively explained approximately 43% of the variance in teacher well-being. These findings inform practical and theoretical implications, suggesting avenues for enhancing principal instructional leadership and, consequently, fostering teacher well-being in the hierarchical Chinese education setting.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".