Understanding the relationship between teacher leadership and teacher well-being: the mediating roles of trust in leaders and teacher efficacy
Bibliographic record
Abstract
Purpose Teacher well-being has been a concern, but there has been a lack of research on how teacher leadership can contribute to teacher well-being in a high-accountability context and a hierarchical education system such as that of China, particularly through the meditating roles of trust in the leader and teacher efficacy. Therefore, the purpose of this study was to understand the relationship between teacher leadership and teacher well-being while exploring the mediating roles of trust in leaders and teacher efficacy in this relationship. Design/methodology/approach Using structural equation modeling (SEM) and bootstrap methods with valid answers from 1,144 teachers in 25 primary schools in 1 Chinese city, this study mainly answered three questions: Is there a significant relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of trust in leaders on the relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of teacher efficacy on the relationship between teacher leadership and teacher well-being? Findings This study reported a positive relationship between teacher leadership and teacher well-being. This study also found positive mediating roles for trust in leaders and teacher efficacy in the relationship between teacher leadership and teacher well-being in a high-accountability and hierarchical system like that of China. Originality/value This study provides an understanding of the transferability of teacher leadership theories across cultures and has practical significance for educational practice in high-accountability and hierarchical education contexts similar to that of 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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".