The Impact of Boss Phubbing on Teacher Self-Efficacy and Well-Being in Coastal Schools During the Digital Era
Bibliographic record
Abstract
This study aims to analyze the impact of boss phubbing on teachers’ self-efficacy and teacher wellbeing in coastal-area schools within the context of digital-era educational management. Data were collected through a cross-sectional survey of teachers in coastal schools with a total sample of 206 respondents. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Jamovi 2.6.23 software. The findings indicate that boss phubbing does not directly affect teacher wellbeing but has a negative impact on teachers’ self-efficacy, which in turn plays a significant role in enhancing teacher wellbeing. These results highlight the importance of teachers’ self-efficacy as a mediator in reducing the negative effects of supervisors' digital behavior on teacher wellbeing in the digital era. Practical implications suggest that school management should pay attention to leaders’ digital behavior and foster teachers’ self-efficacy to improve work quality and teacher wellbeing. The limitations of this study include its cross-sectional design and the sample being limited to coastal-area schools; therefore, future research is recommended to employ longitudinal designs and consider additional moderating variables such as social support and organizational culture.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".