Teacher–Student Conflict and Teacher Job Stress in South Korea: Public Health Implications and the Mediating Role of Controlling Classroom Managements
Bibliographic record
Abstract
This study explored the impact of teacher–student conflict on teacher job stress in South Korea, with a focus on the mediating role of students’ perceptions of controlling classroom environments. Data were drawn from 616 first-year middle school students, their parents, and teachers who participated in the 14th wave of the Korean Children and Youth Panel Survey (2021). Descriptive statistics, reliability analyses, and Pearson correlation coefficients were computed using SPSS Version 26.0, and mediation analysis was performed with PROCESS macro (Version 4.2; Model 4) employing bootstrapping procedures. The results reveal significant positive associations among teacher–student conflict, teacher job stress, and controlling classroom environments. Moreover, controlling classroom environments are positively related to teacher job stress and partially mediated the association between teacher–student conflict and teacher job stress. These findings underscore the importance of addressing classroom management practices to alleviate teacher stress, foster healthier teacher–student interactions, and promote teacher well-being. The study also highlights limitations, including the reliance on cross-sectional data, and suggests directions for future research to further clarify causal relationships and explore intervention strategies.
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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 | 0.002 |
| 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 | 0.000 |
| 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".