Burnout among Chinese EFL university instructors: a mixed-methods exploration of school climate, job demands, and emotion regulation
Bibliographic record
Abstract
Introduction: Teacher burnout is a significant global concern in higher education, impacting instructor well-being and educational quality. English as a Foreign Language (EFL) instructors in Chinese universities face unique pressures that may heighten their burnout vulnerability. This mixed-methods study, guided by the Job Demands-Resources (JD-R) model, investigated the intricate relationships between perceived school climate, challenging job demands, emotion regulation, and teacher burnout among Chinese EFL university instructors. Methods: The study employed an explanatory sequential mixed-methods design. Quantitative data were collected from 478 Chinese EFL university instructors using scales assessing perceived school climate, challenging job demands, emotion regulation, and burnout; these data were analyzed using confirmatory factor analysis and structural equation modeling. Subsequently, qualitative data were gathered through semi-structured interviews with 21 instructors, selected purposively from the quantitative sample, and analyzed using thematic analysis to provide deeper insights. Results: Quantitative analysis revealed that a positive perceived school climate was associated with lower burnout, while high challenging job demands were associated with higher burnout. Emotion regulation significantly mediated these relationships, buffering the negative effects of job demands and enhancing the positive effects of school climate. The qualitative analysis yielded three key themes: (1) The Supportive yet Stifling School Climate, which highlighted appreciation for collegiality alongside constraints from rigidity and hierarchy; (2) The Weight of Unrealistic Expectations, detailing heavy workloads and competing demands; and (3) Navigating the Emotional Landscape, describing instructors' strategies and challenges in managing emotions. These themes provided rich context, illustrating how instructors navigate institutional structures, workloads, and emotional stressors. Discussion: The findings underscore that both work environment characteristics (school climate and job demands) and personal resources (emotion regulation) are crucial in understanding EFL teacher burnout in the Chinese university context. The integrated results highlight the importance of fostering positive, supportive school climates that promote autonomy and recognize teaching excellence, alongside providing resources and support to help instructors manage job demands and enhance their emotion regulation skills.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".