Unraveling Job Stress, Burnout, and Psychological Capital among Chinese EFL Teachers in Higher Institutions
Bibliographic record
Abstract
This study delves into the status quo, variations based on demographic information, and the relationship between job stress, burnout, and Psychological Capital (PsyCap) among Chinese English as Foreign Language (EFL) teachers in higher institutions. The investigation utilized a questionnaire for data collection and analysis. 297 EFL teachers from various institutions in China were recruited between July and October 2023. The data underscores that Chinese EFL instructors in higher institutions experience moderately high levels of stress, burnout, and PsyCap. However, when compared to stress and burnout levels, PsyCap emerges as relatively lower. The statistical results revealed that male teachers report significantly higher stress levels than their female counterparts; no difference was identified in job burnout indicators; among four indicators in PsyCap, male teachers exhibit significantly higher self-efficacy compared to female teachers. Private school teachers face elevated levels of stress and increased burnout compared to their public school counterparts, alongside possessing lower levels of PsyCap than those in public schools. Positive correlations exist between job stress and burnout, and negative correlations with PsyCap. PsyCap partially mediates the stress-burnout relationship, with indicators like hope and resilience playing a mediating role. This research may offer some guidance for educators, institutions, policymakers, and researchers to enhance the well-being of Chinese EFL teachers in various educational settings.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".