Relationship between Job Satisfaction and Psychological Distress among Fresh Graduates during COVID-19 Pandemic in Zhejiang, China: The Mediating Role of Coping
Bibliographic record
Abstract
With the destructive COVID-19 issues in China, the problem of job satisfaction has influenced more and more people, especially fresh graduates. Although existing research has discovered the association between job satisfaction and psychological distress, this study made contributions to the knowledge base and future researchers by investigating the mediation effect of coping on the above relationship. Fresh Graduates (N=384) completed questionnaires namely the Minnesota Satisfaction Questionnaire Short Form, the 4-item Brief Resilient Coping Scale, and The 10-item Kessler Psychological Distress Scale. Results showed there is a negative relationship between job satisfaction and psychological distress, and coping would partially mediate the above relationship. It was seen that job satisfaction is related with higher coping, while individuals with lower coping are more likely to experience higher levels of psychological distress, which may have a negative impact on their mental health and well-being. This study provides knowledge implications for society, policymakers, employers, and universities to be aware of the significance of fostering job satisfaction and promoting healthy coping strategies in order to improve the well-being of fresh graduates entering the workforce during difficult periods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".