Welfare, wellness, and job satisfaction of <scp>Chinese</scp> physicians: <scp>A</scp> national survey of public tertiary hospitals in <scp>China</scp>
Bibliographic record
Abstract
Little national data are available on Chinese physicians' welfare, wellness, and job satisfaction. We conducted a self-administered smartphone-based national survey in early 2016 of 17 945 physicians from 136 tertiary hospitals across 31 provinces in China. In addition to collecting the physicians' basic information, we also measured 5 domains (the ethical and working environments, welfare, wellness, and job satisfaction). Half of the physicians reported a hospital-based annual income of less than RMB 72 000 ($10 300), and 60.31% of them did not think that the current medical pricing system reflects physicians' value. More than half (58.64%) of them did not have or did not know about medical malpractice insurance. These physicians worked long hours (an average of 10 h) and slept short hours (average 6 h). Only 35.78% of them thought that they were in good health, and 51.03% were in good mental health. Approximately, a quarter of them had helped to pay medical bills for patients who could not afford care, and 1 in 7 has been penalised for seeing patients who generated bad debts. Only 33.42% of them thought that their occupation receives social recognition and respect, and 70.98% would not encourage their children to pursue a medical career. The top 3 factors that may influence physician job satisfaction as chosen by the physicians were as follows: (1) the income distribution policy (45.92%), (2) working environment safety (25.86%), and (3) public trust and respect for their job (16.10%). In conclusion, we found that Chinese physicians bear heavy physical, mental, and financial stress, and many of them lack confidence that they receive trust and respect from society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".