Would you choose to be a psychiatrist again? A large-sample nationwide survey of psychiatrists and psychiatry residents in China
Bibliographic record
Abstract
BACKGROUND: The mental health workforce sustainability in China suffers high rates of attrition and the intention to leave. Among current professionals, the intention to choose the same career is an interesting way to gauge their job satisfaction and other factors, and it may affect the career choices of younger generations. We aimed to survey the intention of psychiatrists and psychiatry residents to choose the same career if they could start over and to identify associated factors. METHODS: We conducted an anonymous survey of psychiatrists in 41 tertiary psychiatric hospitals in China. We collected demographic data, work-related information, the sense of professional identity, job satisfaction, and burnout (Maslach Burnout Inventory), and we specifically asked each participant whether they would choose to be a psychiatrist again if they could. RESULTS: Among 3,783 psychiatrists we surveyed, one-quarter responded that they would not choose to be a psychiatrist again if they had a choice, with less than half (47.2%) saying they would. Those who would not choose psychiatry again were more likely to have a negative (relative to positive) professional identity (OR = 7.47, P＜0.001, 95%CI: 4.587-12.164); experience job burnout (OR = 2.945, P＜0.001, 95%CI: 2.356-3.681); be dissatisfied with their job (OR = 2.739, P＜0.001, 95%CI: 2.102-3.569) and excessive regulation (OR = 1.819, P＜0.001, 95%CI: 1.487-2.226); have a heavy workload (OR = 1.749, P＜0.001, 95%CI: 1.423-2.149) or a lower income (OR = 1.748, P＜0.001, 95%CI: 1.415-2.161); be married (relative to single) (OR = 1.604, P = 0.004, 95%CI: 1.165-2.208); be dissatisfied with strained doctor-patient relationship (OR = 1.333, P = 0.005, 95%CI: 1.089-1.632); have more night shifts per month (OR = 1.055, P = 0.021, 95%CI: 1.008-1.104) or work longer hours per week (OR = 1.016, P = 0.001, 95%CI: 1.006-1.025). CONCLUSION: Among psychiatrists in tertiary hospitals in China, those with a heavier workload, poor sense of professional identity, job dissatisfaction, and burnout were less likely to choose psychiatry again. Policymakers and hospital administrators need to take effective measures to improve psychiatrists' sense of professional identity and increase their intention to stay.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".