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Record W4389333371 · doi:10.1186/s13033-023-00614-6

Would you choose to be a psychiatrist again? A large-sample nationwide survey of psychiatrists and psychiatry residents in China

2023· article· en· W4389333371 on OpenAlexaboutno aff
Mengyue Gu, Liucheng Zheng, Jingyang Gu, Song Wang, Yudong Shi, Feng Jiang, Huanzhong Liu, Yi‐lang Tang

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutWorkloadWorkforceMental healthJob satisfactionMedicinePsychiatryQuarter (Canadian coin)Health administrationAttritionPsychologyFamily medicineNursingClinical psychologyPublic healthSocial psychologyManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.471
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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