Data_Sheet_1_Optimizing the Practice Environment for Medical Staff in the Post-pandemic Era: A Discrete Choice Experiment.pdf

ObjectiveThis study aimed to elicit the stated job preferences of Chinese medical staff in the post-pandemic era and identify the relative importance of different factors in the practice environment.MethodsWe used an online discrete choice experiment (DCE) survey instrument to elicit the job preferences of medical staff (doctors and nurses) in tertiary hospitals in Anhui, China. Attributes and levels were generated using qualitative methods, and four attributes were considered: career development, workload, respect from society, and monthly income. A set of profiles was created using a D-efficient design. The data were analyzed considering potential preference heterogeneity, using the conditional logit model and the latent class logit (LCL) model.ResultsA total of 789 valid questionnaires were included in the analysis, with an effective response rate of 73.33%. Career development, workload, respect from society, and monthly income were significant factors that influenced job preferences. Three classes were identified based on the LCL model, and preference heterogeneity among different medical staff was demonstrated. Class 1 (16.17%) and Class 2 (43.51%) valued respect from society most, whereas Class 3 (40.32%) prioritized monthly income. We found that when respect from society was raised to a satisfactory level (50–75% positive reviews), the probability of medical staff choosing a certain job increased by 69.9%.ConclusionRespect from society was the most preferred attribute, while workload, monthly income, and career development were all key factors in the medical staff's job choices. The heterogeneity of the medical professionals' preferences shows that effective policy interventions should be customized to accommodate these drive preferences.

本研究旨在探求后疫情时代中国医护人员的明确职业偏好，并识别实践中不同因素的相关重要性。方法上，我们采用在线离散选择实验（DCE）调查工具，收集了安徽省三级医院医护人员（医生和护士）的职业偏好。属性和水平通过定性方法生成，考虑了四个属性：职业发展、工作负荷、社会尊重和月收入。使用D高效设计创建了一系列配置文件。在分析数据时，考虑到潜在偏好的异质性，采用了条件逻辑模型和潜在类别逻辑（LCL）模型。结果显示，共有789份有效问卷被纳入分析，有效回复率为73.33%。职业发展、工作负荷、社会尊重和月收入是影响职业偏好的显著因素。基于LCL模型，识别出三个类别，并展示了不同医护人员之间的偏好异质性。类别1（16.17%）和类别2（43.51%）最重视社会尊重，而类别3（40.32%）则更看重月收入。我们发现，当社会尊重达到令人满意的水平（50-75%正面评价）时，医护人员选择特定工作的概率增加了69.9%。结论表明，社会尊重是最受偏好的属性，而工作负荷、月收入和职业发展均是医护人员职业选择的关键因素。医疗专业人士偏好的异质性表明，有效的政策干预应针对这些驱动偏好进行定制。