ObjectiveTo systematically evaluate the types of interventions, the application of outcome measures, and the reporting quality of randomized controlled trials (RCTs) in medical education, and to provide evidence-based support for improving study design and optimizing evaluation frameworks in this field. MethodsPubMed, Embase, CBM, CNKI, and WanFang Data databases were systematically searched from inception to April 2025. A total of 340 medical education RCTs were included through stepwise random sampling and screening. Descriptive statistics were used to summarize publication characteristics, participant types, intervention classifications, and distributions of outcome measures. Regression analysis was conducted to identify factors associated with reporting quality. ResultsRCTs of medical education were included, with the majority originating from China (n=284). Participants were predominantly undergraduate clinical medical students, and sample sizes were mainly small to moderate (20-60 participants). Cognitive-oriented interventions accounted for the largest proportion (76.03%), and control group designs were highly homogeneous. A total of 843 outcome measures were identified, primarily focusing on short-term outcomes such as theoretical knowledge and clinical skills, whereas long-term outcomes (e.g., behavioral changes and patient-related outcomes) were markedly underrepresented. Overall reporting quality was suboptimal, with inadequate implementation of blinding and allocation concealment. Multivariable regression analysis indicated that explicit specification of primary outcomes, study region, and intervention type were significant determinants of reporting quality. ConclusionSignificant gaps remain in intervention design, outcome selection, and methodological reporting in medical education RCTs, particularly in the development of long-term outcome measures and adherence to key methodological standards. This study systematically synthesizes existing evidence and identifies key factors influencing reporting quality, providing empirical support for improving RCT design, standardizing outcome measure systems, and enhancing methodological rigor in medical education research. The findings also offer important implications for educational practice and policy-making.
ObjectiveTo investigate the network structure of comorbid depression and anxiety symptoms among medical staff and analyze differences across institutional types. MethodsA convenience sampling method was used to select medical staff from medical institutions at various levels in Guang'an City as participants between August 10 and 15, 2024. General demographic questionnaires, the Chinese version of the Patient Health Questionnaire (PHQ-9) for depression screening, and the Chinese version of the Generalized Anxiety Disorder Scale (GAD-7) were used to survey them. The study aimed to analyze the influencing factors of anxiety and depression and construct a network model. Predictability, bridging strength, and node strength were used to assess the network structure. The non-parametric bootstrap method was employed to evaluate the accuracy and stability of the network, and finally, a Network Comparison Test (NCT) was used to examine the impact of different levels of healthcare institutions on the network model. ResultsA total of 889 participants were included in the study. The analysis showed that the incidence of depressive symptoms (PHQ-9≥5) among healthcare workers was 44.88%, while the incidence of anxiety symptoms (GAD-7≥5) was 43.98%, with a comorbidity rate of 36.67%. Network analysis revealed that the top three symptoms with the highest node strength were difficulty relaxing (A4), excessive worry (A3), and fatigue (D4). The top three symptoms with the highest bridging strength were irritability/anger (A6), fatigue (D4), and worrying about terrible things happening (A7). The different levels of healthcare institutions did not have a significant impact on the network model. ConclusionThe central symptoms (such as difficulty relaxing, excessive worry, and fatigue) and key bridging symptoms (such as irritability/anger, fatigue, and worrying about terrible things happening) in the anxiety and depression symptom network can serve as potential intervention targets for healthcare workers at risk of depressive and anxiety symptoms.