Objective: To explore the interrelationships between mental vulnerability, cognitive emotion regulation strategies, and positive psychological capital in medical postgraduates, so as to provide precise intervention targets for mental health services in universities. Methods: From April to June 2025, a convenience sampling method was used to select 1 461 medical postgraduates from three colleges in Hubei Province. Participants were assessed using the Mental Vulnerability Questionnaire (MVQ), the Cognitive Emotion Regulation Questionnaire-Chinese Version (CERQ-C) and the Positive Psychological Capital Questionnaire (PPQ). The network analysis model was constructed using a Gaussian graphical model. Results: The network analysis revealed that the edge connection between somatic symptoms and mental symptoms was the strongest, followed by the connection between refocus on planning and positive reappraisal within cognitive emotion regulation, with edge weights of 0.69 and 0.59, respectively. Interpersonal problems within psychological vulnerability exhibited the highest expected influence( EI =1.27), serving as the core node of the network. Rumination within cognitive emotion regulation demonstrated the highest bridge expected influence( BEI =0.33), playing a critical connecting role among psychological vulnerability, cognitive emotion regulation, and positive psychological capital. Conclusion In the mental health network of medical postgraduates, mental vulnerability, cognitive emotion regulation, and positive psychological capital interact through their respective key dimensions, jointly affect overall mental health.

Objective: To analyze the factors associated with high level fear of negative evaluation (FNE) among junior high school students and to construct a nomogram risk prediction model, so as to provide scientific tools for psychological health intervention for junior high school students. Methods: A convenience sampling combined with cluster random sampling method was used to select 5 485 junior high school students from 4 cities (Wuhan, Huanggang, Xianning and Xiaogan) for an online questionnaire survey in March 2025. The total sample was randomly split into a training set ( n =3 839) and a validation set ( n =1 646). Univariate analysis was performed in the training set using Chi-square test and t-test. Variables with statistical significance were subsequently included in multivariate Logistic regression to identify independent predictors and to construct a nomogram based risk prediction model. The discriminative ability and clinical utility of the model were evaluated in the validation set using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: There were 1 649 junior high school students with low level FNE and 2 190 with high level FNE in the training set. The self control ability of junior high school students with lowlevel and high level FNE showed a statistically significant difference (23.96±3.96, 21.48±3.37, t=25.15, P < 0.01 ). Statistically significant differences in the detection rate of high level FNE were observed among junior high school students with different genders, family types, parenting styles, academic rankings, psychological flexibility, mobile phone addiction tendencies, emotional management training, exercise frequency, left behind experiences, and places of origin ( χ 2=82.01- 1 126.68 , all P <0.01). The results of Logistic regression analysis revealed that, the following factors were identified as significant factors influencing high level FNE among junior high school students:exercise frequency ( OR=0.21, 95%CI =0.17-0.26); parenting style ( OR=0.48, 95%CI =0.40-0.58); left behind experience ( OR=3.88, 95%CI =3.27-4.61); smartphone addiction proneness ( OR=2.19, 95%CI =0.89-0.93); self-control ability ( OR=0.91, 95%CI =0.89-0.93); and psychological flexibility ( OR=0.16, 95%CI =0.10-0.28) (all P <0.05). The AUC for the training and validation set were 0.88 (95% CI =0.87-0.89) and 0.87 (95% CI =0.85-0.89), respectively. The Hosmer-Lemeshow goodness of fit test yielded χ 2=8.57, 15.20 (both P >0.05). Conclusion The risk prediction model with high level FNE demonstrates good accuracy and can assist educators and parents in timely screening of junior high school students with high level FNE, thereby providing a basis for implementing targeted interventions.