Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

Objective: To explore the differences of sensory manifestations between ASD children and typical development children, and to clarify the characteristics of sensory abnormalities in ASD and their relationship with various clinical symptoms, so as to provide scientific basis for early identification and specific intervention. Methods: A total of 265 ASD children who received rehabilitation training in autism rehabilitation institutions in Heilongjiang Province were collected as the case group, and 223 typical development children in ordinary kindergartens and schools in Harbin were taken as the control group. Short Sensory Profile (SSP) was used to evaluate the difference of children s sensory perception level between the two groups, and Social Response Scale (SRS) and Autism Behavior Checklist (ABC) were used to evaluate the severity of symptoms including social disorder of autistic children. The correlation between SSP scores in ASD group and clinical scales was analyzed. Results: The comparison of SSP scores between the two groups found that the median scores of all sensory dimensions in ASD group (tactile=33, taste/smell=18, motion sensitivity=13, Low response/sensation seeking=28, auditory filtering=19, low strength=22, visual/auditory=20) were lower than those of the healthy control group( Z =-2.73,-4.36,-3.17,-5.09,-11.00,-10.45,-3.43, P <0.05). The abnormal rate of multisensory score in children in ASD group was 55.1%, and that in control group was 21.2%, with significant difference( χ 2=57.15, P <0.05). Correlation analysis showed that SSP score in ASD group was negatively correlated with all dimensions of SRS, nonverbal communication, and social function of ADI-R scale, ADOS communication and social interaction, and total scores of ABC and CARS( P < 0.05 ). Conclusion Children with ASD have atypical sensory experiences, especially in auditory filtering dimension, and the level of atypical sensation is related to the severity of clinical symptoms of autism. In the future clinical diagnosis, treatment and research, it is necessary to strengthen the ability to recognize the sensory symptoms of children with ASD, so as to realize the early diagnosis and intervention.