Abstract
BackgroundFor the last decade, doctor-patient contradiction in China has remained prominent, and workplace violence toward medical staff still occurs frequently. However, little is known about the types and laws of propagation of violence against medical staff online.ObjectiveBy using a self-organizing map (SOM), we aimed to explore the microblog propagation law for violent incidents in China that involve medical staff, to classify the types of incidents and provide a basis for rapidly and accurately predicting trends in public opinion and developing corresponding measures to improve the relationship between doctors and patients.MethodsFor this study, we selected 60 cases of violent incidents in China involving medical staff that led to heated discussions on the Sina microblog from 2011 to 2018, searched the web data of the microblog using crawler software, recorded the amount of new tweets every 2 hours, and used the SOM neural network to cluster the number of tweets. Polynomial and exponential functions in MATLAB software were applied to predict and analyze the data.ResultsTrends in the propagation of online public opinion regarding the violent incidents were categorized into 8 types: bluff, waterfall, zigzag, steep, abrupt, wave, steep slope, and long slope. The communications exhibited different characteristics. The prediction effect of 4 types of incidents (ie, bluff, waterfall, zigzag, and steep slope) was good and accorded with actual spreading trends.ConclusionsOur study found that the more serious the consequences of a violent incident, such as a serious injury or death, the more attention it drew on the microblog, the faster was its propagation speed, and the longer was its duration. In these cases, the propagation types were mostly steep slope, long slope, and zigzag. In addition, the more serious the consequences of a violent incident, the higher popularity it exhibited on the microblog. The popularity within a week was significantly higher for acts resulting from patients’ dissatisfaction with treatments than for acts resulting from nontherapeutic incidents.
Highlights
BackgroundAt the beginning of 2009, China started a new round of medical and health system reformation
For hospitals and health management departments, understanding the communication types and the characteristics of the violent incidents being discussed on microblogs such as the Sina microblog can be helpful to institutions for scientifically predicting the propagation rules of incidents, for effectively managing and guiding public opinion relating to doctors and patients, and for improving the doctor-patient relationship
By the application of the self-organizing map (SOM) neural network algorithm, 60 medical incidents against Chinese medical staff were divided into 8 types, based on the number of dynamic changes in the Sina microblog tweets
Summary
BackgroundAt the beginning of 2009, China started a new round of medical and health system reformation. Doctor-patient contradiction in China has remained prominent, and workplace violence toward medical staff still occurs frequently. Objective: By using a self-organizing map (SOM), we aimed to explore the microblog propagation law for violent incidents in China that involve medical staff, to classify the types of incidents and provide a basis for rapidly and accurately predicting trends in public opinion and developing corresponding measures to improve the relationship between doctors and patients. Conclusions: Our study found that the more serious the consequences of a violent incident, such as a serious injury or death, the more attention it drew on the microblog, the faster was its propagation speed, and the longer was its duration In these cases, the propagation types were mostly steep slope, long slope, and zigzag. The popularity within a week was significantly higher for acts resulting from patients’ dissatisfaction with treatments than for acts resulting from nontherapeutic incidents
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