Dataset Information

Classification and Prediction of Violence Against Chinese Medical Staff on the Sina Microblog Based on a Self-Organizing Map: Quantitative Study.

ABSTRACT: BACKGROUND:For 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. 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. METHODS:For 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. RESULTS:Trends 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. 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. 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.

SUBMITTER: Duan G

PROVIDER: S-EPMC7284412 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Classification and Prediction of Violence Against Chinese Medical Staff on the Sina Microblog Based on a Self-Organizing Map: Quantitative Study.

Duan Guimin G Liao Xin X Yu Weiping W Li Guihua G

Journal of medical Internet research 20200526 5

<h4>Background</h4>For 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.<h4>Objective</h4>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 ...[more]

PMID: 32348253

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Project description:IntroductionWith the normalization of COVID-19 prevention and control, a large number of intergenerational audiences with different cognition preferences and value orientations have started to pour into non-acquaintance virtual communities (VCs) to address their social needs by disclosing their own thoughts, feelings and experiences toward certain topics. To avoid the negative impacts of self-disclosure, this study introduced the concept of social integration into cyber society among non-acquaintance VCs, such as the topic-based VCs. Our theoretical model considers both the psychological antecedents and consequences of VC audiences' social integration and our findings have implications for public online (and even offline) social life. Moreover, this research could play a guiding role in improving VC audiences' social integration status in future online learning and telecommuting scenarios.MethodTo assess the theoretical model constructed in this manuscript, we conducted an online survey in two different topic-based VCs among Microblog and yielded 472 useable responses from intergenerational audiences, among which 28.81% were born before 1985, 26.67% were born from 1985 to 1995, and 48.52% were born after 1995. Our sample consisted of 208 individuals from Health Regimen VC and 264 individuals from Star Chasing VC, 200 (42.37%) were men and 272 (57.63%) were women.ResultsOur structural equation model (SEM) indicated that individuals' self-disclosure in topic-based VCs might not directly guide them to acquire social integration. However, intimacy and cognitive communion derived from VC audiences' self-disclosure might not only enhance their social integration, but also improve their psychological well-being. In addition, VC audiences' social integration mediated the relationship between intimacy and psychological well-being, and the relationship between cognitive communion and psychological well-being. Moreover, VC audiences' intimacy was found to have a direct influence on their cognitive communion.ConclusionIn the context of topic-based VCs, audiences' self-disclosure could significantly foster their intimacy and cognitive communion with others, and both intimacy and cognitive communion are conductive to VC audiences' social integration. Thus, audiences in topic-based VCs who wish to improve their psychological well-being need to disclose themselves and build corresponding psychological foundations (i.e., intimacy and cognitive communion) to enhance their social integration. Meanwhile, topic-based VCs should pay attention to the cultivation of intimacy and cognitive communion among audiences while encouraging them to reveal themselves.

Project description:BackgroundWorkplace violence against medical staff in China is a widespread problem that has negative impacts on medical service delivery. The study aimed to contribute to the prevention of workplace violence against medical staff in China by identifying patterns of workplace violence, key risk factors, and the interplay of risk factors that result in workplace violence.MethodsNinety-seven publicly reported Chinese healthcare violent incidents from late 2013 to 2017 were retrospectively collected from the internet and analysed using content analysis. A modified socio-ecological model guided analysis of the violent incidents focusing on risk.ResultsPhysical violence, yinao, or a combination of physical and verbal violence were the typical forms of violence reported. The findings identified risk at all levels. Individual level risk factors included service users' unreasonable expectations, limited health literacy, mistrust towards medical staff, and inadequacy of medical staff's communication during the medical encounter. Organisational level risk factors under the purview of hospital management included problems with job design and service provision system, inadequacies with environmental design, security measures, and violence response mechanisms within hospitals. Societal level risk factors included lack of established medical dispute-handling mechanisms, problems in legislation, lack of trust and basic health literacy among service users. Situational level risks were contingent on risk factors on the other levels: individual, organisational, and societal.ConclusionsInterventions at individual, situational, organisational, and societal levels are needed to systematically address workplace violence against medical staff in China. Specifically, improving health literacy can empower patients, increase trust in medical staff and lead to more positive user experiences. Organizational-level interventions include improving human resource management and service delivery systems, as well as providing training on de-escalation and violence response for medical staff. Addressing risks at the societal level through legislative changes and health reforms is also necessary to ensure medical staff safety and improve medical care in China.

Project description:ObjectiveThis study aimed to investigate the mental state of medical staff and medical students in the early stages of the SARS-CoV-2 outbreak, as well as analyze the risk factors of serious mental illness (SMI), so as to provide a scientific basis for further psychological intervention and management.MethodA cross-sectional survey was conducted from February 2-7, 2020. The Kessler 6 Psychological Distress Scale and a general information questionnaire were administered on-line to a convenience sample of 548 medical staff and medical students in China. Multivariate binary logistic regression analysis was used to screen the risk factors of SMI in medical staff and medical students.ResultsOf the 505 respondents in the final analysis, 188 (37.23%) were at high risk of SMI. Respondents were at significantly higher risk of SMI if they had been suspected of being infected with the SARS-CoV-2 (OR = 7.00, 95% CI: 1.19-41.14), had relatives suspected of being infected with the SARS-CoV-2 (OR = 23.60, 95% CI: 1.11-501.30), felt concerned towards media coverage of outbreak-related information (OR = 11.95, 95% CI: 3.07-46.57), recently dreamed related to SARS-CoV-2 (OR = 4.21, 95% CI: 2.22-8.01), experienced difficulty in controlling emotions during SARS-CoV-2 epidemic (OR = 3.25, 95% CI: 1.66-6.37), or spent hours watching outbreaks per day (OR = 1.29, 95% CI: 1.13-1.46).ConclusionOur findings highlight that medical staff and medical students were vulnerable to SMI during the early stages of the SARS-CoV-2 outbreak and identify the factors associated with SMI which can be used to formulate psychological interventions to improve the mental health. The independent risk factors for SMI among them are suspicion that they or relatives were infected with the SARS-CoV-2, greater interest in media reports about the epidemic, frequency of recent dreams related to SARS-CoV-2, difficulty in controlling emotions during the epidemic, and hours spent watching outbreaks per day.

Dataset Information

Classification and Prediction of Violence Against Chinese Medical Staff on the Sina Microblog Based on a Self-Organizing Map: Quantitative Study.

Publications

Classification and Prediction of Violence Against Chinese Medical Staff on the Sina Microblog Based on a Self-Organizing Map: Quantitative Study.

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