Nurse managers, in addition to developing technical skills, need to develop skills to manage human relationships to prevent bullying and turnover among nurses.

Background The increase in violence against health professionals in the COVID-19 pandemic makes it necessary to identify the predictors of violence, in order to prevent these events from happening. Objective Evaluating the prevalence and analyzing the variables involved in the occurrence of violence against health professionals during the COVID-19 pandemic in Brazil. Method This is a cross-sectional study conducted online involving Brazilian health professionals during the COVID-19 pandemic. The data were collected through a structured questionnaire (Google Online Form) sent to health professionals on social networks and analyzed through logistic regression by using sociodemographic variables. The set of grouped variables was assigned to the final model when p <0.05. A network was built using the Mixed Graph Models (MGM) approach. A centrality measurement chart was constructed to determine which nodes have the greatest influence, strength and connectivity between the nodes around them. Results The predictors of violence in the adjusted regression model were the following: being a nursing technician / assistant; having been working for less than 20 years; working for over 37 hours a week; having suffered violence before the pandemic; having been contaminated with COVID-19; working in direct contact with patients infected by the virus; and having family members who have suffered violence. The network created with professionals who suffered violence demonstrated that the aggressions occurred mainly in the workplace, with an indication of psycho-verbal violence. In cases in which the aggressors were close people, aggressions were non-verbal and happened both in public and private places. The assaults practiced by strangers occurred in public places. Conclusions Violence against health professionals occurs implicitly and explicitly, with consequences that can affect both their psychosocial well-being and the assistance given to their patients and families.

Objective: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná , southern Brazil, and evaluate their association with socioeconomic and spatial determinants. Methods: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. Results: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná , and significantly influenced the formation of high-risk SMR clusters. Conclusion: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.

ObjectiveEvaluate disparities in a Brazilian state by conducting an analysis to determine whether socioeconomic status was associated with the reported intimate partner sexual violence (IPSV) rates against women.DesignA retrospective, ecological study.SettingsData retrieved from the Notifiable Diseases Information System database of the Ministry of Health of Brazil.ParticipantsAll cases of IPSV (n=516) against women aged 15–49 years reported in the Notifiable Diseases Information System between 2009 and 2014.Outcome measuresThe data were evaluated through an exploratory analysis of spatial data.ResultsWe identified a positive spatial self-correlation in the IPSV rate (0.7105, P≤0.001). Five high–high-type clusters were identified, predominantly in the Metropolitan, West, South Central, Southwest, Southeast and North Central mesoregions, with only one cluster identified in the North Pioneer mesoregion. Our findings also indicated that the associations between the IPSV rate and socioeconomic predictors (women with higher education, civil registry of legal separations, economically active women, demographic density and average female income) were significantly spatially non-stationary; thus, the regression coefficients verified that certain variables in the model were associated with the IPSV rate in some regions of the state. In addition, the geographically weighted regression (GWR) model improved the understanding of the associations between socioeconomic indicators and the IPSV notification rate, showing a better adjustment than the ordinary least square (OLS) model (OLS vs GWR model: R2: 0.95 vs 0.99; Akaike information criterion: 4117.90 vs 3550.61; Moran’s I: 0.0905 vs −0.0273, respectively).ConclusionsIPSV against women was heterogeneous in the state of Paraná. The GWR model showed a better fit and enabled the analysis of the distribution of each indicator in the state, which demonstrated the utility of this model for the study of IPSV dynamics and the indication of local determinants of IPSV notification rates.

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