Prediction of violence or threat of violence among employees in social work, healthcare and education: the Finnish Public Sector cohort study

Abstract

<p><strong>Objectives: </strong>To develop a risk prediction algorithm for identifying work units with increased risk of violence in the workplace.</p><p><strong>Design: </strong>Prospective cohort study.</p><p><strong>Setting: </strong>Public sector employees in Finland.</p><p><strong>Participants: </strong>18 540 nurses, social and youth workers, and teachers from 4276 work units who completed a survey on work characteristics, including prevalence and frequency of workplace violence/threat of violence at baseline in 2018-2019 and at follow-up in 2020-2021. Those who reported daily or weekly exposure to violence or threat of violence daily at baseline were excluded.</p><p><strong>Exposures: </strong>Mean scores of responses to 87 survey items at baseline were calculated for each work unit, and those scores were then assigned to each employee within that work unit. The scores measured sociodemographic characteristics and work characteristics of the work unit.</p><p><strong>Primary outcome measure: </strong>Increase in workplace violence between baseline and follow-up (0=no increase, 1=increase).</p><p><strong>Results: </strong>A total of 7% (323/4487) of the registered nurses, 15% (457/3109) of the practical nurses, 5% of the social and youth workers (162/3442) and 5% of the teachers (360/7502) reported more frequent violence/threat of violence at follow-up than at baseline. The area under the curve values estimating the prediction accuracy of the prediction models were 0.72 for social and youth workers, 0.67 for nurses, and 0.63 for teachers. The risk prediction model for registered nurses included five work unit characteristics associated with more frequent violence at follow-up. The model for practical nurses included six characteristics, the model for social and youth workers seven characteristics and the model for teachers included four characteristics statistically significantly associated with higher likelihood of increased violence.</p><p><strong>Conclusions: </strong>The generated risk prediction models identified employees working in work units with high likelihood of future workplace violence with reasonable accuracy. These survey-based algorithms can be used to target interventions to prevent workplace violence.</p>