An Interactive Model of Assault on Staff Members in Inpatient Treatment Facilities

Date

2014-12

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Abstract

Effectiveness of psychosocial treatment programming in inpatient treatment facilities is limited by violence against staff members perpetrated by seriously mentally ill residents. The goal of this study was to analyze a model in which characteristics of staff members interact with aversive staff-resident interactions to predict assault in inpatient treatment facilities. Data were analyzed from 541 staff members who worked at a forensic psychiatric hospital between 1997 and 2007. Bootstrapping analyses revealed that older staff members and nurses who engaged in higher rates of negative nonsocial responses to appropriate resident behavior were at increased risk of assault. Staff members who were younger, male, psychiatric aides, nurses, and less experienced and who also engaged in higher rates of negative nonsocial responses to inappropriate resident behavior were also at increased risk of assault. Finally, engaging in lower rates of negative verbal responses to resident behavior placed staff members who worked in the Rehabilitation Services department and staff members who were more experienced at increased risk of assault. Characteristics of staff members that have previously been found to predict assault, such as age and length of employment, did not do so in this study; however, the variance accounted for by each of the models tested was significant, ranging from 13 to 16 percent. These results suggest that staff training should focus on negative nonsocial and negative verbal responses to resident behavior, teaching staff members how to set limits and deny requests in a way that minimizes the potentially negative impact on residents. Staff members should be taught how to respond to inappropriate resident behavior in a way that is consistent with the treatment programs in which they work.

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Keywords

Inpatient treatment facilities, Assault, Staff members, Staff-resident interactions, Observational assessment

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