Predictors of resilience among registered nurses at three private hospitals in South Lebanon -

Abstract

Resilience has an impact on both nurses’ job satisfaction and retention. Since high resilience increase nurses’ ability to cope with stresses related to job adversities and consequently enhances their job satisfaction, it is worth studying the predictors of resilience among Lebanese registered nurses. Controversies regarding predictors of resilience in the literature make this topic important to investigate. Moreover, the deficiency of data regarding this issue in Lebanon impacts national registered nurses’ shortage, and makes it favorable for selection. The purpose of this study was to assess the average resilience scores (RS)and predictors of resilience among registered nurses at three private hospitals in South Lebanon. A related objective was to explore the relationship between resilience and registered nurses’ perception of transformational leadership (GTLS). A cross sectional study design was used to determine the predictors of resilience among registered nurses in three private hospitals in Saida. A convenience sample of 240 registered nurses who have been working at the three hospitals for more than one year were surveyed. The survey instrument included the True Resilience Scale (C), Global Transformational Leadership Scale and demographic questions. A multiple linear regression model was used to assess predictors of resilience. The response rate was 85percent. The mean resilience score was 119 (SD 15.3) and the nurses’ perception of their leadership as transformational score was 25 (SD 6.8). Statistically significant higher resilience scores were reported among nurse managers, nurses with more than five years of experience and those working in critical nursing units compared to bedside nurses, nurses with less than five year experience and those working in non-critical units, respectively (p0.05). A moderate statistically significant correlation (r=0.53, p0.05) between perception of GTL and resilience was detected. In the multiple linear regression model, 30percent of the variat