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Resilience in the operating room: developing and testing of a resilience model
Author(s) -
Gillespie Brigid M.,
Chaboyer Wendy,
Wallis Marianne,
Grimbeek Peter
Publication year - 2007
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/j.1365-2648.2007.04340.x
Subject(s) - coping (psychology) , competence (human resources) , psychology , psychological resilience , self efficacy , variables , applied psychology , social psychology , clinical psychology , computer science , machine learning
Title. Resilience in the operating room: developing and testing of a resilience modelAim. This paper is a report of a study to examine the relation of perceived competence, collaboration, control, self‐efficacy, hope, coping, age, experience, education and years of employment to resilience in operating room (OR) nurses. Background. Resilience is viewed as a vital attribute for nurses because it augments adaptation in demanding and volatile clinical environments such as ORs. However, there has been little research into the utility of resilience as a means of dealing with workplace stress, and there is only limited understanding of variables that explain resilience in the context of nursing. Method. A correlational cross‐sectional survey design was used. Of a national sample of 2860 Australian OR nurses, 1430 were selected by systematic random sampling and invited to complete a questionnaire in 2006. The instrument included scales measuring perceived competence, collaboration, control, self‐efficacy, hope, coping and resilience, and gathered information about the demographic characteristics of respondents. Results. Two regression models were used to develop a model of resilience. An initial model tested the hypothesis that a set of 12 explanatory variables contributed to resilience in OR nurses. Five variables (hope, self‐efficacy, coping, control and competence) explained resilience at statistically significant levels. Age, experience, education and years of employment did not contribute to resilience at statistically significant levels. The final model explained 60% of the variance. In both models, the strongest explanatory variables were hope, self‐efficacy and coping. Conclusion. Identification of explanatory variables that contribute to resilience in ORs may assist in implementing strategies that promote these behaviours, and thus retain nurses in this specialty.