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Development of an early warning resilience survey for healthcare organizations
OBJECTIVE: To design and validate a brief set of measures identifying staff and work areas exhibiting low levels of resilience within healthcare organizations. DATA SOURCES/STUDY DESIGN: Primary data were gathered via survey administration between April and August of 2016 from 33,622 respondents acr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839017/ https://www.ncbi.nlm.nih.gov/pubmed/31720458 http://dx.doi.org/10.1016/j.heliyon.2019.e02670 |
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author | Morgan, Kristopher H. Libby, Nicholas E. Weaver, Amy K. Cai, Cindy |
author_facet | Morgan, Kristopher H. Libby, Nicholas E. Weaver, Amy K. Cai, Cindy |
author_sort | Morgan, Kristopher H. |
collection | PubMed |
description | OBJECTIVE: To design and validate a brief set of measures identifying staff and work areas exhibiting low levels of resilience within healthcare organizations. DATA SOURCES/STUDY DESIGN: Primary data were gathered via survey administration between April and August of 2016 from 33,622 respondents across 123 facilities. These surveys included pilot items designed to measure resilience and were administered to all employees alongside employee engagement surveys. DATA COLLECTION/EXTRACTION METHODS: Following the data collection period for the pilot survey, data from all organizations were integrated into a single analytical dataset. Factor analyses were used to determine the underlying constructs of healthcare worker resilience. Cronbach's alpha and correlation analyses tested the internal consistency and validity of the instrument. PRINCIPAL FINDINGS: A brief set consisting of eight items was identified as a psychometrically validated measure of resilience. This measure consists of two subscales, Activation and Decompression. These measures exist independent of employee engagement, indicating an empirical distinction between the two concepts. Resilience was found to predict 38% of variance in engagement scores. CONCLUSIONS: An eight-item instrument can accurately measure resilience to identify burnout risk and serve as a predictor of other workforce outcomes such as engagement. |
format | Online Article Text |
id | pubmed-6839017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-68390172019-11-12 Development of an early warning resilience survey for healthcare organizations Morgan, Kristopher H. Libby, Nicholas E. Weaver, Amy K. Cai, Cindy Heliyon Article OBJECTIVE: To design and validate a brief set of measures identifying staff and work areas exhibiting low levels of resilience within healthcare organizations. DATA SOURCES/STUDY DESIGN: Primary data were gathered via survey administration between April and August of 2016 from 33,622 respondents across 123 facilities. These surveys included pilot items designed to measure resilience and were administered to all employees alongside employee engagement surveys. DATA COLLECTION/EXTRACTION METHODS: Following the data collection period for the pilot survey, data from all organizations were integrated into a single analytical dataset. Factor analyses were used to determine the underlying constructs of healthcare worker resilience. Cronbach's alpha and correlation analyses tested the internal consistency and validity of the instrument. PRINCIPAL FINDINGS: A brief set consisting of eight items was identified as a psychometrically validated measure of resilience. This measure consists of two subscales, Activation and Decompression. These measures exist independent of employee engagement, indicating an empirical distinction between the two concepts. Resilience was found to predict 38% of variance in engagement scores. CONCLUSIONS: An eight-item instrument can accurately measure resilience to identify burnout risk and serve as a predictor of other workforce outcomes such as engagement. Elsevier 2019-11-01 /pmc/articles/PMC6839017/ /pubmed/31720458 http://dx.doi.org/10.1016/j.heliyon.2019.e02670 Text en © 2019 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Morgan, Kristopher H. Libby, Nicholas E. Weaver, Amy K. Cai, Cindy Development of an early warning resilience survey for healthcare organizations |
title | Development of an early warning resilience survey for healthcare organizations |
title_full | Development of an early warning resilience survey for healthcare organizations |
title_fullStr | Development of an early warning resilience survey for healthcare organizations |
title_full_unstemmed | Development of an early warning resilience survey for healthcare organizations |
title_short | Development of an early warning resilience survey for healthcare organizations |
title_sort | development of an early warning resilience survey for healthcare organizations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839017/ https://www.ncbi.nlm.nih.gov/pubmed/31720458 http://dx.doi.org/10.1016/j.heliyon.2019.e02670 |
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