Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis
Abstract Background The willingness of healthcare workers (HCW) to respond is an important factor in the health system’s response capacity during emergencies. Although much research has been devoted to exploring this issue, the statistical methods employed have been predominantly traditional and hav...
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doaj-7bccaa828efd4bfaa0dfceb8a3533f8c2020-11-25T03:39:19ZengBMCBMC Medical Education1472-69202019-05-0119111210.1186/s12909-019-1561-7Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysisStav Shapira0Michael Friger1Yaron Bar-Dayan2Limor Aharonson-Daniel3PREPARED Center for Emergency Response Research, Ben-Gurion University of the NegevDepartment of Public Health, Faculty of Health Sciences, Ben-Gurion University of the NegevPREPARED Center for Emergency Response Research, Ben-Gurion University of the NegevPREPARED Center for Emergency Response Research, Ben-Gurion University of the NegevAbstract Background The willingness of healthcare workers (HCW) to respond is an important factor in the health system’s response capacity during emergencies. Although much research has been devoted to exploring this issue, the statistical methods employed have been predominantly traditional and have not enabled in-depth analysis focused on absenteeism-prone employees during emergencies. The present study employs an innovative statistical approach for modeling HCWs’ willingness to respond (WTR) following an earthquake. Methods A validated questionnaire measuring knowledge, perceptions, and attitudes toward an earthquake scenario was distributed among Israeli HCWs in a hospital setting. Two regression models were employed for data analysis – a traditional linear model, and a quantile regression model that makes it possible to examine associations between explanatory variables across different levels of a dependent variable. A supplementary analysis was performed for selected variables using broken line spline regression. Results Females under the age of forty, and nurses were the most absenteeism-prone sub-groups of employees (showed low WTR) in earthquake events. Professional commitment to care and perception of efficacy were the most powerful predictors associated with WTR across all quantiles. Both marital status (married) and concern for family wellbeing, designated as statistically significant in the linear model, were found to be statistically significant in only one of the WTR quantiles (the former in Q10 and the latter in Q50). Gender and number of children, which were not significantly associated with WTR in the linear model, were found to be statistically significant in the 25th quantile of WTR. Conclusions This study contributes to both methodological and practical aspects. Quantile regression provides a more comprehensive view of associations between variables than is afforded by linear regression alone. Adopting an advanced statistical approach in WTR modeling can facilitate effective implementation of research findings in the field.http://link.springer.com/article/10.1186/s12909-019-1561-7Health personnelDisaster planningStatistical modelsEarthquakesAbsenteeism |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Stav Shapira Michael Friger Yaron Bar-Dayan Limor Aharonson-Daniel |
spellingShingle |
Stav Shapira Michael Friger Yaron Bar-Dayan Limor Aharonson-Daniel Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis BMC Medical Education Health personnel Disaster planning Statistical models Earthquakes Absenteeism |
author_facet |
Stav Shapira Michael Friger Yaron Bar-Dayan Limor Aharonson-Daniel |
author_sort |
Stav Shapira |
title |
Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
title_short |
Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
title_full |
Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
title_fullStr |
Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
title_full_unstemmed |
Healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
title_sort |
healthcare workers’ willingness to respond following a disaster: a novel statistical approach toward data analysis |
publisher |
BMC |
series |
BMC Medical Education |
issn |
1472-6920 |
publishDate |
2019-05-01 |
description |
Abstract Background The willingness of healthcare workers (HCW) to respond is an important factor in the health system’s response capacity during emergencies. Although much research has been devoted to exploring this issue, the statistical methods employed have been predominantly traditional and have not enabled in-depth analysis focused on absenteeism-prone employees during emergencies. The present study employs an innovative statistical approach for modeling HCWs’ willingness to respond (WTR) following an earthquake. Methods A validated questionnaire measuring knowledge, perceptions, and attitudes toward an earthquake scenario was distributed among Israeli HCWs in a hospital setting. Two regression models were employed for data analysis – a traditional linear model, and a quantile regression model that makes it possible to examine associations between explanatory variables across different levels of a dependent variable. A supplementary analysis was performed for selected variables using broken line spline regression. Results Females under the age of forty, and nurses were the most absenteeism-prone sub-groups of employees (showed low WTR) in earthquake events. Professional commitment to care and perception of efficacy were the most powerful predictors associated with WTR across all quantiles. Both marital status (married) and concern for family wellbeing, designated as statistically significant in the linear model, were found to be statistically significant in only one of the WTR quantiles (the former in Q10 and the latter in Q50). Gender and number of children, which were not significantly associated with WTR in the linear model, were found to be statistically significant in the 25th quantile of WTR. Conclusions This study contributes to both methodological and practical aspects. Quantile regression provides a more comprehensive view of associations between variables than is afforded by linear regression alone. Adopting an advanced statistical approach in WTR modeling can facilitate effective implementation of research findings in the field. |
topic |
Health personnel Disaster planning Statistical models Earthquakes Absenteeism |
url |
http://link.springer.com/article/10.1186/s12909-019-1561-7 |
work_keys_str_mv |
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