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Quantifying the impact of environment factors on the risk of medical responders’ stress-related absenteeism

Quantifying the impact of environment factors on the risk of medical responders’ stress-related absenteeism
Quantifying the impact of environment factors on the risk of medical responders’ stress-related absenteeism
Medical emergency response staff are exposed to incidents which may involve high-acuity patients or some intractable or traumatic situations. Previous studies on emergency response staff stress-related absence have focused on perceived factors and their impacts on absence leave. To date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. We show that these approaches ignore environment data, such as stress factors. The increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data-driven approach to quantify the effect of environment variables on the risk of stress-related absenteeism.
We propose a two-stage data-driven framework to identify the variables of importance and to quantify their impact on medical staff stress-related risk of absenteeism. First, machine learning techniques are applied to identify the importance of different stressors on staff stress-related risk of absenteeism. Second, the Cox Proportional-Hazards Model is applied to estimate the relative risk of each stressor. Four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the Emergency Services and completion of the safeguarding form. We discuss counterintuitive results and implications to policy.
0272-4332
1834-1851
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Chen, Zhiyin
08f8ac01-603c-4d76-ba7b-033d7d906c29
Wise, James
03a91e3c-f115-48de-8a8d-16758b005acb
Mortimore, Simon
1b426007-1a5e-485e-9421-f00256d34631
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Chen, Zhiyin
08f8ac01-603c-4d76-ba7b-033d7d906c29
Wise, James
03a91e3c-f115-48de-8a8d-16758b005acb
Mortimore, Simon
1b426007-1a5e-485e-9421-f00256d34631

Brito, Mario, Chen, Zhiyin, Wise, James and Mortimore, Simon (2022) Quantifying the impact of environment factors on the risk of medical responders’ stress-related absenteeism. Risk Analysis, 42 (8), 1834-1851. (doi:10.1111/risa.13909).

Record type: Article

Abstract

Medical emergency response staff are exposed to incidents which may involve high-acuity patients or some intractable or traumatic situations. Previous studies on emergency response staff stress-related absence have focused on perceived factors and their impacts on absence leave. To date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. We show that these approaches ignore environment data, such as stress factors. The increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data-driven approach to quantify the effect of environment variables on the risk of stress-related absenteeism.
We propose a two-stage data-driven framework to identify the variables of importance and to quantify their impact on medical staff stress-related risk of absenteeism. First, machine learning techniques are applied to identify the importance of different stressors on staff stress-related risk of absenteeism. Second, the Cox Proportional-Hazards Model is applied to estimate the relative risk of each stressor. Four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the Emergency Services and completion of the safeguarding form. We discuss counterintuitive results and implications to policy.

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Quantifying the impact of environment factors on the risk of medical responders stress-related absenteeism - Accepted Manuscript
Restricted to Repository staff only until 17 February 2024.
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Accepted/In Press date: 17 February 2022
Published date: August 2022
Additional Information: Publisher Copyright: © 2022 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.

Identifiers

Local EPrints ID: 454731
URI: http://eprints.soton.ac.uk/id/eprint/454731
ISSN: 0272-4332
PURE UUID: 85352900-1ccd-45e7-8220-d373e0ccdd4c
ORCID for Mario Brito: ORCID iD orcid.org/0000-0002-1779-4535

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Date deposited: 22 Feb 2022 17:35
Last modified: 24 Aug 2022 01:41

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Contributors

Author: Mario Brito ORCID iD
Author: Zhiyin Chen
Author: James Wise
Author: Simon Mortimore

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