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AbstractIntroduction: Sickness absence (SA) is problematic in occupations requiring 24/7 coverage where one person's SA cascades into more work days, longer shift durations and elevated fatigued states for remaining workers. As part of this dissertation, a systematic literature review found strong evidence that fatigue increased the risk of SA in the workforce. Few studies examined this relationship in nurses, despite reported high fatigue and differences in shiftwork characteristics. Fatigue-risk scores generated from bio-mathematical fatigue models are popular in safety-sensitive industries and may be useful for assessing and monitoring fatigue on nursing units and predicting SA. Purpose: The purpose of this study was to explore prospective associations between work-related fatigue, bio-mathematically modeled fatigue-risk and SA in 12-hour shift hospital nurses. Methods: Two studies were conducted that used retrospective cohort design of hospital nurses representing four nursing units from a major pediatric hospital. Baseline data on work-related fatigue were from Fatigue Risk, Alertness Management Effectiveness (FRAME) study (n=40) using the self-reported Occupational Fatigue Exhaustion Recovery Scale. Data on fatigue-risk scores were generated from work-rest schedules of 197 nurses working 41,538 shifts using Fatigue Audit InterDyne (FAID) and Fatigue Risk Index (FRI) software programs. Work-related fatigue and fatigue-risk scores were then linked to SA data that were extracted from the hospital's attendance system. The statistical approach was generalized linear mixed models that account for non-independency of repeated measures. Results: The SA rate in both studies was ~5%. Among FRAME participants, for every 1SD increase in acute fatigue scores, nurses were 1.29 times more likely to be absent from work (OR=1.29, 95%CI=1.02-1.63). In the bio-mathematical model study, when FAID-scores were moderate (scores=41-79, OR=1.38, 95%CI=1.21-1.58) or high (scores=81-150, OR=1.67, 95%CI=1.42-1.95), nurses were more likely to take SA than nurses with lower (<41) scores. Similarly, when FRI-scores were >60, nurses were 1.58 times (95%CI=1.05-2.37) more likely to take SA compared to nurses with lower scores. Conclusion: Work-related acute fatigue and fatigue-risk modeled bio-mathematically significantly predicted nurses' SA. While surveys are instrumental in identifying the nature and severity of fatigue, bio-mathematical fatigue models may be more practical to monitor for day-to-day fatigue changes in the workplace.
DescriptionUniversity of Maryland, Baltimore. Nursing. Ph.D. 2017
Keywordbio-mathematical fatigue models
Nursing Staff, Hospital