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Tuesday, November 6, 2007

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This presentation is part of : Strategies and Models for Caring for the Nurse
Factors Influencing on Nurse Fatigue
Areewan Oumtanee, RN, PhD, School of Nursing, Chulalongkorn University, Pratumwan, Thailand and Kantaporn Yodchai, RN, MSN, Medical Nursing Department, Faculty of Nursing, Prince of Songkla University, Hat Yai, Songkla, Thailand.
Learning Objective #1: gain knowledge on nurse fatigue in Thailand
Learning Objective #2: learn predictors influencing on nurse fatigue

Fatigue is a significant threat to patient safety and nurse retention.  However, nurse fatigue is an ignore issue although nurses voice that they often work very  hard.   There has been little known about the level of fatigue of Thai nurses working in a unit.    Thus, the purposes of this study  were to identify the level of nurse fatigue, relating variables,  and predictors of fatigue of staff nurses in Thailand.   Three hundred and seventy-eight staff nurses working in a  tertiary hospital  were selected in the study.  Variables selected to study were personal factor (age), work factors (sufficient staffing and rotation shifts), quality of sleep, sleepinees, and fatique. Study instruments were personal factor, work factors, Epworth Sleepiness Score, Quality of Sleep Scale, and  Fatique Scale. Those scales were tested for content validity and internal reliability. The reliability with alpha chronbach of  Epworth Sleepiness Score, Quality of Sleep Scale, and   Fatique Scale  were .88, .82, and.95, respectively.  All data were analyzed by using mean, standard deviation, t-test, and stepwise  regression.
        The major findings were as follows:
1.      The mean scores of fatigue  were moderate level (mean = 4.36, SD = 1.8,   moderate level =  ranging scores from 4.00-6.99).
2.      Rotation shifts were not related to fatigue.
3.      Sleepiness  was positively related to fatigue (r = .311). 
4.      Quality of sleep, sufficient staffing, and age  were negatively retated to fatigue (r = -.530, -.190  and -.187 respectively) .
5.      The predictors of fatigue were quality of sleep, sleepiness, and sufficient staffing.  Those accounted for 35.4 % of variance.