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A model for predicting acute and chronic fatigue in Chinese nurses.

fang j., qiu c., xu h. & you g. (2013) A model for predicting acute and chronic fatigue in Chinese nurses. Journal of Advanced Nursing 69(3), 546-558. doi: 10.1111/j.1365-2648.2012.06029.x Abstract Aim. To explore a model designed to examine direct and indirect effects of variables on acute and chr...

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Published in:Journal of Advanced Nursing (John Wiley & Sons, Inc.) Vol. 69; no. 3; pp. 546 - 559
Main Authors: Fang, Jinbo, Qiu, Changjian, Xu, Huiqiong, You, Guiying
Format: Journal Article
Published: John Wiley & Sons, Inc. Mar2013
Online Access:View this record in EBSCOhost
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      jtl: Journal of Advanced Nursing (John Wiley & Sons, Inc.)
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      dt: Mar2013
      vid: 69
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      pub: John Wiley & Sons, Inc.
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        atl: A model for predicting acute and chronic fatigue in Chinese nurses.
      aug:
        au:
          Fang, Jinbo
          Qiu, Changjian
          Xu, Huiqiong
          You, Guiying
        affil: Jinbo Fang PhD RN Associate professor Nursing Department, West China Hospital, SiChuan University, Chengdu City, China
      sug:
        subj:
          Models, Theoretical
          Fatigue
          Occupational Health
          Nursing Staff, Hospital
          Funding Source
          Cross Sectional Studies
          Descriptive Research
          Correlational Studies
          China
          Questionnaires
          Path Analysis
          Analysis of Variance
          Workload
          Occupational Hazards
          Occupational Exposure
          Work Environment
          Sleep
          Shiftwork
          Shift Workers
          Anxiety
          Depression
          Stress, Occupational
          Job Satisfaction
          Simple Random Sample
          Data Analysis Software
          Scales
          Summated Rating Scaling
          Coefficient alpha
          Recovery
          Test-Retest Reliability
          Pearson's Correlation Coefficient
          Adult
          Middle Age
          Marital Status
          Educational Status
          Descriptive Statistics
          Job Experience
          Chi Square Test
          Multiple Logistic Regression
          Human
          Adult: 19-44 years
          Middle Aged: 45-64 years
      ab: fang j., qiu c., xu h. & you g. (2013) A model for predicting acute and chronic fatigue in Chinese nurses. Journal of Advanced Nursing 69(3), 546-558. doi: 10.1111/j.1365-2648.2012.06029.x Abstract Aim. To explore a model designed to examine direct and indirect effects of variables on acute and chronic fatigue. Background. Although influencing factors for fatigue have been studied extensively, direct and indirect effects of predictors for acute and chronic fatigue remain poorly understood. Therefore, we proposed an original prediction model for acute and chronic fatigue in Chinese nurses based on previous studies. Design. Cross-sectional, descriptive, correlational design. Methods. We used a multi-stage sampling process to finally include 581 nurses working in the general hospitals in Chengdu, China. Data were collected between November 2007-March 2008 by using questionnaires. A path analysis was applied to test the fit of the hypothesized model and analysis of variance. Results. The final model fits the data well. A total of 44·8% of variance in acute fatigue was directly and indirectly explained by job demand, job control, support at work, exposure to hazards in work environments and sleep quality, and only indirectly influenced by intershift recovery and shift work. Moreover, 61·5% of total chronic fatigue was directly and indirectly accounted for by anxiety, depression, job dissatisfaction, intershift recovery, and acute fatigue, and only indirectly influenced by job control, support at work, exposure to hazards in work environments, shift work, and sleep quality. Conclusion. Acute and chronic fatigue is affected by different factors and nurse managers should generate specific interventions to decrease them.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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