Background: The Occupational Fatigue Exhaustion/Recovery Scale (OFER) was designed to assess occupational fatigue in nurses. Although the original English version of this instrument has shown high degrees of reliability and validity, a Chinese version of this scale has yet to be verified. Purpose: The aim of this study was to evaluate the psychometric properties of the OFER in a population of Chinese nurses. Methods: The scale was translated using translation and back-translation. The validities and reliabilities were evaluated on 923 qualified participants using content validity index, concurrent validity, factorial validity, internal consistency reliability, and test–retest reliability. Results: The content validity index for the OFER was .92. The correlation coefficients between the scores of the OFER subscales and the criteria in this study (varying from −.498 to .705) verified that the OFER has acceptable concurrent validity. Principal component analysis and confirmatory factor analysis revealed that three factors correspond to the structure of the original instrument and that recovery mediates the relationship between acute and chronic fatigue. The Cronbach’s alpha for the chronic fatigue, acute fatigue, and intershift recovery subscales were .83, .85, and .86, respectively. Test–retest reliabilities with correlation coefficients from .61 to .78 were found in the three subscales. Conclusions/Implications for Practice: OFER is a reliable and valid instrument for assessing work-related fatigue in Chinese nurses. However, further improvement of the acute fatigue subscale is recommended. The OFER has the potential to elicit information that is useful for assessing fatigue in nurses in China. Furthermore, as it differentiates between acute and chronic fatigue, OFER may be an effective tool for guiding the development and implementation of various, related intervention measures.

Background: Most of the previous studies on nursing practice environment and job burnout employed conventional analyses ignoring the impact of unit-level data clusters. This study addressed this gap by examining the effects of the nursing practice environments on dimensions of occupational burnout among a sample of Chinese nurses using multilevel logistic regression models and demonstrating the superiority of employing multilevel models over conventional models within this context. Methods: A proportionate stratified sampling method was applied in this cross-sectional study that invited 1,300 registered nurses (RNs) from nine clinical units of a large, academic hospital in urban China to complete the questionnaire. Nurse-reported information was obtained using the Practice Environment Scale of the Nursing Work Index (PES-NWI) and the Maslach Burnout Inventory (MBI). Findings: A total of 1,178 valid questionnaires were returned for a response rate of 90.62%. RNs generally perceived their nursing practice environment as favorable as measured by the PES-NWI. Approximately 40% of the respondents reported experiencing emotional exhaustion and depersonalization. The multivariate models indicated that nurse burnout was significantly associated with nurse participation in hospital affairs, nursing foundations for quality of care, and adequate staffing. In addition, our results illustrated the advantage of multilevel modeling over the conventional modeling for handling hierarchical data in terms of the accuracy of the estimates and the goodness-of-fit of the model. Conclusions/Application to Practice: These findings underscore the importance of measures aimed at enhancing nursing practice environments to prevent RNs from experiencing feelings of burnout and of considering multilevel analysis in future nursing research.

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BackgroundGood sleep is essential to human health. Insufficient quality sleep may compromise the wellness of nurses and even jeopardize the safety of patients. Although the contributors of sleep quality in nurses have been previously studied, the direct and indirect effects of modifiable work-related predictors remain uncertain.PurposeThe study was designed to explore the direct and indirect effects of modifiable work-related factors on sleep quality in Chinese nurses.MethodsA multistage sampling method was employed in this cross-sectional study to recruit 923 participants. An evidence-based predicting model was postulated and then subsequently tested and optimized using path analysis.ResultsThe final model fit the data well, with the involved predictors accounting for 34.1% of the variance in sleep quality of the participants. Shift work, job demands, exposure to hazards in work environments, chronic fatigue, and inter-shift recovery were identified as direct predictors, while whereas job satisfaction, job control, support at work, and acute fatigue were identified as indirect predictors.Conclusions/Implications for PracticeSleep quality in Chinese nurses is influenced directly and indirectly by various modifiable work-related factors. Interventions such as adjusting work shifts and reducing job burdens should be prioritized by administrative staff to ensure the sleep quality and clinical performance of Chinese nurses and to subsequently improve nursing care quality.

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