Abstract
Background and Purpose: The purpose of this study was to develop a measure that would adequately and sensitively measure the occupational stress experience of nurses working in very remote health care facilities. Because no existing nursing stress tool is suitable to assess the unique stressors of remote nursing practice, the aim was to address this gap in psychometric measurement capacity and develop the Remote Area Nursing Stress Scale (RANSS). Method: A focus group (n = 19) of remote area nurses identified potential questionnaire items through open discussion and by later listing the stressors they experienced individually in their day-to-day functioning. Subsequently, the Delphi method was employed to further refine the questionnaire (n = 12 experts). The RANSS was successfully pilot tested and was afterward administered to nurses working in very remote Australia in 2008 (n = 349) and in 2010 (n 5 433). Results: Principal components analysis and confirmatory factor analysis were performed for both waves of survey administration, demonstrating a robust 7-factor structure consistent across samples and accounting for significant variance in dependent measures. Conclusion: The development and validation of the RANSS is a significant advancement in remote area nursing research. The RANSS should be administered on an ongoing basis to monitor occupational stress among nurses working in very remote Australia. The RANSS should also be administered internationally in countries that also accommodate remote health care facilities. This would determine whether the RANSS is a psychometrically valid stress measure beyond the context of very remote Australia.
Original language | English |
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Pages (from-to) | 246-263 |
Number of pages | 18 |
Journal | Journal of Nursing Measurement |
Volume | 21 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2013 |
Keywords
- Confirmatory factor analysis (CFA)
- Delphi method
- Principal component analysis (PCA)
- Remote area nursing stress scale (RANSS)
- Structural equation modelling (SEM)