Hospital mortality and optimality of nursing workload: A study on the predictive validity of the RAFAELA Nursing Intensity and Staffing system

Jaana K. Junttila; Aija Koivu; Lisbeth Fagerström; Kaisa Haatainen; Pirkko Nykänen

doi:10.1016/j.ijnurstu.2016.03.008

Hospital mortality and optimality of nursing workload: A study on the predictive validity of the RAFAELA Nursing Intensity and Staffing system

Jaana K. Junttila, Aija Koivu, Lisbeth Fagerström, Kaisa Haatainen, Pirkko Nykänen

Research output: Contribution to journal › Article › Scientific › peer-review

35 Citations (Scopus)

Abstract

Conclusions: Hospital mortality can be predicted by the RAFAELA system. This study rendered additional confirmation for the predictive validity of this patient classification system. In future, larger studies with a wider variety of nurse sensitive outcomes and multiple risk adjustments are needed. Future research should also focus on other important criteria for an adequate nursing workforce management tool such as simplicity, efficiency and acceptability. (C) 2016 Elsevier Ltd. All rights reserved.

Original language	Undefined/Unknown
Pages (from-to)	46–53
Number of pages	8
Journal	International Journal of Nursing Studies
Volume	60
DOIs	https://doi.org/10.1016/j.ijnurstu.2016.03.008
Publication status	Published - 2016
MoE publication type	A1 Journal article-refereed

Keywords

Hospital mortality
Nursing intensity
Nursing workload
Patient classification system
Quantitative research
Scale development

Access to Document

10.1016/j.ijnurstu.2016.03.008

Cite this

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abstract = "Conclusions: Hospital mortality can be predicted by the RAFAELA system. This study rendered additional confirmation for the predictive validity of this patient classification system. In future, larger studies with a wider variety of nurse sensitive outcomes and multiple risk adjustments are needed. Future research should also focus on other important criteria for an adequate nursing workforce management tool such as simplicity, efficiency and acceptability. (C) 2016 Elsevier Ltd. All rights reserved.",

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AU - Haatainen, Kaisa

AU - Nykänen, Pirkko

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