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|Main Title:||Nurse forecasting in Europe (RN4CAST)|
|Subtitle:||Rationale, design and methodology|
Aiken, Linda H.
Van den Heede, Koen
Rafferty, Anne Marie
Moreno-Casbas, Maria Teresa
Scott, Anne P.
|Abstract:||Background: Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care. Methods/Design: A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences. This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce. Discussion: RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe.|
|DDC Class:||610 Medizin und Gesundheit|
|Creative Commons License:||https://creativecommons.org/licenses/by/2.0/|
|Journal Title:||BMC Nursing|
|Notes:||First published by BioMed Central: Sermeus, Walter; Aiken, Linda H.; Van den Heede, Koen; Rafferty, Anne Marie; Griffiths, Peter; Moreno-Casbas, Maria Teresa; Busse, Reinhard; Lindqvist, Rikard; Scott, Anne P.; Bruyneel, Luk; Brzostek, Tomasz; Kinnunen, Juha; Schubert, Maria; Schoonhoven, Lisette; Zikos, Dimitrios: Nurse forecasting in Europe (RN4CAST) : rationale, design and methodology. - In: BMC Nursing. - ISSN 1472-6955 (online). - 10 (2011), art. 6. - doi:10.1186/1472-6955-10-6.|
|Appears in Collections:||Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 3 Prozesswissenschaften » Institut für Biotechnologie » Publications|
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