TY - JOUR
T1 - Nurse forecasting in Europe (RN4CAST)
T2 - Rationale, design and methodology
AU - Sermeus, Walter
AU - Aiken, Linda H.
AU - Van den Heede, Koen
AU - Rafferty, Anne M.
AU - Griffiths, Peter
AU - Moreno-Casbas, Maria T.
AU - Busse, Reinhard
AU - Lindqvist, Rikard
AU - Scott, Anne P.
AU - Bruyneel, Luk
AU - Brzostek, Tomasz
AU - Kinnunen, Juha
AU - Schubert, Maria
AU - Schoonhoven, Lisette
AU - Zikos, Dimitrios
N1 - Funding Information:
The project has been granted financial support from the European Commission. Depending on national legislation, the study protocol was approved by either central ethical committees (e.g. nation or university) or local ethical committees (e.g. hospitals). Proof of the ethical approvals has been submitted to the editorial board of this journal for verification. The consortium has developed strict criteria (included in the project proposal and additional internal documents) regarding the sampling of nurses and patients, the storage, flows and access of the data to safeguard the security, privacy and confidentiality.
Funding Information:
The RN4CAST Consortium consists of Walter Sermeus, Koen Van den Heede, Luk Bruyneel, Emmanuel Lesaffre, Luwis Diya (Belgium, Catholic University Leuven); Linda Aiken, Herbert Smith, Timothy Cheney, Douglas Sloane (USA, University of Pennsylvania); Juha Kinnunen, Anneli Ensio, Virpi Jylhä (Finland, University of Eastern Finland); Reinhard Busse, Britta Zander (Germany, Technical University Berlin); John Mantas, Dimitrios Zikos (Greece, University of Athens); Anne Scott, Anne Matthews, Anthony Staines (Ireland, Dublin City University); Ingeborg Strømseng Sjetne (Norway, Norwegian Knowledge Center for the Health Services); Tomasz Brzostek, Maria Kózka, Piotr Brzyski, Lucyna Przewoźniak, Anna Ksykiewicz-Dorota (Poland, Jagiellonian University Medical College); Teresa Moreno-Casbas, Carmen Fuentelsaz-Gallego, Esther Gonzalez-María, Mónica Contreras-Moreira (Spain, Institute of Health Carlos III); Carol Tishelman, Rikard Lindqvist, Sara Runesdotter, Lisa Smeds (Sweden, Karolinska Institute); Sabina De Geest, Maria Schubert, René Schwendimann (Switzerland, Basel University); Maud Heinen, Lisette Schoonhoven, Theo van Achterberg (The Netherlands, Radboud University Nijmegen Medical Centre); Peter Griffiths (England, University of Southampton); Jane Ball, Simon Jones, Brian McIntosh, Anne Marie Rafferty (England, King’s College London). The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 223468. For more information on the RN4CAST project, please visit http://www.rn4cast.eu.
PY - 2011/4/18
Y1 - 2011/4/18
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79955107761&partnerID=8YFLogxK
U2 - 10.1186/1472-6955-10-6
DO - 10.1186/1472-6955-10-6
M3 - Article
AN - SCOPUS:79955107761
SN - 1472-6955
VL - 10
JO - BMC Nursing
JF - BMC Nursing
M1 - 6
ER -