Page Content
Nurse Forecasting in Europe (RN4CAST): Rationale, design and methodology.
Autor | Sermeus W,
Aiken LH, Van den Heede K, Rafferty AM, Griffiths P, Moreno-Casbas MT,
Busse R, Lindqvist R, Scott AP, Bruyneel L, Brzostek T, Kinnunen J,
Schubert M, Schoonhoven L, Zikos
D |
Verlag | BMC Nursing 10:6
(DOI:10.1186/1472-6955-10-6) |
Abstract
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 .