Inhalt des Dokuments
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) |
Zusammenfassung
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 .