Center for Health Services and Nursing Research, Katholieke Universiteit Leuven, Kapucijnenvoer 35/4, 3000 Leuven, Belgium.
BMC Nurs. 2011 Apr 18;10:6. doi: 10.1186/1472-6955-10-6.
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.
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.
目前的护理人力资源规划模型不可靠且效率低下，因为它们只考虑了工作量，而忽略了对患者护理质量的影响。RN4CAST 项目旨在通过创新的预测方法来解决问题，不仅要考虑工作量，还要考虑护理人员的质量和患者护理的质量。
方法/设计：本研究采用多国家、多层次的横断面设计，以获取预测模型中重要的未测量因素，包括医院工作环境的特征如何影响护士的招聘、留用和患者的结果。在参与的 12 个欧洲国家中，每个国家都至少抽取了 30 家综合急性医院。通过四个数据源（护士、患者和组织调查以及常规收集的医院出院数据）收集数据。对每个医院随机选择的医疗和外科病房的所有在职护士进行调查。护士调查的目的是衡量护士在工作中的经历（如工作满意度、倦怠），并允许创建人员配备和工作条件的综合医院水平措施。在部分国家和医院，通过一天的普查方法组织患者调查，以衡量患者对医疗和护理的体验。除了进行患者调查外，还将使用医院出院摘要数据集来计算其他患者结局，如住院死亡率和抢救失败。通过组织调查，收集有关组织概况的信息（如床位数、可用技术类型、教学状态），以控制分析中的机构差异。将通过共同标识符对这些信息进行链接，并通过多级回归类型分析研究护理工作环境的不同方面与患者和护士结局之间的关系。这些结果将用于模拟改变护理工作环境的不同方面对护理质量和护理人员满意度的影响。
RN4CAST 是欧洲有史以来进行的最大规模的护士劳动力研究之一，将提高预测模型的准确性，并为欧洲更有效地管理护理资源提供新方法。
