Contextually Appropriate Nurse Staffing Models - a Realist Review

The nursing profession is the largest regulated healthcare profession, and effective nurse staffing is critical for quality and cost containment. However, the current nurse staffing literature shows that the same staffing model in various contexts leads to different outcomes for clients, professionals and the health system. Together with an international research team we will use a realist lens to determine “what staffing model works, for whom in what circumstances, how and why”. 


This research project aims to conduct a realist review to determine how nurse staffing models in which contexts and through what mechanisms produce outcomes for clients, professionals and the health system. 


The results of this research project guide the collaboration between researchers and decision-makers to produce recommendations for appropriate implementation of nurse staffing models in identified contexts in order to obtain desired outcomes for clients, professionals and the health system.


01 May 2020 - 01 April 2023


The study encompasses three overlapping iterative phases. In the first phase, the literature is mapped and focus groups are held. In the second phase, the research questions are refined. In the last phase program theories are defined as hypothetical explanatory accounts of how different nurse staffing models intends to work. 


This research project is funded by the Canadian Institutes of Health Research (CIHR, case number 426039). 

HU researchers involved in the research

Related research groups

Collaboration with knowledge partners

The principal applicants are Dr. Cummings (University of Alberta), Dr. Booth (University of Sheffield), Dr. Harvey (University of Adelaide), Dr. Kitson (Flinders University of South Australia) and Dr. Raymond (University of Alberta).

Would you like to collaborate or doe you have a question?

Inge Wolbers | Researcher | Research group Chronic Diseases

Inge Wolbers

  • Researcher
  • Research group: Chronic Diseases