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While artificial intelligence (AI) was first developed in the late 1950s
This article is the first in a series exploring realist research, a methodological approach well suited to the complexity of nursing practice. Unlike traditional approaches such as randomised controlled trials (RCTs) and systematic reviews, which focus on whether interventions work, realist research examines how and why interventions work when implemented in specific groups; reflecting the individualised care nurses provide. By introducing the key concepts of realist research, this article highlights its relevance to nursing and lays the groundwork for using realist research to drive meaningful improvements in healthcare.
Realist research offers a unique lens to examine the complexity of healthcare delivery. While traditional research methods often seek to determine if interventions work or not in controlled environments, realist research seeks to explain how, why, for whom and under what circumstances interventions succeed—or fail—in real-world settings.
Critically evaluating the evidence, in particular research evidence, which underpins practice, is central to quality care and service improvements. Systematically appraising research includes assessing the rigour with which methods were undertaken and factors that may have biased findings. This article will outline what bias means in relation to research, why it is important to consider bias when appraising research and describe common types of bias across research processes. We will also offer strategies that researchers can undertake to minimise bias.
The Critical Appraisal Skills Programme (CASP) describes bias in research as ‘systematic errors that can occur at any stage of the research process’ and can have a ‘significant impact on the reliability and validity of the findings’ that may lead to a distortion of the conclusions.
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