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Updated: Aug 5, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education
Published on: June 21, 2010
The value of pilot and feasibility studies in nursing education research: a worked example
Dhanya Anand Anil Kumar1, Natasya Raja Azlan1, Hugh Davies1
1School of Nursing and Midwifery, Edith Cowan University, Joondalup, Western Australia, Australia.
Background:
Feasibility studies play a critical role in determining whether clinical or educational interventions can be implemented as intended before progressing to large-scale trials. In nursing education, many innovative teaching strategies, particularly complex interventions such as simulation-based learning, are often advanced directly to full trials without first establishing feasibility. This premature progression creates recruitment, acceptability, fidelity, and protocol adherence challenges, which may compromise study quality and valuable resources. This paper addresses this gap by clarifying the purpose and value of feasibility studies in nursing education research and outlining key considerations for designing an early-stage feasibility study that aligns with contemporary evidence and best-practice methodological guidance.
Methods:
A practical, step-by-step methodology is presented for designing rigorous early- stage feasibility studies in nursing education, based on a worked example, structured around the four phases of the Medical Research Council Framework for complex interventions: (1) Development, (2) Feasibility and Piloting, (3) Evaluation, and (4) Implementation Planning.
Implication:
As a methodological paper, we focus on insights rather than empirical outcomes. Examples from the working feasibility trial demonstrate how feasibility data informs judgments about readiness for a larger study. Funding bodies increasingly value feasibility evidence when assessing readiness for large-scale trials.
Conclusion:
This methodological paper highlights key considerations for evaluating feasibility and demonstrates how preliminary feasibility data guides decisions about if a study is ready to progress to a larger-scale trial. The examples provided illustrate how feasibility indicators, rather than effect size estimates or hypothesis-driven analyses, support transparent and appropriate decision-making in early-phase research.
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