Abstract
Objectives
An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning?
Methods
Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis.
Results
Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing.
Conclusions
Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.
Mosaics for Visualizing Knowledge Structures
