Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
International journal of nursing education scholarship
Effect of concept mapping on critical thinking skills in nursing students: a systematic review with meta-analysis
BACKGROUND: Critical thinking (CT) among nursing students is crucial for delivering high-quality nursing care to patients. Concept mapping (CM) is one of the evidence-based pedagogies that helps to improve CT in individuals. OBJECTIVES: The objective of this review and meta-analysis was to evaluate the impact of concept mapping on CT abilities, academic achievement, and clinical skills in nursing students. METHODS: This systematic review and meta-analysis aimed to identify published articles on the effect of CM on CT in nursing students, written in English and published from 2011 to 2024. The articles were sourced from SCOPUS, Web of Science (WoS), ScienceDirect, Google Scholar, and PubMed databases. To ensure a comprehensive and relevant search, the following MeSH terms were used: "Effectiveness" OR "Impact," AND "Concept Mapping," AND "CT," AND "Nursing Student," AND "RCT" OR "Randomized Controlled Trials" OR "Quasi-Experimental." This study used the Cochrane Collaboration's Risk of Bias (RoB) tool and Standardized Mean Difference (SMD) for outcome measurement, with results analyzed using RevMan Web. RESULTS: This study analyzed 14 RCTs and quasi-experimental studies involving 1,747 participants (E=870, C=877). The meta-analysis found that concept mapping enhanced nursing students' CT skills (MD=4.86, 95 % CI [2.16 to 7.56], p=0.0004), academic achievements (MD=3.38, 95 % CI [0.76 to 5.99], p=0.01), and performance in clinical practice (MD=5.94, 95 % CI [3.22 to 8.65], p<0.0001) when compared to routine instructional methods. CONCLUSIONS: CM provides valuable insights into enhancing CT and clinical skills in nursing students, eventually preparing them to become excellent nursing professionals in the healthcare system.
26 July 2026
Read appraisal →Gut and liver
Data-Informed Intuition in Hepatology: Integrating Evidence, Context, and Clinical Reasoning
Artificial intelligence and data-driven models are changing hepatology, but expert clinical judgment remains essential. Liver diseases are complex and evolve over time, requiring reasoning that links numerical data with clinical context. We propose data-informed intuition (DII), a conceptual framework bridging evidence-based medicine with clinical experience and reflective reasoning. Instead of presenting a validated prediction model, it proposes DII as an integrative scaffold for clinical reasoning that can be examined and revised. DII is viewed as a trainable skill that improves with data feedback, not mere guesswork. Clinicians continually calibrate their judgment as patient information accumulates. In hepatology, DII helps interpret disease trajectories, adapt predictive scores to context, and handle uncertainty in liver transplantation, hepatocellular carcinoma, acute liver failure, and related conditions. The DII framework describes eight axes (time, rate, magnitude, pattern, context, causality, integration, and uncertainty) that make visible how data and intuition interact in clinical reasoning. Integrating DII into fellowship training and artificial intelligence-assisted systems could nurture clinicians who combine algorithmic precision with human insight. This perspective summarizes the conceptual basis, clinical uses, and educational value of DII, and outlines both a research agenda for its empirical testing and a path toward data-informed professionalism in hepatology.
15 July 2026
Read appraisal →JMIR medical education
Developing and Validating a Coding Scheme for Clinical Reasoning in History Taking Using Generative AI-Based Virtual Patients: Systematic Text Condensation Approach
BACKGROUND: Effective history taking helps clinicians identify key symptoms and form accurate hypotheses. Generative artificial intelligence (GenAI)-based virtual patients (VPs) are increasingly used to simulate and practice history taking. However, there is currently no straightforward approach to effectively identify students' clinical reasoning activities during these interactions, which limits the ability to provide instructional feedback. OBJECTIVE: This study aims to develop and validate a coding scheme to identify medical students' history-taking behaviors during interactions with GenAI-based VPs. METHODS: Second-year medical students (N=210) participated in 5 history-taking cases with GenAI-based VPs, yielding 1030 dialogues. Researchers applied the systematic text condensation method to these dialogue data from cases 1 to 4 to inductively develop a coding scheme and validate coding consistency. Subsequently, the dialogue data from case 5 were used to assess the correlation between the students' history-taking behaviors and their academic performance, including diagnostic accuracy, history-taking checklist scores, clinical knowledge test scores, and postencounter form scores. RESULTS: A coding scheme comprising 12 behaviors across 3 dimensions-clinical reasoning behaviors, information gathering behaviors, and social interaction behaviors-was developed with high interrater reliability (κ≥0.85). The correlation analysis revealed that key clinical reasoning behaviors, such as summarizing and integrating and logical organization, showed significant positive correlations with multiple performance metrics, underscoring their importance in fostering clinical competence. In contrast, information gathering behaviors such as specifying symptoms and routine question were associated with clinical knowledge and thoroughness of history taking but were less predictive of diagnostic accuracy. CONCLUSIONS: This study developed a reliable, theory-informed coding scheme that can identify students' questioning behaviors during history taking with GenAI-based VPs. The scheme effectively captures higher-order cognitive strategies and provides valuable insights into the development of clinical reasoning in medical students. This approach offers a scalable and efficient way to integrate real-time feedback into future medical education, fostering personalized learning and advancing competency-based assessments in clinical training.
14 Apr 2026
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