Evidence map›Paper›PMID 39780160›Full record

ArticleBMC medical education2025

Developing clinical reasoning along the cognitive continuum: a mixed methods evaluation of a novel Clinical Diagnosis Assessment.

Lucinda E Ainge, Amanda K Edgar, Jacqueline M Kirkman, James A Armitage

Abstract read
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Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Lucinda E AingeDeakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia. Lucy.ainge@deakin.edu.au.
Amanda K EdgarDeakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Jacqueline M KirkmanDeakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
James A ArmitageDeakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties. Therefore, we designed the Clinical Diagnosis Assessment, an assessment for learning, to explicitly train Type 1 and Type 2 reasoning skills in optometry students. Decision justification was included to prompt reflective practice and expose metacognitive reasoning. By manipulating assessment task features, we aimed to broaden students' experience with clinical reasoning strategies and identify specific clinical reasoning challenges.

methodsThe Clinical Diagnosis Assessment was designed with two parts; to explicitly train Type 1 and Type 2 clinical reasoning strategies respectively. A validated clinical reasoning measure, the Diagnostic Thinking Inventory for Optometry was used to measure change in clinical reasoning skill. Clinical Diagnosis Assessment and Diagnostic Thinking Inventory scores were quantitatively compared against written exams and OSCEs. Focus groups explored student perception and approach to the assessment.

resultsOut of a total of 67 second-year Optometry students, 55 students (82%) completed the Diagnostic Thinking Inventory for Optometry, 50 students (75%) consented to use of assessment scores and 17 students (26%) participated in focus groups. Quantitative analysis indicated modest but significant Pearson coefficients of determination between Clinical Diagnosis Assessment and OSCE performance (p < .05, r

conclusionsThe assessment exposed students underlying thinking, allowing identification of a range of clinical reasoning difficulties such as semantic transformation, prioritization, management planning, cognitive bias and managing diagnostic uncertainty. Better understanding of clinical reasoning difficulties will allow targeted remediation and inform curriculum design. The Clinical Diagnosis Assessment improves clinical reasoning skill, while promoting execution and awareness of discreet clinical reasoning strategies.

Indexed as

Clinical CompetenceClinical ReasoningEducational MeasurementCognitionFemaleFocus GroupsHumansMaleAuthentic assessmentClinical reasoningCognitive biasCognitive continuum theoryDecision justificationDual process theoryHeuristicMetacognitionOptometric education

Identifiers

PMID39780160
PMCPMC11708177

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.