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Does a learner-centered approach using teleconference improve medical students' psychological safety and self-explanation in clinical reasoning conferences? a crossover study.

ABSTRACT: During clinical reasoning case conferences, a learner-centered approach using teleconferencing can create a psychologically safe environment and help learners speak up. This study aims to measure the psychological safety of students who are supposed to self-explain their clinical reasoning to conference participants. This crossover study compared the effects of two clinical reasoning case conference methods on medical students' psychological safety. The study population comprised 4th-5th year medical students participating in a two-week general medicine clinical clerkship rotation, from September 2019 to February 2020. They participated in both a learner-centered approach teleconference and a traditional, live-style conference. Teleconferences were conducted in a separate room, with only a group of students and one facilitator. Participants in group 1 received a learner-centered teleconference in the first week and a traditional, live-style conference in the second week. Participants assigned to group 2 received a traditional, live-style conference in the first week and a learner-centered approach teleconference in the second week. After each conference, Edmondson's Psychological Safety Scale was used to assess the students' psychological safety. We also counted the number of students who self-explained their clinical reasoning processes during each conference. Of the 38 students, 34 completed the study. Six out of the seven psychological safety items were significantly higher in the learner-centered approach teleconferences (p<0.01). Twenty-nine (85.3%) students performed self-explanation in the teleconference compared to ten (29.4%) in the live conference (p<0.01). A learner-centered approach teleconference could improve psychological safety in novice learners and increase the frequency of their self-explanation, helping educators better assess their understanding. Based on these results, a learner-centered teleconference approach has the potential to be a method for teaching clinical reasoning to medical students.

SUBMITTER: Hoshina Y

PROVIDER: S-EPMC8270125 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSelf-explanation without feedback has been shown to improve medical students' diagnostic reasoning. While feedback is generally seen as beneficial for learning, available evidence of the value of its combination with self-explanation is conflicting. This study investigated the effect on medical students' diagnostic performance of adding immediate or delayed content-feedback to self-explanation while solving cases.MethodsNinety-four 3rd-year students from a Canadian medical school were randomly assigned to three experimental conditions (immediate-feedback, delayed-feedback, control). In the learning phase, all students solved four clinical cases by giving i) the most likely diagnosis, ii) two main arguments supporting this diagnosis, and iii) two plausible alternative diagnoses, while using self-explanation. The immediate-feedback group was given the correct diagnosis after each case; delayed-feedback group received the correct diagnoses only after the four cases; control group received no feedback. One week later, all students solved four near-transfer (i.e., same final diagnosis as the learning cases but different scenarios) and four far-transfer cases (i.e., different final diagnosis from the learning cases and different scenarios) by answering the same three questions. Students' diagnostic accuracy (score for the response to the first question only) and diagnostic performance (combined score of responses to the three questions) scores were assessed in each phase. Four one-way ANOVAs were performed on each of the two scores for near and far-transfer cases.ResultsThere was a significant effect of experimental condition on diagnostic accuracy on near-transfer cases (p < .05). The immediate-feedback and delayed-feedback groups performed equally well, both better than control (respectively, mean = 90.73, standard deviation =10.69; mean = 89.92, standard deviation = 13.85; mean = 82.03, standard deviation = 17.66). The experimental conditions did not significantly differ on far-transfer cases.ConclusionsProviding feedback to students in the form of the correct diagnosis after using self-explanation with clinical cases is potentially beneficial to improve their diagnostic accuracy but this effect is limited to similar cases. Further studies should explore how more elaborated feedback combined with self-explanation may impact students' diagnostic performance on different cases.

Project description:BackgroundOngoing developments in medical education recognize the move to curricula that support self-regulated learning processes, skills of thinking, and the ability to adapt and navigate uncertain situations as much as the knowledge base of learners. Difficulties encountered in pursuing this reform, especially for pharmacology, include the tendency of beginner learners not to ask higher-order questions and the potential incongruency between creating authentic spaces for self-directed learning and providing external expert guidance. We tested the feasibility of developing, implementing, and sustaining an innovative model of social pedagogy as a strategy to address these challenges.MethodsConstructivism, communities of practice, and networked learning theory were selected as lenses for development of the model. Three hundred sixty-five first-year medical students participated between 2014 and 2018; they were introduced to pharmacodynamics and pharmacokinetics via 15 online modules that each included: learning objectives, a clinical vignette, teaching video, cumulative concept map, and small group wiki assignment. Five-person communities organized around the 15 wiki assignments were a key component where learners answered asynchronous, case-based questions that touched iteratively on Bloom's cognitive taxonomy levels. The social pedagogy model's wiki assignments were explored using abductive qualitative data analysis.ResultsQualitative analysis revealed that learners acquired and applied a conceptual framework for approaching pharmacology as a discipline, and demonstrated adaptive mastery by evaluating and interacting competently with unfamiliar drug information. Learners and faculty acquired habits of self-directed assessment seeking and learner-centered coaching, respectively; specifically, the model taught learners to look outward to peers, faculty, and external sources of information for credible and constructive feedback, and that this feedback could be trusted as a basis to direct performance improvement. 82-94% of learners rated the social pedagogy-based curriculum valuable.ConclusionsThis social pedagogy model is agnostic with regard to pharmacology and type of health professional learner; therefore, we anticipate its benefits to be transferable to other disciplines.

Project description:BackgroundUse of virtual patient educational tools could fill the current gap in the teaching of clinical reasoning skills. However, there is a limited understanding of their effectiveness. The aim of this study was to synthesise the evidence to understand the effectiveness of virtual patient tools aimed at improving undergraduate medical students' clinical reasoning skills.MethodsWe searched MEDLINE, EMBASE, CINAHL, ERIC, Scopus, Web of Science and PsycINFO from 1990 to January 2022, to identify all experimental articles testing the effectiveness of virtual patient educational tools on medical students' clinical reasoning skills. Quality of the articles was assessed using an adapted form of the MERSQI and the Newcastle-Ottawa Scale. A narrative synthesis summarised intervention features, how virtual patient tools were evaluated and reported effectiveness.ResultsThe search revealed 8,186 articles, with 19 articles meeting the inclusion criteria. Average study quality was moderate (M = 6.5, SD = 2.7), with nearly half not reporting any measurement of validity or reliability for their clinical reasoning outcome measure (8/19, 42%). Eleven articles found a positive effect of virtual patient tools on reasoning (11/19, 58%). Four reported no significant effect and four reported mixed effects (4/19, 21%). Several domains of clinical reasoning were evaluated. Data gathering, ideas about diagnosis and patient management were more often found to improve after virtual patient use (34/47 analyses, 72%) than application of knowledge, flexibility in thinking and problem-solving (3/7 analyses, 43%).ConclusionsUsing virtual patient tools could effectively complement current teaching especially if opportunities for face-to-face teaching or other methods are limited, as there was some evidence that virtual patient educational tools can improve undergraduate medical students' clinical reasoning skills. Evaluations that measured more case specific clinical reasoning domains, such as data gathering, showed more consistent improvement than general measures like problem-solving. Case specific measures might be more sensitive to change given the context dependent nature of clinical reasoning. Consistent use of validated clinical reasoning measures is needed to enable a meta-analysis to estimate effectiveness.

Project description:PurposeDeveloping medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasible for this purpose because they are time-intensive to score. This study explored the potential of using machine learning technologies to score one such assessment-the diagnostic justification essay.MethodFrom May to September 2018, machine scoring algorithms were trained to score a sample of 700 diagnostic justification essays written by 414 third-year medical students from the Southern Illinois University School of Medicine classes of 2012-2017. The algorithms applied semantically based natural language processing metrics (e.g., coherence, readability) to assess essay quality on 4 criteria (differential diagnosis, recognition and use of findings, workup, and thought process); the scores for these criteria were summed to create overall scores. Three sources of validity evidence (response process, internal structure, and association with other variables) were examined.ResultsMachine scores correlated more strongly with faculty ratings than faculty ratings did with each other (machine: .28-.53, faculty: .13-.33) and were less case-specific. Machine scores and faculty ratings were similarly correlated with medical knowledge, clinical cognition, and prior diagnostic justification. Machine scores were more strongly associated with clinical communication than were faculty ratings (.43 vs .31).ConclusionsMachine learning technologies may be useful for assessing medical students' long-form written clinical reasoning. Semantically based machine scoring may capture the communicative aspects of clinical reasoning better than faculty ratings, offering the potential for automated assessment that generalizes to the workplace. These results underscore the potential of machine scoring to capture an aspect of clinical reasoning performance that is difficult to assess with traditional analytic scoring methods. Additional research should investigate machine scoring generalizability and examine its acceptability to trainees and educators.

Project description:BACKGROUND:The clinical reasoning process, which requires biomedical knowledge, knowledge about problem-solving strategies, and knowledge about reasons for diagnostic procedures, is a key element of physicians' daily practice but difficult to assess. The aim of this study was to empirically develop a Clinical Reasoning Indicators-History Taking-Scale (CRI-HT-S) and to assess the clinical reasoning ability of advanced medical students during a simulation involving history taking. METHODS:The Clinical Reasoning Indictors-History Taking-Scale (CRI-HT-S) including a 5-point Likert scale for assessment was designed from clinical reasoning indicators identified in a qualitative study in 2017. To assess indicators of clinical reasoning ability, 65 advanced medical students (semester 10, n =?25 versus final year, n =?40) from three medical schools participated in a 360-degree competence assessment in the role of beginning residents during a simulated first workday in hospital. This assessment included a consultation hour with five simulated patients which was videotaped. Videos of 325 patient consultations were assessed using the CRI-HT-S. A factor analysis was conducted and the students' results were compared according to their advancement in undergraduate medical training. RESULTS:The clinical reasoning indicators of the CRI-HT-S loaded on three factors relevant for clinical reasoning: 1) focusing questions, 2) creating context, and 3) securing information. Students reached significantly different scores (p <?.001) for the three factors (factor 1: 4.07?±?.47, factor 2: 3.72?±?.43, factor 3: 2.79?±?.83). Students in semester 10 reached significantly lower scores for factor 3 than students in their final year (p <?.05). CONCLUSIONS:The newly developed CRI-HT-S worked well for quantitative assessment of clinical reasoning indicators during history taking. Its three-factored structure helped to explore different aspects of clinical reasoning. Whether the CRI-HT-S has the potential to be used as a scale in objective structured clinical examinations (OCSEs) or in workplace-based assessments of clinical reasoning has to be investigated in further studies with larger student cohorts.

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Does a learner-centered approach using teleconference improve medical students' psychological safety and self-explanation in clinical reasoning conferences? a crossover study.

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