Harnessing a knowledge translation framework to implement an undergraduate medical education intervention: A longitudinal study.

INTRODUCTION: Implementation of evidence-informed educational interventions (EEI) involves applying and adapting theoretical and scientific knowledge to a specific context. Knowledge translation (KT) approaches can both facilitate and structure the process. The purpose of this paper is to describe lessons learned from applying a KT approach to help implement an EEI for clinical reasoning in medical students. METHODS: Using the Knowledge to Action framework, we designed and implemented an EEI intended to support the development of students' clinical reasoning skills in a renewed medical curriculum. Using mixed-methods design, we monitored students' engagement with the EEI longitudinally through a platform log; we conducted focus groups with students and stakeholders, and observed the unfolding of the implementation and its continuation. Data are reported according to six implementation outcomes: Fidelity, Feasibility, Appropriateness, Acceptability, Adoption, and Penetration. RESULTS: Students spent a mean of 24 min on the activity (fidelity outcome) with a high completion rate (between 75% and 95%; feasibility outcome) of the entire activity each time it was done. Focus group data from students and stakeholders suggest that the activity was acceptable, appropriate, feasible, adopted and well-integrated into the curriculum. DISCUSSION: Through the process we observed the importance of having a structuring framework, of working closely and deliberatively with stakeholders and students, of building upon concurrent evaluations in order to adapt iteratively the EEI to the local context and, while taking students' needs into consideration, of upholding the EEI's core educational principles.

Comparison of Assessment by a Virtual Patient and by Clinician-Educators of Medical Students' History-Taking Skills: Exploratory Descriptive Study.

BACKGROUND: A virtual patient (VP) can be a useful tool to foster the development of medical history-taking skills without the inherent constraints of the bedside setting. Although VPs hold the promise of contributing to the development of students' skills, documenting and assessing skills acquired through a VP is a challenge. OBJECTIVE: We propose a framework for the automated assessment of medical history taking within a VP software and then test this framework by comparing VP scores with the judgment of 10 clinician-educators (CEs). METHODS: We built upon 4 domains of medical history taking to be assessed (breadth, depth, logical sequence, and interviewing technique), adapting these to be implemented into a specific VP environment. A total of 10 CEs watched the screen recordings of 3 students to assess their performance first globally and then for each of the 4 domains. RESULTS: The scores provided by the VPs were slightly higher but comparable with those given by the CEs for global performance and for depth, logical sequence, and interviewing technique. For breadth, the VP scores were higher for 2 of the 3 students compared with the CE scores. CONCLUSIONS: Findings suggest that the VP assessment gives results akin to those that would be generated by CEs. Developing a model for what constitutes good history-taking performance in specific contexts may provide insights into how CEs generally think about assessment.

Self-explanation in learning clinical reasoning: the added value of examples and prompts.

CONTEXT: Recent studies suggest that self-explanation (SE) while diagnosing cases fosters the development of clinical reasoning in medical students; however, the conditions that optimise the impact of SE remain unknown. The example-based learning framework justifies an exploration of students' use of their own SEs combined with the study of examples. This study aimed to assess the impact on medical students' diagnostic performance of: (i) combining students' SEs with their listening to examples of residents' SEs, and (ii) the addition of prompts (specific questions) while working with examples. METHODS: This study consisted of a training phase and an assessment phase conducted 1 week later. In the training phase, 54 Year 3 medical students were randomly assigned to one of three groups. In all groups, students first solved four clinical cases using SE. Subsequently, Group 1 listened to examples of residents' SEs with prompts; Group 2 listened to examples of residents' SEs without prompts, and the control group solved word puzzles. Then, all students again solved the same four cases. One week later, all students solved four similar and four different cases. Students' diagnostic performance and diagnostic accuracy scores were assessed for each case at each time-point. RESULTS: Although all groups' diagnostic accuracy scores on similar cases improved significantly between the training and the assessment phase, Group 1 showed a significantly higher diagnostic performance score after 1 week than the control group (p = 0.037). On different cases, Group 1 obtained significantly higher diagnostic accuracy (p = 0.011) and diagnostic performance (p < 0.001) scores than the control group and a significantly higher diagnostic performance score than Group 2 (p = 0.018). CONCLUSIONS: Self-explanation seems to be an effective technique to help medical students learn clinical reasoning. Its impact is increased significantly by combining it with examples of residents' SEs and prompts. Although students' exposure to examples of clinical reasoning is important, their 'active processing' of these examples appears to be critical to their learning from them.

Does medical students' diagnostic performance improve by observing examples of self-explanation provided by peers or experts?

Educational strategies that promote the development of clinical reasoning in students remain scarce. Generating self-explanations (SE) engages students in active learning and has shown to be an effective technique to improve clinical reasoning in clerks. Example-based learning has been shown to support the development of accurate knowledge representations. The purpose of this study was to investigate the effect of combining student's SE and observation of peer's or expert's SE examples on diagnostic performance. Fifty-three third-year medical students were assigned to a peer SE example, an expert SE example or control (no example) group. All participants solved a set of the same four clinical cases (training cases), 1-after SE, 2-after listening to a peer or expert SE example or after a control task, and 3-1 week later. They solved a new set of four different cases (transfer cases) also 1 week later. For training cases, students improved significantly their diagnostic performance overtime but the main effect of group was not significant suggesting that students' SE mainly drives the observed effect. On transfer cases, there was no difference between the three groups (p > .05). Educational implications are discussed and further studies on different types of examples and additional strategies to help students actively process examples are proposed.

Students' self-explanations while solving unfamiliar cases: the role of biomedical knowledge.

OBJECTIVE: General guidelines for teaching clinical reasoning have received much attention, despite a paucity of instructional approaches with demonstrated effectiveness. As suggested in a recent experimental study, self-explanation while solving clinical cases may be an effective strategy to foster reasoning in clinical clerks dealing with less familiar cases. However, the mechanisms that mediate this benefit have not been specifically investigated. The aim of this study was to explore the types of knowledge used by students when solving familiar and less familiar clinical cases with self-explanation. METHODS: In a previous study, 36 third-year medical students diagnosed familiar and less familiar clinical cases either by engaging in self-explanation or not. Based on an analysis of previously collected data, the present study compared the content of self-explanation protocols generated by seven randomly selected students while solving four familiar and four less familiar cases. In total, 56 verbal protocols (28 familiar and 28 less familiar) were segmented and coded using the following categories: paraphrases, biomedical inferences, clinical inferences, monitoring statements and errors. RESULTS: Students provided more self-explanation segments from less familiar cases (M = 275.29) than from familiar cases (M = 248.71, p = 0.046). They provided significantly more paraphrases (p = 0.001) and made more errors (p = 0.008). A significant interaction was found between familiarity and the type of inferences (biomedical versus clinical, p = 0.016). When self-explaining less familiar cases, students provided significantly more biomedical inferences than familiar cases. CONCLUSIONS: Lack of familiarity with a case seems to stimulate medical students to engage in more extensive thinking during self-explanation. Less familiar cases seem to activate students' biomedical knowledge, which in turn helps them to create new links between biomedical and clinical knowledge, and eventually construct a more coherent mental representation of diseases. This may clarify the previously found positive effect that self-explanation has on the diagnosis of unfamiliar cases.

The influence of medical students' self-explanations on diagnostic performance.

CONTEXT: Skill in clinical reasoning is a highly valued attribute of doctors, but instructional approaches to foster medical students' clinical reasoning skills remain scarce. Self-explanation is an instructional procedure, the positive effects of which on learning have been demonstrated in a variety of domains, but which remain largely unexplored in medical education. OBJECTIVES: The purpose of this study was to investigate the effects of self-explanation on students' learning of clinical reasoning during clerkships and to examine whether these effects are affected by topic familiarity. METHODS: An experimental study with a training phase and an assessment phase was conducted with 36 Year 3 medical students, randomly assigned to one of two groups. In the training phase, students solved 12 clinical cases (four cases on a less familiar topic; four on a more familiar topic; four on filler topics), either generating self-explanations (n = 18) or not (n = 18). The self-explanations were generated after minimal instructions and no feedback was provided to students. One week later, in the assessment phase, students were requested to diagnose 12 different, more difficult cases, similarly distributed among the same more familiar topic, less familiar topic and filler topics, and their diagnostic performance was assessed. RESULTS: In the training phase the performance of the two groups did not differ. However, in the assessment phase 1 week later, a significant interaction was found between self-explanation and case topic familiarity (F(1,34) = 6.18, p < 0.05). Students in the self-explanation condition, compared with those in the control condition, demonstrated better diagnostic performance on subsequent clinical cases, but this effect emerged only for cases concerning the less familiar topic. CONCLUSIONS: The present study shows the beneficial influence of generating self-explanations when dealing with less familiar clinical contexts. Generating self-explanations without feedback resulted in better diagnostic performance than in the control group at 1 week after the intervention.