Exploring medical students' perceptions of individual and group-based clinical reasoning with virtual patients: a qualitative study.

BACKGROUND: Virtual Patients are computer-based simulations used to teach and evaluate patient interviews, medical diagnoses, and treatment of medical conditions. It helps develop clinical reasoning skills, especially in undergraduate medical education. This study aimed to and investigate the medical students' perceptions of individual and group-based clinical reasoning and decision-making processes by using Virtual Patients. METHODS: The study group comprised 24 third-year medical students. Body Interact® software was utilized as a VP tool. The students' readiness and the courses' learning goals were considered when choosing the scenarios. Semi-structured interview forms were employed for data collection. MAXQDA 2020 qualitative analysis software was used to analyze the data. The students' written answers were analyzed using content analysis. RESULTS: The participants perceived individual applications as beneficial when making clinical decisions with Virtual Patients, but they suggested that group-based applications used with the same cases immediately following individual applications were a more appropriate decision-making method. The results indicated that students learn to make decisions through trial and error, based on software scoring priorities, or using clinical reasoning protocols. CONCLUSION: In group-based reasoning, the discussion-conciliation technique is utilized. The students stated that the individual decision-making was advantageous because it provided students with the freedom to make choices and the opportunity for self-evaluation. On the other hand, they stated that the group based decision-making process activated their prior knowledge, assisted in understanding misconceptions, and promoted information retention. Medical educators need to determine the most appropriate method when using Virtual Patients, which can be structured as individual and/or group applications depending on the competency sought.

Profiling students via clustering in a flipped clinical skills course using learning analytics.

Flipped classrooms have become popular as a student-centered approach in medical education because they allow students to improve higher-order thinking skills and problem-solving applications during in-class activities. However, students are expected to study videos and other class materials before class begins. Learning analytics and unsupervised machine learning algorithms (clustering) can be used to examine the pre-class activities of these students to identify inadequate student preparation before the in-class stage and make appropriate interventions. Furthermore, the students' profiles, which provide their interaction strategies towards online materials, can be used to design appropriate interventions. This study investigates student profiles in a flipped classroom. The learning management system interactions of 375 medical students are collected and preprocessed. The k-means clustering algorithms examined in this study show a two-cluster structure: 'high interaction' and 'low-interaction.' These results can be used to help identify low-engaged students and give appropriate feedback.

Adaptation of a short and universal learning self-efficacy scale for clinical skills in Turkish.

BACKGROUND: The performance of a clinical task depends on an individual's skills, knowledge, and beliefs. However, there is no reliable and valid tool for measuring self-efficacy beliefs toward clinical skills in the Turkish language. This research work aims to study the linguistic equivalence, validity, and reliability of a Self-Efficacy Scale for Clinical Skills (L-SES). MATERIALS AND METHODS: After reaching the original item pool of the scale, applying both forward and backward translation processes, and collecting responses of 11 experts from health professional sciences and educational sciences, the translation and adoption processes were completed. We randomly divided 651 medical students' responses to a 15-item questionnaire into two datasets and conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) analyses. RESULTS: CFA validated the three-factor model, and the model fit indexes were found to have acceptable values. The item factor loads ranged from .34 to .84, and items in the scale explained 47% of the total variance. Cronbach's alpha (.91), Spearman-Brown (.88), and Guttman Split-Half (.88) coefficients obtained within the scope of internal consistency reliability demonstrated that the scale had the desired internal consistency. CONCLUSION: The Turkish version of the short and universal learning self-efficacy scale for clinical skills questionnaire is a valid and reliable scale for measuring medical students' self-efficacy for clinical skills. Adopted questionnaires may have different factor structures when applied to two different cultures. We also discussed this issue as a hidden pattern in our study.