Making sense of COVID-19: manifestations of health capital during the pandemic.

BACKGROUND: Grounded in Bourdieu's theory of human practice, this study aims to examine how individuals as social agents made sense of and acted upon their COVID-19 experiences. A recent conceptualization of health capital is utilized to explain the practices of patients in the pandemic, in relation to their biographical background. METHODS: This is a qualitative research in which the data were collected by biographical narrative interviews through a theoretical sampling approach. Eighteen interviews with COVID-19 patients were conducted and 8 of them were analyzed by the Documentary Method. RESULTS: The informants made sense of their illness experiences through their health capital, which is manifested in their self-perception of health, their attitudes towards the healthcare system, their conception of terms such as luck, their work status, and the gendered division of labour at home in the COVID-19 pandemic. All the manifestations are mediated by the social, cultural, and economic capital of the informants, and their habitual practices are based on their symbolic capital. CONCLUSION: The study depicts how social agents' health capital manifested in the pandemic, relying on their symbolic capital, and shaping their practices. Further research across diverse contexts is needed to fully understand extra dimensions of health capital as a descriptor of the social determinants of health.

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.

A study on the standard setting, validity, and reliability of a standardized patient performance rating scale - student version.

INTRODUCTION: The quality of the performances of standardized patients plays a significant role in the effectiveness of clinical skills education. Therefore, providing standardized patients with constant feedback is essential. It is especially important to get students' perspectives immediately following their encounters with standardized patients. In the literature, there is no scale for use by students to evaluate the performance of standardized patients. Thus, the three main goals of this study were to: (1) develop a scale for use by students to evaluate the performance of standardized patients, (2) examine the psychometric properties of the scale, and (3) determine a cut-off score for the scale in a standard-setting. MATERIALS AND METHODS: Seven hundred and two medical students participated in the scale- development process, the pilot test, and the validation process, and seven educators took part in the standard-setting process. After the evaluation of content validity, construct validity was assessed via exploratory and confirmatory factor analyses. For the standard-setting study, the extended Angoff method was used. RESULTS: The exploratory factor analysis revealed that the scale had a single-factor structure, which was confirmed by confirmatory factor analysis. The Cronbach's alpha internal consistency coefficient was 0.91. The scale consists of nine items. The cut-off score was determined to be 24.11/45, which represents the minimum acceptable standard for standardized patient performance. CONCLUSIONS: Our study outlined the critical steps in developing a measurement tool and produced a valid and reliable scale that allows medical students to assess the performance of standardized patients immediately following their interaction with the standardized patient. This scale constitutes an important contribution to the literature as it provides a tool for standardized patient trainers to assess standardized patients' weaknesses and help them improve their performance.KEY MESSAGESEvaluation of SP performance is essential to ensure the educational quality of clinical skills training programs.Students are the most relevant stakeholders to give feedback about SP performance immediately after encounters.The 'Standardized Patient Performance Rating Scale - Student Version' is a valid, reliable scale that can be used by students for the evaluation of standardized patients' strengths and weaknesses at individual-performance levels quickly.

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.