Early maladaptive schema, attachment style, and parenting style in a clinical population with personality disorder and normal individuals: a discriminant analysis model.

INTRODUCTION: Researchers have shown various variables' role in forming personality disorders (PD). This study aimed to assess the role of early maladaptive schema (EMS), attachment style (AS), and parenting style (PS) in discriminating between personality disorders and normal individuals. METHODS: In this study, 78 personality disorder patients and 360 healthy volunteers aged 18-84 were selected using convenience sampling. They completed the Schema Questionnaire-Short Form (SQ-SF), Revised Adult Attachment Scale (RAAS), and Baumrind's Parenting Styles Questionnaire (PSI). Data were analyzed using discriminant analysis with IBM SPSS 25. RESULTS: The results showed higher mean scores in all early maladaptive schema domains, insecure attachment styles, and authoritarian parenting in the personality disorder group than in the normal group. Also, discriminant analyses revealed that the function was statistically significant and could distinguish between the two groups and a compound of essential variables, disconnection, impaired autonomy, and secure attachment, respectively, discriminating two groups. Given that all components were able to distinguish between the two groups. CONCLUSION: Therefore, intervention based on these factors early in life may help reduce the characteristics of personality disorders. Also, considering the role of these factors, treatment protocols can be prepared.

Virtual Dissection: an Educational Technology to Enrich Medical Students' Learning Environment in Gastrointestinal Anatomy Course.

Background: Virtual dissection provides a digital experience of medical images to visualize anatomy on touchscreen tables. This study aimed to integrate the virtual dissection table (VDT) into the gastrointestinal anatomy course and assess medical students' intended learning outcomes and satisfaction with this educational technology. Methods: This quasi-experimental study enrolled second-year undergraduate medical students who studied anatomical sciences in the autumn semester of 2021-2022 at a single medical school. In the intervention and control groups, the participants were randomized to study anatomy by VDT or topographical anatomy textbooks. The knowledge tests evaluated the students' learning outcomes of gastrointestinal anatomy, and following the course, students completed a satisfaction survey. Results: The findings indicated that a significant gain occurred, and instructional intervention during which the learning environment was enriched with virtual dissection could enhance the students' learning (F = 13.33, df = 2, P < 0.01, partial η2 = 0.20) and satisfaction (T = 6.10, df = 54, P < 0.01, Cohen's d = 1.63, CI95% = 1.02-2.23). Conclusions: This study demonstrates the potential for virtual dissection to augment anatomical science education. Further research is required to consider the contributing features and apply this educational technology to enhance students' anatomy learning. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-023-01867-z.

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).

INTRODUCTION: There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. METHODS: In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. RESULTS: Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. CONCLUSIONS: The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.

Early prediction of medical students' performance in high-stakes examinations using machine learning approaches.

Introduction: Since the advent of medical education systems, managing high-stakes exams has been a top priority and challenge for all policymakers. However, considering machine learning (ML) techniques as a replacement for medical licensing examinations, particularly during crises such as the COVID-19 outbreak, could be an effective solution. This study uses ML models to develop a framework for predicting medical students' performance on high-stakes exams, such as the Comprehensive Medical Basic Sciences Examination (CMBSE). Material and methods: Prediction of students' status and score on high-stakes examinations faces several challenges, including an imbalanced number of failing and passing students, a large number of heterogeneous and complex features, and the need to identify at-risk and top-performing students. In this study, two major categories of ML approaches are compared: first, classic models (logistic regression (LR), support vector machine (SVM), and k-nearest neighbors (KNN)), and second, ensemble models (voting, bagging (BG), random forests (RF), adaptive boosting (ADA), extreme gradient boosting (XGB), and stacking). Results: To evaluate the models' discrimination ability, they are assessed using a real dataset containing information on medical students over a five-year period (n = 1005). The findings indicate that ensemble ML models demonstrate optimal performance in predicting CMBSE status (RF and stacking). Similarly, among the classic regressors, LR exhibited the highest root-mean-square deviation (RMSD) (0.134) and coefficient of determination (R2) (0.62), whereas the RF model had the highest RMSD (0.077) and R2 (0.80) overall. Furthermore, Anatomical Sciences, Biochemistry, Parasitology, and Entomology grade point average (GPA) and grades demonstrated the strongest positive correlation with the outcomes. Conclusion: Comparing classic and ensemble ML models revealed that ensemble models are superior to classic models. Therefore, the presented framework could be considered a suitable alternative for the CMBSE and other comparable medical licensing examinations.

Prediction of medical sciences students' performance on high-stakes examinations using machine learning models: a protocol for a systematic review.

INTRODUCTION: Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. Accordingly, we aim to provide a comprehensive framework and systematic review protocol for applying ML in predicting medical science students' performance on high-stakes examinations. Improving the current understanding of the input and output features, preprocessing methods, setting of ML models and required evaluation metrics seems essential. METHODS AND ANALYSIS: A systematic review will be conducted by searching the electronic bibliographic databases of MEDLINE/PubMed, EMBASE, SCOPUS and Web of Science. The search will be limited to studies published from January 2013 to June 2023. Studies explicitly predicting student performance in high-stakes examinations and referencing their learning outcomes and use of ML models will be included. Two team members will first screen literature meeting the inclusion criteria at the title, abstract and full-text levels. Second, the Best Evidence Medical Education quality framework rates the included literature. Later, two team members will extract data, including the studies' general data and the ML approach's details. Finally, the information consensus will be reached and submitted for analysis. The synthesised evidence from this review provides helpful information for medical education policy-makers, stakeholders and other researchers in adopting the ML models to evaluate medical science students' performance in high-stakes exams. ETHICS AND DISSEMINATION: This systematic review protocol summarises findings of existing publications rather than primary data and does not require an ethics review. The results will be disseminated in publications of peer-reviewed journals.

Are students performing the same in E-learning and In-person education? An introspective look at learning environments from an Iranian medical school standpoint.

INTRODUCTION: This study investigated medical students' intended learning outcomes based on e-learning and in-person education. METHODS: In this cross-sectional comparative analytical study, a group of 126 undergraduate medical students' intended learning outcomes under two different teaching methods, including e-learning and in-person, were repeatedly measured based on the census sampling method. Participants were in the preclinical curriculum phase (physiopathology) at Mashhad University of Medical Sciences (MUMS), Iran. Due to expert panel opinion, the same medical teachers and similar difficulty of lessons were considered in two investigated academic semesters. In addition, difficulty and discrimination indexes of formative and summative assessments were controlled for two study groups. The students' learning outcome index was the knowledge test scores participants received in the relevant lessons of the General Medicine (GM) curriculum preclinical courses. RESULTS: The findings indicated that students learning outcomes were significantly higher during e-learning than in in-person education for all examined variables (P < 0.05). Moreover, the difference between students' Grade Point Average (GPA) categories among the two groups was significant (P = 0.022). Students with a GPA of less than 14 experienced higher increments in their average scores after the e-learning compared to in-person education. Compared to face-to-face courses, improvements in pharmacology, theoretical semiology, and pathology scores after e-learning courses were statistically significant (P < 0.001). The differences in mean scores related to practical pathology and semiology in the two approaches were not statistically significant, P = 0.624 and P = 0.149, respectively. Furthermore, the overall students' average scores increased significantly during e-learning versus in-person education (P < 0.001). CONCLUSION: We concluded that e-learning could be appreciated as a successful method of medical education and can be used as an alternative educational method. However, considering the importance of practical or clinical courses in medical education, further research about the efficacy of the e-learning approach is highly recommended.