Identifying group metacognition associated with medical students' teamwork satisfaction in an online small group tutorial context.

BACKGROUND: Collaborative learning is an essential pedagogy in medical education, within which small group learning constitutes an integral component. Online small group teaching has been widely applied and blended with in-person sessions in the aftermath of the Covid-19 pandemic. This study examined whether group metacognition was associated with teamwork satisfaction in an online small group teaching curriculum for medical students. METHODS: We enrolled medical students of the 2nd and 4th years during the 2021 fall semester after they participated in 3 consecutive sessions of online small group tutorials (SGTs), which have been implemented in our medical school for more than 20 years. The students completed a group metacognitive scale (GMS) and a teamwork satisfaction scale (TSS) after the sessions. We analyzed whether group metacognition in 4 dimensions (knowledge of cognition, planning, evaluating, and monitoring) could be connected with medical students' teamwork satisfaction using partial least squares-structural equation modeling (PLS-SEM). RESULTS: A total of 263 medical students participated in this study. Both GMS and TSS exhibited good reliability and validity. Three of the 4 dimensions of group metacognition (cognition, planning, and evaluating) positively correlated with teamwork satisfaction (path coefficients 0.311, 0.279, and 0.21; p = 0.002, 0.002, and 0.043, respectively) following the online SGT curriculum, whereas the monitoring dimension did not (path coefficient 0.087; p = 0.357). The model achieved an adjusted R square of 0.683. CONCLUSION: We discovered that group metacognition correlated positively with better teamwork satisfaction, supporting the importance of group metacognitive competency for online collaborative learning.

An In-depth Exploration of the Interplay between fMRI Methods and Theory in Cognitive Neuroscience.

Functional magnetic resonance imaging (fMRI) has been a cornerstone of cognitive neuroscience since its invention in the 1990s. The methods that we use for fMRI data analysis allow us to test different theories of the brain, thus different analyses can lead us to different conclusions about how the brain produces cognition. There has been a centuries-long debate about the nature of neural processing, with some theories arguing for functional specialization or localization (e.g., face and scene processing) while other theories suggest that cognition is implemented in distributed representations across many neurons and brain regions. Importantly, these theories have received support via different types of analyses; therefore, having students implement hands-on data analysis to explore the results of different fMRI analyses can allow them to take a firsthand approach to thinking about highly influential theories in cognitive neuroscience. Moreover, these explorations allow students to see that there are not clearcut "right" or "wrong" answers in cognitive neuroscience, rather we effectively instantiate assumptions within our analytical approaches that can lead us to different conclusions. Here, I provide Python code that uses freely available software and data to teach students how to analyze fMRI data using traditional activation analysis and machine-learning-based multivariate pattern analysis (MVPA). Altogether, these resources help teach students about the paramount importance of methodology in shaping our theories of the brain, and I believe they will be helpful for introductory undergraduate courses, graduate-level courses, and as a first analysis for people working in labs that use fMRI.

Sensitive periods and other timing hypotheses in developmental psychopathology: A tutorial.

Researchers often aim to assess whether repeated measures of an exposure are associated with repeated measures of an outcome. A question of particular interest is how associations between exposures and outcomes may differ over time. In other words, researchers may seek the best form of a temporal model. While several models are possible, researchers often consider a few key models. For example, researchers may hypothesize that an exposure measured during a sensitive period may be associated with repeated measures of the outcome over time. Alternatively, they may hypothesize that the exposure measured immediately before the current time period may be most strongly associated with the outcome at the current time. Finally, they may hypothesize that all prior exposures are important. Many analytic methods cannot compare and evaluate these alternative temporal models, perhaps because they make the restrictive assumption that the associations between exposures and outcomes remains constant over time. Instead, we provide a tutorial describing four temporal models that allow the associations between repeated measures of exposures and outcomes to vary, and showing how to test which temporal model is best supported by the data. By finding the best temporal model, developmental psychopathology researchers can find optimal windows for intervention.

Principles of artificial intelligence in radiooncology.

PURPOSE: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology. METHODS: In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively. RESULTS: This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size. CONCLUSION: This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.

Using structural equation modeling to investigate students' satisfaction with an undergraduate tutorial system.

BACKGROUND: The undergraduate tutorial system (UTS) is a crucial measure in China for adhering to the principle of prioritizing foundational education, innovating the undergraduate talent training mode, and building a powerful country of higher education. This study investigated undergraduate students' satisfaction with UTS and the influencing factors, aiming to promote the healthy and sustainable development of UTS and provide practical implications and suggestions for universities. METHODS: Based on relevant theories, we conducted a survey study and leveraged structural equation modeling to assess students' satisfaction with UTS and explore the influencing factors. RESULTS: Our Pearson correlation analysis showed that students' satisfaction with mentors was positively correlated with dimensions such as humanistic care (r = 0.844, P < 0.05), mentor assistance (r = 0.906, P < 0.05), and mentor-student communication (r = 0.908, P < 0.05). Path analysis showed that mentor-student communication (ß = 0.486, P < 0.01), mentor assistance (ß = 0.228, P < 0.05), humanistic care (ß = 0.105, P < 0.05) were positive factors affecting students' satisfaction with mentors, while satisfaction with mentors (ß = 0.923, P < 0.01) had a positive impact on students' satisfaction with UTS. Students' satisfaction with mentors explained 73.4% of the variation in students' satisfaction with UTS, indicating that satisfaction with mentors was an important intermediary variable of UTS students. CONCLUSION: The sustainable implementation of UTS requires the effort to improve student satisfaction, and the breakthrough of strengthening the targeted mentorship in "transmitting wisdom, imparting knowledge, and resolving doubts" for students. Efforts should also be devoted to fostering students' comprehensive skills and better serving the cultivation of talents in the new era.

A Simple and Systematic Approach to Qualitative Data Extraction From Social Media for Novice Health Care Researchers: Tutorial.

Social media analyses have become increasingly popular among health care researchers. Social media continues to grow its user base and, when analyzed, offers unique insight into health problems. The process of obtaining data for social media analyses varies greatly and involves ethical considerations. Data extraction is often facilitated by software tools, some of which are open source, while others are costly and therefore not accessible to all researchers. The use of software for data extraction is accompanied by additional challenges related to the uniqueness of social media data. Thus, this paper serves as a tutorial for a simple method of extracting social media data that is accessible to novice health care researchers and public health professionals who are interested in pursuing social media research. The discussed methods were used to extract data from Facebook for a study of maternal perspectives on sudden unexpected infant death.

The pivotal role of clinical tutorial teaching in knowledge construction.

Students' new knowledge is gradually built up in the context of the task for which it is required and consolidated by applying it to clinical cases. As students see more and more clinical cases the knowledge emerges from an associative mesh of different levels of understanding. During tutorial clinical teaching, residents should be gradually exposed to an increasing range of real-world learning tasks and increasing levels of complexity. This exposure allows them to gradually develop shortcuts in the retrieval of their knowledge. This commentary provides a rationale for the construction of knowledge and the pivotal role that clinical tutorial teaching plays in this task.

Flipped and Peer-Assisted teaching: a new model in virtual anatomy education.

INTRODUCTION: In response to the COVID-19 crisis, this study aimed to introduce a new virtual teaching model for anatomy education that combines Peer-Assisted Learning (PAL) and flipped classrooms, aligning with constructivist principles. METHOD: The Flipped Peer Assisted (FPA) method was implemented in a virtual neuroanatomy course for second-year medical students at Birjand University of Medical Sciences via a descriptive study. The method involved small groups of PAL, with peer learning serving as educational assistants and the teacher acting as a facilitator. Educational content was uploaded to the university's learning management system (LMS). The opinion of medical students regarding the teaching method were evaluated using a 15-item questionnaire on a five-point Likert scale. RESULTS: A total of 210 students participated in the instruction using the FPA method. The analysis of students' scores revealed an average score of 26.75 ± 3.67 on the 30-point test. According to student feedback, this teaching method effectively motivated students to study, enhanced teamwork and communication skills, transformed their perspective on the anatomy course, provided opportunities for formative assessment and feedback, and demonstrated the teacher's dedication to education. CONCLUSION: The FPA model demonstrates its effectiveness in transforming traditional classroom teaching and fostering teaching and learning in virtual environments, particularly during pandemics like COVID-19. This model holds promise for enhancing anatomy education in challenging circumstances.

Biodiversity Analysis of Metaproteomics Samples with Unipept: A Comprehensive Tutorial.

Metaproteomics has become a crucial omics technology for studying microbiomes. In this area, the Unipept ecosystem, accessible at https://unipept.ugent.be , has emerged as a valuable resource for analyzing metaproteomic data. It offers in-depth insights into both taxonomic distributions and functional characteristics of complex ecosystems. This tutorial explains essential concepts like Lowest Common Ancestor (LCA) determination and the handling of peptides with missed cleavages. It also provides a detailed, step-by-step guide on using the Unipept Web application and Unipept Desktop for thorough metaproteomics analyses. By integrating theoretical principles with practical methodologies, this tutorial empowers researchers with the essential knowledge and tools needed to fully utilize metaproteomics in their microbiome studies.

How to be a good reviewer: A step-by-step guide for approaching peer review of a scientific manuscript.

Objectives: The peer review process is critical to maintaining quality, reliability, novelty, and innovation in the scientific literature. However, the teaching of scientific peer review is rarely a component of formal scientific or clinical training, and even the most experienced peer reviewers express interest in continuing education. The objective of this review article is to summarize the collective perspectives of experienced journal editors about how to be a good reviewer in a step-by-step guide that can serve as a resource for the performance of peer review of a scientific manuscript. Methods: This is a narrative review. Results: A review of the history and an overview of the modern-day peer review process are provided with attention to the role played by the reviewer, including important reasons for involvement in scientific peer review. The general components of a scientific peer review are described, and a model for how to structure a peer review report is provided. These concepts are also summarized in a reviewer checklist that can be used in real-time to develop and double-check one's reviewer report before submitting it. Conclusions: Peer review is a critically important service for maintaining quality in the scientific literature. Peer review of a scientific manuscript and the associated reviewer's report should assess specific details related to the accuracy, validity, novelty, and interpretation of a study's results. We hope that this article will serve as a resource and guide for reviewers of all levels of experience in the performance of peer review of a scientific manuscript.

Generating Synthetic Electronic Health Record Data Using Generative Adversarial Networks: Tutorial.

Synthetic electronic health record (EHR) data generation has been increasingly recognized as an important solution to expand the accessibility and maximize the value of private health data on a large scale. Recent advances in machine learning have facilitated more accurate modeling for complex and high-dimensional data, thereby greatly enhancing the data quality of synthetic EHR data. Among various approaches, generative adversarial networks (GANs) have become the main technical path in the literature due to their ability to capture the statistical characteristics of real data. However, there is a scarcity of detailed guidance within the domain regarding the development procedures of synthetic EHR data. The objective of this tutorial is to present a transparent and reproducible process for generating structured synthetic EHR data using a publicly accessible EHR data set as an example. We cover the topics of GAN architecture, EHR data types and representation, data preprocessing, GAN training, synthetic data generation and postprocessing, and data quality evaluation. We conclude this tutorial by discussing multiple important issues and future opportunities in this domain. The source code of the entire process has been made publicly available.

The time-dependent Poisson-gamma model in practice: Recruitment forecasting in HIV trials.

Despite a growing body of literature in the area of recruitment modeling for multicenter studies, in practice, statistical models to predict enrollments are rarely used and when they are, they often rely on unrealistic assumptions. The time-dependent Poisson-Gamma model (tPG) is a recently developed flexible methodology which allows analysts to predict recruitments in an ongoing multicenter trial, and its performance has been validated on data from a cohort study. In this article, we illustrate and further validate the tPG model on recruitment data from randomized controlled trials. Additionally, in the appendix, we provide a practical and easy to follow guide to its implementation via the tPG R package. To validate the model, we show the predictive performance of the proposed methodology in forecasting the recruitment process of two HIV vaccine trials conducted by the HIV Vaccine Trials Network in multiple Sub-Saharan countries.

Empowering health professions educators: enhancing curriculum delivery through customized e-tutorial training on fundamental digital tools.

Introduction: In the dynamic landscape of education, the fusion of technology and learning, commonly termed "technology-enhanced learning" (TEL), has emerged as a transformative phenomenon. This study focuses on the imperative integration of TEL in medical education, recognizing the diverse digital literacy levels of adult learners. The exploration introduces the innovative E-Portal training program, designed to empower health professions educators with essential skills for proficiently employing digital tools in instruction. Methodology: A dedicated team of medical educationists conducted a thorough investigation into E-curriculum design and delivery, employing the Moodle Learning Management System as the foundation for the E-Portal training program. The training, spanning crucial stages such as course design, content delivery, self-paced teaching, and quality assessment, facilitated participant progression at individual paces, unlocking subsequent steps upon meeting stipulated prerequisites. A pre-training questionnaire gauged participants' comprehension of distance learning, e-learning, synchronous and asynchronous teaching, and self-directed study. Subsequent focus group discussion post-training generated rich insights into participants' experiences, reflections, and identified challenges. Results: The results illuminate participants' limited adeptness with e-learning terminology, successful assimilation of components and functionalities, and heightened confidence in online teaching practices. However, discerned challenges during implementation, such as technical hurdles and engagement issues, highlight the multifaceted nature of TEL integration. While the E-Portal training positively impacted preparedness, participant feedback advocates for improvements in assessment tools, technical training provisions, and exploration of alternative Learning Management Systems. Discussion and conclusion: Study emphasizes the ongoing need for diverse training methodologies across Learning Management Systems, acknowledging the evolving nature of education and technology. This study underscores the transformative role of a tailored E-Portal training program in seamlessly integrating digital tools into medical education. The comprehensive insights provided contribute to a nuanced understanding of the advantages, obstacles, and potential avenues for enhancement in curriculum delivery through technology-enhanced learning, thereby propelling the field forward.

Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

Understanding Clinical Learning Quality Aspects in Mental Health Nursing Practice Course among Students: A Comprehensive Examination.

There is a growing demand for comprehensive evaluations of the clinical learning quality of nursing education and the necessity to establish robust predictors and mediators for enhancing its outcomes within the context of mental health practice. This study is threefold: 1. Evaluating nursing students' clinical learning quality before and after mental health nursing practice; 2. Establish if the grade of a theoretical course in mental health nursing and the student's perception of their theoretical knowledge level predicts the grade of mental health nursing practice; 3. Explore how model learning opportunities, self-directed learning, safety, and nursing care quality mediate learning environment quality and tutorial strategies quality following mental health nursing practice. Using a before and after the study, 107 undergraduate nursing students at an Israeli university completed a questionnaire and the Clinical Learning Quality Evaluation Index tool to assess their perceptions of clinical learning quality before and after mental health nursing practice. The results showed a decline in students' perceptions of tutorial strategy quality following mental health practical learning in clinical settings, with the theoretical course grade predicting the practical experience grade and underscoring the mediating role of learning opportunities between the learning environment and tutorial strategies. The study's findings emphasize the importance of an adaptive learning environment and a solid theoretical foundation in fostering effective tutorial strategies and enhancing the overall learning outcomes for nursing students in mental health education.

Building and analyzing metacells in single-cell genomics data.

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

A step-by-step method for cultural annotation by LLMs.

Building on the growing body of research highlighting the capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT), this paper presents a structured pipeline for the annotation of cultural (big) data through such LLMs, offering a detailed methodology for leveraging GPT's computational abilities. Our approach provides researchers across various fields with a method for efficient and scalable analysis of cultural phenomena, showcasing the potential of LLMs in the empirical study of human cultures. LLMs proficiency in processing and interpreting complex data finds relevance in tasks such as annotating descriptions of non-industrial societies, measuring the importance of specific themes in stories, or evaluating psychological constructs in texts across societies or historical periods. These applications demonstrate the model's versatility in serving disciplines like cultural anthropology, cultural psychology, cultural history, and cultural sciences at large.

How to edit a surgical case video.

A video can help highlight the real-time steps, anatomy and the technical aspects of a case that may be difficult to convey with text or static images alone. Editing with a regimented workflow allows for the transmission of only essential information to the viewer while maximizing efficiency by going through the editing process. This video tutorial breaks down the fundamentals of surgical video editing with tips and pointers to simplify the workflow.

A framework for integrating evidence to assess hazards and risk.

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference.

Using Risk Ratios to Quantify Potential Behavior-Environment Relations.

Behavior-environment functional relations are the units of explanation in applied behavior analysis (ABA). Whether hypothesized experimentally or descriptively, quantification of putative functional relations improves our ability to predict and influence behavior. Risk ratios are an accessible, straightforward quantitative analysis that can serve this purpose. They have been employed to great effect in other fields (e.g., medicine, public health), but are rarely used within ABA. In this tutorial, we describe risk ratios and how they are calculated, discuss why risk ratios are well suited for quantifying behavior-environment relations, and illustrate their utility and applicability across five demonstrations from real clinical cases. Recommendations for the use of risk ratios in research and practice are discussed.