Assessing Abdominal Examination Skills in a Surgery Clerkship Standardized Patient Encounter for Curriculum Improvement.

INTRODUCTION: Standardized patient (SP) encounters allow medical students to practice physical examination skills and clinical reasoning. SP cases are used for learning and assessment, but recorded encounters can also be valuable curriculum evaluation tools. We aimed to review SP encounters to improve abdominal examination skills and the broader physical examination curriculum. METHODS: We reviewed recorded SP encounters of third-year medical students on surgery clerkship rotation. Students examined a cisgender woman presenting with acute right lower abdominal pain. We observed abdominal examinations to determine which maneuvers were attempted and completed correctly. We then used these outcomes to develop targeted clerkship training for the subsequent student cohort. Our intervention targeted abdominal examination gaps by explaining how to integrate abdominal examination findings with a focused history for surgical patients. We evaluated the intervention's impact on abdominal examination skills with third-year medical students in comparison (2021-2022, n = 119) and intervention (2022-2023, n = 132) groups. RESULTS: In both the comparison and intervention groups, nearly all students attempted at least 1 general examination maneuver like auscultation, palpation, percussion, or rebound tenderness. Only 40% of students in the comparison group attempted an advanced maneuver like the Rovsing, Psoas, or Obturator sign. After the intervention, 75% of students in the intervention group attempted an advanced maneuver (χ2(1, 251) = 31.0, p < .001). Cohorts did not gain skills over time through the clerkship. Rebound tenderness was frequently assessed incorrectly by students in both groups, with many avoiding the right lower quadrant entirely. CONCLUSIONS: This project highlights how medical students struggle to utilize abdominal examination maneuvers and integrate findings. The results also showed that students did not consistently learn advanced examination skills either before or during clerkship rotation, which may be commonly assumed by clinical faculty. Finally, this work demonstrates how SP encounters can be used to evaluate and improve surgical education curriculum.

Faculty Training on Navigating Gender and Sex in Medical Education.

Introduction: Language that assumes gender and sex are binary and aligned is pervasive in medicine and is often used when teaching on physiology and pathology. Information presented through this lens oversimplifies disease mechanisms and poorly addresses the health of gender and sexually diverse (GSD) individuals. We developed a training session to help faculty reference gender and sex in a manner that would be accurate and inclusive of GSD health. Methods: The 1-hour session for undergraduate and graduate medical educators highlighted cisgender and binary biases in medical teachings and introduced a getting-to-the-root mindset that prioritized teaching the processes underlying differences in disease profiles among gender and sex subpopulations. The training consisted of 30 minutes of didactic teaching and 20 minutes of small-group discussion. Medical education faculty attended and self-reported knowledge and awareness before and after the training. Results were compared using paired t tests. Expenses included fees for consultation and catering. Results: Forty faculty participated (pretraining survey n = 36, posttraining survey n = 21). After the training, there was a significant increase in self-reported awareness of the difference between gender and sex (p = .002), perceived relevance of gender to teachings (p = .04), and readiness to discuss physiological drivers of sex-linked disease (p = .005). Discussion: Participants reported increased understanding and consideration of gender and sex in medical education; feedback emphasized a desire for continued guidance. This easily adaptable session can provide an introduction to a series of medical teachings on gender and sex.

Influences on the Decision to Euthanize a Compromised Pig.

The decision to euthanize a compromised pig can be challenging for pig farmers and veterinarians. To understand more about the challenges in Germany, a cross-sectional online survey was conducted. Based on a hybrid design, the responses of 39 veterinarians and 62 pig farmers were analyzed to generate a list of common clinical signs associated with the euthanasia of sows, fatteners, and piglets. Moreover, a set of influences on the farm, due to economic and personal considerations, were found to shape the decision-making process. The two most salient reasons outlined for the delay of timely euthanasia were uncertainty and misinterpretation of the chance for healing. The lack of valid clinical signs or a sound justification was most frequently mentioned as a challenge to the general decision-making process. In summation, this study highlights the need to generate a valid taxonomy for clinical signs that includes their development in a compromised pig over time. Future studies should elaborate on the justification of euthanasia decisions to facilitate the resolution of ethical dilemmas among the involved pig farmers and veterinarians. Lastly, the results suggest that clinical reasoning and consultation skills should be included when decision-making behavior is to be trained.

Reassessing the Use of Race in Clinical Algorithms: An Interactive, Case-Based Session for Medical Students Using eGFR.

Introduction: Medical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example. Methods: We created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness. Results: Fifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity. Discussion: Our module expands on others' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.

Reasoning with large language models for medical question answering.

OBJECTIVES: To investigate approaches of reasoning with large language models (LLMs) and to propose a new prompting approach, ensemble reasoning, to improve medical question answering performance with refined reasoning and reduced inconsistency. MATERIALS AND METHODS: We used multiple choice questions from the USMLE Sample Exam question files on 2 closed-source commercial and 1 open-source clinical LLM to evaluate our proposed approach ensemble reasoning. RESULTS: On GPT-3.5 turbo and Med42-70B, our proposed ensemble reasoning approach outperformed zero-shot chain-of-thought with self-consistency on Steps 1, 2, and 3 questions (+3.44%, +4.00%, and +2.54%) and (2.3%, 5.00%, and 4.15%), respectively. With GPT-4 turbo, there were mixed results with ensemble reasoning again outperforming zero-shot chain-of-thought with self-consistency on Step 1 questions (+1.15%). In all cases, the results demonstrated improved consistency of responses with our approach. A qualitative analysis of the reasoning from the model demonstrated that the ensemble reasoning approach produces correct and helpful reasoning. CONCLUSION: The proposed iterative ensemble reasoning has the potential to improve the performance of LLMs in medical question answering tasks, particularly with the less powerful LLMs like GPT-3.5 turbo and Med42-70B, which may suggest that this is a promising approach for LLMs with lower capabilities. Additionally, the findings show that our approach helps to refine the reasoning generated by the LLM and thereby improve consistency even with the more powerful GPT-4 turbo. We also identify the potential and need for human-artificial intelligence teaming to improve the reasoning beyond the limits of the model.

Interprofessional clinical reasoning education.

Clinical reasoning is considered one of the most important competencies but is not included in most healthcare curricula. The number and diversity of patient encounters are the decisive factors in the development of clinical reasoning competence. Physical real patient encounters are considered optimal, but virtual patient cases also promote clinical reasoning. A high-volume, low-fidelity virtual patient library thus can support clinical reasoning training in a safe environment and can be tailored to the needs of learners from different health care professions. It may also stimulate interprofessional understanding and team shared decisions. Implementation will be challenged by tradition, the lack of educator competence and prior experience as well as the high-density curricula at medical and veterinary schools and will need explicit address from curriculum managers and education leads.

Creating virtual patients using large language models: scalable, global, and low cost.

Virtual patients (VPs) have long been used to teach and assess clinical reasoning. VPs can be programmed to simulate authentic patient-clinician interactions and to reflect a variety of contextual permutations. However, their use has historically been limited by the high cost and logistical challenges of large-scale implementation. We describe a novel globally-accessible approach to develop low-cost VPs at scale using artificial intelligence (AI) large language models (LLMs). We leveraged OpenAI Generative Pretrained Transformer (GPT) to create and implement two interactive VPs, and created permutations that differed in contextual features. We used systematic prompt engineering to refine a prompt instructing ChatGPT to emulate the patient for a given case scenario, and then provide feedback on clinician performance. We implemented the prompts using GPT-3.5-turbo and GPT-4.0, and created a simple text-only interface using the OpenAI API. GPT-4.0 was far superior. We also conducted limited testing using another LLM (Anthropic Claude), with promising results. We provide the final prompt, case scenarios, and Python code. LLM-VPs represent a 'disruptive innovation' - an innovation that is unmistakably inferior to existing products but substantially more accessible (due to low cost, global reach, or ease of implementation) and thereby able to reach a previously underserved market. LLM-VPs will lay the foundation for global democratization via low-cost-low-risk scalable development of educational and clinical simulations. These powerful tools could revolutionize the teaching, assessment, and research of management reasoning, shared decision-making, and AI evaluation (e.g. 'software as a medical device' evaluations).

A dimensional analysis of experienced intensive care unit nurses' clinical decision-making for bleeding after cardiac surgery.

BACKGROUND: Bleeding following cardiac surgery is common and serious, yet a gap persists in understanding how experienced intensive care nurses identify and respond to such complications. AIM: To describe the clinical decision-making of experienced intensive care unit nurses in addressing bleeding after cardiac surgery. STUDY DESIGN: This qualitative study adopted the Recognition-Primed Decision Model as its theoretical framework. Thirty-nine experienced nurses from four adult intensive care units participated in semi-structured interviews based on the critical decision method. The interviews explored their clinical judgements and decisions in bleeding situations, and data were analysed through dimensional analysis, an alternative to grounded theory. RESULTS: Participants maintained consistent vigilance towards post-cardiac surgery bleeding, recognizing it through a haemorrhagic dimension associated with blood loss and chest drainage and a hypovolemic dimension focusing on the repercussions of reduced blood volume. These dimensions organized their understanding of bleeding types (i.e., normal, medical, surgical, tamponade) and necessary actions. Their decision-making encompassed monitoring bleeding, identifying the cause, stopping the bleeding, stabilizing haemodynamic and supporting the patient and family. Participants also adapted their actions to specific circumstances, including local practices, professional autonomy, interprofessional dynamics and resource availability. CONCLUSIONS: Nurses' decision-making was shaped by their personal attributes, the patient's condition and contextual circumstances, underscoring their expertise and pivotal role in anticipating actions and adapting to diverse conditions. The concept of actionability emerged as the central dimension explaining their decision-making, defined as the capability to implement actions towards specific goals within the possibilities and constraints of a situation. RELEVANCE TO CLINICAL PRACTICE: This study underscores the need for continual updates to care protocols to align with current evidence and for quality improvement initiatives to close existing practice gaps. Exploring the concept of actionability further, developing adaptability-focused educational programmes, and understanding decision-making intricacies are crucial for informing nursing education and decision-support systems.

[The expert panel for Script Concordance Tests: A truly adequate reference?] / Le panel d'experts pour les tests de concordance de script : une référence réellement adéquate ?

The Script Concordance Tests (SCTs) are an examination modality introduced by decree in the French National Ranking Exam for medical students in 2024. Their objective is to evaluate clinical reasoning in situations of uncertainty. In practice, SCTs assess the impact of new information on the probability of a hypothesis formulated a priori based on an authentic clinical scenario. This approach resembles probabilistic (or Bayesian) reasoning. Due to the uncertainty associated with the explored clinical situation, SCTs do not compare the student's response to an expected one in a theoretical knowledge reference. Instead, the distribution of responses from a panel of experienced physicians is used to establish the question's scoring scale. Literature data suggest that physicians, even experienced ones, like most humans, often exhibit biased intuitive probabilistic reasoning. These biases raise questions about the relevance of using expert panel responses as scoring scales for SCTs.

Clinical reasoning in real-world practice: a primer for medical trainees and practitioners.

Clinical reasoning is a crucial skill and defining characteristic of the medical profession, which relates to intricate cognitive and decision-making processes that are needed to solve real-world clinical problems. However, much of our current competency-based medical education systems have focused on imparting swathes of content knowledge and skills to our medical trainees, without an adequate emphasis on strengthening the cognitive schema and psychological processes that govern actual decision-making in clinical environments. Nonetheless, flawed clinical reasoning has serious repercussions on patient care, as it is associated with diagnostic errors, inappropriate investigations, and incongruent or suboptimal management plans that can result in significant morbidity and even mortality. In this article, we discuss the psychological constructs of clinical reasoning in the form of cognitive 'thought processing' models and real-world contextual or emotional influences on clinical decision-making. In addition, we propose practical strategies, including pedagogical development of a personal cognitive schema, mitigating strategies to combat cognitive bias and flawed reasoning, and emotional regulation and self-care techniques, which can be adopted in medical training to optimize physicians' clinical reasoning in real-world practice that effectively translates learnt knowledge and skill sets into good decisions and outcomes.

Global impact of a virtual neurology morning report.

The global shortage and inequitable distribution of neurologists has led to significant gaps not only in neurology care, but also in neurology education. In order to increase access to neurology education, we developed neurology virtual morning report (NVMR), a virtual, open-access, case-based clinical reasoning conference available to learners worldwide. To evaluate NVMR's impact on participants' perception of, interest in, and confidence in neurology, we conducted a survey. Respondents represented 25 different countries of various income levels. The majority of respondents reported that NVMR decreased their perception of difficulty in understanding neurology and increased confidence in various clinical reasoning domains in neurology. Additionally, the majority of medical student participants showed an increased interest in pursuing neurology as a future specialty after participating in NVMR. NVMR represents a potential model for virtual educational conferences and highlights the opportunities digital education has to improve equitable access to neurology education.

[Informing the clinical reasoning of advanced practice students with nursing theories]. / Éclairer le raisonnement clinique des étudiants en pratique avancée par les théories infirmières.

This article examines the integration of a Clinical Nurse Reasoning (CNR) model into advanced nursing practice training to develop the clinical reasoning skills of advanced practice nurses. The article explores the contribution of Callista Roy's conceptual model of adaptation and presents a global analysis of a complex care situation that justifies the introduction of an CNR model paired with nursing knowledge to ensure comprehensive, quality nursing care.

Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis.

AIMS: To characterize the thematic foci, structure, and evolution of nursing research on clinical reasoning and judgment. DESIGN: Bibliometric analysis. METHODS: We used a bibliometric method to analyze 1528 articles. DATA SOURCE: We searched the Scopus bibliographic database on January 7, 2024. RESULTS: Through a keyword co-occurrence analysis, we found the most frequent keywords to be clinical judgment, clinical reasoning, nursing education, simulation, nursing, clinical decision-making, nursing students, nursing assessment, critical thinking, nursing diagnosis, patient safety, nurses, nursing process, clinical competence, and risk assessment. The focal themes, structure, and evolution of nursing research on clinical reasoning and judgment were revealed by keyword mapping, clustering, and time-tracking. CONCLUSION: By assessing key nursing research areas, we extend the current discourse on clinical reasoning and clinical judgment for researchers, educators, and practitioners. Critical challenges must still be met by nursing professionals with regard to their use of clinical reasoning and judgment within their clinical practice. Further knowledge and comprehension of the clinical reasoning process and the development of clinical judgment must be successfully translated from research to nursing education and practice. IMPLICATIONS FOR THE PROFESSION: This study highlights the nursing knowledge gaps with regard to nurses' use of clinical reasoning and judgment and encourages nursing educators and professionals to focus on developing nurses' clinical reasoning and judgment with regard to their patients' safety. IMPACT: In addressing nurses' use of clinical reasoning and judgment, and with regard to patient safety in particular, this study found that, in certain clinical settings, the use of clinical reasoning and judgment remains a challenge for nursing professionals. This study should thus have an effect on nursing academics' research choices, on nursing educators' teaching practices, and on nurses' clinical practices. REPORTING METHOD: Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.

Critical thinking pedagogical practices in medical education: a systematic review.

Introduction: The development of critical thinking (CT) has been a universal goal in higher education. A systematic review of the literature was conducted to evaluate the effectiveness of currently used pedagogical practices to foster CT/ clinical reasoning (CR)/ clinical judgment (CJ) skills and/or dispositions in undergraduate medical students. Methods: PubMed, Web of Science and Scopus databases were searched from January 2010 to April 2021 with a predefined Boolean expression. Results: Of the 3221 articles originally identified, 33 articles were included by using PICOS methodology. From these, 21 (64%) reported CR pedagogical practices and 12 (36%) CT pedagogical practices. Discussion: Overall, pedagogical practices such as cognitive/visual representation, simulation, literature exposure, test-enhancing and team-based learning, clinical case discussion, error-based learning, game-based learning seem to enhance CT/CR skills and/or dispositions. Further research is required to identify the optimal timing, duration and modality of pedagogical interventions for effectively foster CT/CR in medical education.

Using ChatGPT in the Development of Clinical Reasoning Cases: A Qualitative Study.

Background There has been an explosion of commentary and discussion about the ethics and utility of using artificial intelligence in medicine, and its practical use in medical education is still being debated. Through qualitative research methods, this study aims to highlight the advantages and pitfalls of using ChatGPT in the development of clinical reasoning cases for medical student education. Methods Five highly experienced faculty in medical education were provided instructions to create unique clinical reasoning cases for three different chief concerns using ChatGPT 3.0. Faculty were then asked to reflect on and review the created cases. Finally, a focus group was conducted to further analyze and describe their experiences with the new technology. Results Overall, faculty found the use of ChatGPT in the development of clinical reasoning cases easy to use but difficult to get to certain objectives and largely incapable of being creative enough to create complexity for student use without heavy editing. The created cases did provide a helpful starting point and were extremely efficient; however, faculty did experience some medical inaccuracies and fact fabrication. Conclusion There is value to using ChatGPT to develop curricular content, especially for clinical reasoning cases, but it needs to be comprehensively reviewed and verified. To efficiently and effectively utilize the tool, educators will need to develop a framework that can be easily translatable into simple prompts that ChatGPT can understand. Future work will need to strongly consider the risks of recirculating biases and misinformation.

Pharmacists and pharmacy students' perceptions on how a new teaching model supports their clinical decision-making.

BACKGROUND AND PURPOSE: Clinical decision-making (CDM) is crucial in pharmacy practice, necessitating effective teaching in undergraduate and postgraduate pharmacy education. This study aims to explore undergraduates and postgraduates' perceptions of how a new teaching model supports their CDM when addressing patient cases. EDUCATIONAL ACTIVITY AND SETTING: Implemented in a full-day CDM course for pharmacy students and a half-day course for pharmacists in the Netherlands, the model, accompanied by a learning guide, facilitated CDM in patient cases. Eight courses were conducted between September 2022 to June 2023, followed by an online survey measuring participants' agreement on how the model supported their CDM, using a 5-point Likert scale. Additionally, three open-ended questions were included to elicit learning outcomes and self-development opportunities. FINDINGS: Of 175 invited participants, 159 (91%) completed the survey. Most agreed the teaching model supported their CDM, particularly in considering the patient's healthcare needs and context (96%), and exploring all available options (96%). Participants found the model provided a clear structure (97%), and fostered critical thinking (93%). The most frequently mentioned learning outcomes and self-development opportunities included collecting sufficient relevant information, maintaining a broad perspective, and decelerating the process to avoid premature closure. SUMMARY: Participants agreed that the teaching model helped them to make clinical decisions. Both undergraduate and postgraduate pharmacy education could possibly benefit from the teaching model's implementation in supporting pharmacy students and pharmacists conducting CDM in pharmacy practice.

Effects of interacting with a large language model compared with a human coach on the clinical diagnostic process and outcomes among fourth-year medical students: study protocol for a prospective, randomised experiment using patient vignettes.

INTRODUCTION: Versatile large language models (LLMs) have the potential to augment diagnostic decision-making by assisting diagnosticians, thanks to their ability to engage in open-ended, natural conversations and their comprehensive knowledge access. Yet the novelty of LLMs in diagnostic decision-making introduces uncertainties regarding their impact. Clinicians unfamiliar with the use of LLMs in their professional context may rely on general attitudes towards LLMs more broadly, potentially hindering thoughtful use and critical evaluation of their input, leading to either over-reliance and lack of critical thinking or an unwillingness to use LLMs as diagnostic aids. To address these concerns, this study examines the influence on the diagnostic process and outcomes of interacting with an LLM compared with a human coach, and of prior training vs no training for interacting with either of these 'coaches'. Our findings aim to illuminate the potential benefits and risks of employing artificial intelligence (AI) in diagnostic decision-making. METHODS AND ANALYSIS: We are conducting a prospective, randomised experiment with N=158 fourth-year medical students from Charité Medical School, Berlin, Germany. Participants are asked to diagnose patient vignettes after being assigned to either a human coach or ChatGPT and after either training or no training (both between-subject factors). We are specifically collecting data on the effects of using either of these 'coaches' and of additional training on information search, number of hypotheses entertained, diagnostic accuracy and confidence. Statistical methods will include linear mixed effects models. Exploratory analyses of the interaction patterns and attitudes towards AI will also generate more generalisable knowledge about the role of AI in medicine. ETHICS AND DISSEMINATION: The Bern Cantonal Ethics Committee considered the study exempt from full ethical review (BASEC No: Req-2023-01396). All methods will be conducted in accordance with relevant guidelines and regulations. Participation is voluntary and informed consent will be obtained. Results will be published in peer-reviewed scientific medical journals. Authorship will be determined according to the International Committee of Medical Journal Editors guidelines.

The Bobath concept (NDT) in adult neurorehabilitation: a scoping review of conceptual literature.

PURPOSE: This scoping review aims to describe how Bobath concept is conceptualized, operationalized, and studied in adult neurorehabilitation. METHODS: The Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) guidelines were adopted. Non-scientific and research articles were searched in electronic databases PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, ScienceDirect, and Physiotherapy Evidence Database (PEDro), with the keywords "Bobath" or "Neurodevelopmental Treatment", published in English, Spanish, and Portuguese, between 2013 and 2023. RESULTS: Of the 78 publications identified, 31 articles addressed the conceptual underpinnings of Bobath concept (seven theoretical papers, seven Delphi/surveys/mixed methods studies, four qualitative studies, one scoping review, 10 letters to the editor, and two editorials), comprising five themes: (a) theoretical principles; (b) clinical principles; (c) clinical reasoning; (d) conceptualizing movement; and, current (e) evidence debate. The revised definition and the Model of Bobath Clinical Practice provide a clarification of the unique aspects of Bobath concept. A new clinical skill was identified beyond facilitation - visuospatial kinesthetic perception - as well as how Bobath experts conceptualize movement, which are all integral to clinical reasoning. CONCLUSIONS: This review provides an updated Bobath clinical framework that gathers the theoretical foundations and clinical practice principles that require careful consideration in the design of future intervention studies.Implications for rehabilitationThis scoping review consolidates the clinical and theoretical principles of contemporary Bobath practice, providing a clear framework for clinicians.The Model of Bobath Clinical Practice (MBCP) framework enables detailed documentation of movement analysis and movement diagnosis, guiding clinical reasoning and interventions.This review identifies fundamental principles and practices to inform future Bobath intervention studies, ensuring their clinical relevance.A framework with specific recommendations has been developed to guide Bobath intervention studies, enhancing the integration of clinical practice, education, and research.

Mock Wards: Incorporating a Theoretical Framework to Create a Blended Virtual and In-Person Clinical Reasoning Education Platform.

Introduction The coronavirus 2019 pandemic highlighted virtual learning (VL) as a promising tool for medical education, yet its effectiveness in teaching clinical reasoning (CR) remains underexplored. Past studies have suggested VL can effectively prepare students for clinical settings. Informed by the Milestones of Observable Behaviours for CR (MOBCR) and whole-case theoretical frameworks, the Mock Wards (MW) program was created using a novel blended in-person learning (IPL) and VL platform. MW consisted of case-based small-group formats for medical students interested in learning approaches and differentials to commonly encountered presenting symptoms and diagnoses in internal medicine. This study sought to use MW's blended design to qualitatively analyze CR development and compare its utility between VL and IPL. Methods Qualitative analysis was conducted using in-depth semi-structured interviews with first-year pre-clerkship medical students (n = 8) who completed the MW program and participated in the study. The interview guide was informed by the MOBCR framework. Interview transcripts were analyzed using a directed qualitative content analysis approach. Translational coding and HyperRESEARCHTM (Researchware, Inc., Randolph, MA) software-generated mind maps guided the theme development. Results Three overarching themes were constructed: (1) tailoring pedagogical frameworks to learning modalities, (2) learning through interactivity, and (3) balancing accessibility with learner engagement. Participants emphasized that teaching CR skills is modality-specific and not fully interchangeable, with IPL being superior in facilitating social cohesion, non-verbal communication, and feedback. In contrast, VL required structured approaches and relied more on verbal communication and pre-made digital materials. IPL also enhanced interactivity, peer relationships, and spontaneous communication, whereas VL faced challenges such as social awkwardness and technological constraints hindering effective collaboration. VL provided superior accessibility to facilitate distributed learning and management of concurrent academic obligations. Conclusion The MW-blended platform highlights the importance of focusing on modality-tailored pedagogies, emphasizing group interactability, and balancing VL accessibility against decreased engagement within the IPL environment when teaching CR skills. Blended education models may benefit from a scaffolding approach, using IPL as a prerequisite to VL to improve CR development and alignment within a learner's zone of proximal development.