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.

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism.

BACKGROUND: Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity. METHOD: To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial-temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently. RESULTS: Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model. CONCLUSIONS: Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.

Hospital Use of a Web-Based Clinical Knowledge Support System and In-Training Examination Performance Among Postgraduate Resident Physicians in Japan: Nationwide Observational Study.

Background: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire. Objective: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores. Methods: This nationwide cross-sectional study included postgraduate year-1 and -2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019. Results: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95% CI 0.14-0.59; P=.001). Conclusions: The findings suggest that the development of residents' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes.

Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.

Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor segmentation, but the segmentation based on DCNN is data-hungry for model training and ignores clinical knowledge. From the clinical view, bladder tumors originate from the mucosal surface of bladder and must rely on the bladder wall to survive and grow. This clinical knowledge of tumor location is helpful to improve the bladder tumor segmentation. To achieve this, we propose a novel bladder tumor segmentation method, which incorporates the clinical logic rules of bladder tumor and bladder wall into DCNNs to harness the tumor segmentation. Clinical logical rules provide a semantic and human-readable knowledge representation and are easy for knowledge acquisition from clinicians. In addition, incorporating logical rules of clinical knowledge helps to reduce the data dependency of the segmentation network, and enables precise segmentation results even with limited number of annotated images. Experiments on bladder MR images collected from the collaborating hospital validate the effectiveness of the proposed bladder tumor segmentation method.

Making things specific: towards an anthropology of everyday ethics in healthcare.

This paper is the English translation and adaptation of my inaugural lecture in Amsterdam for the Chair Anthropology of Everyday Ethics in Health Care. I argue that the challenges in health care may look daunting and unsolvable in their scale and complexity, but that it helps to consider these problems in their specificity, while accepting that some problems may not be solved but have become chronic. The paper provides reflections on how to develop a scientific approach that does not aim to eradicate bad things but explores ways in which to live with them. Crucial in this quest is the attention to how we conceptualize problems, and whether this is specific enough for addressing present day concerns. I propose an anthropology of everyday ethics as a way to study people's everyday ways of handling a variety of goods in practice. I draw specific attention to exploring aesthetic values in everyday life amongst these, values that are used abundantly to qualify events in everyday life but rarely theorized in philosophy or social science.

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

OBJECTIVES: Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance. MATERIALS AND METHODS: The latest China National Medical Licensing Examination (CNMLE-2022) served as the benchmark. We collected 53 medical books and 381 149 medical questions to construct the medical knowledge base and question bank. The proposed Knowledge and Few-shot Enhancement In-context Learning (KFE) framework leverages the in-context learning ability of LLMs to integrate diverse external clinical knowledge sources. We evaluated KFE with ChatGPT (GPT-3.5), GPT-4, Baichuan2-7B, Baichuan2-13B, and QWEN-72B in CNMLE-2022 and further investigated the effectiveness of different pathways for incorporating LLMs with medical knowledge from 7 distinct perspectives. RESULTS: Directly applying ChatGPT failed to qualify for the CNMLE-2022 at a score of 51. Cooperated with the KFE framework, the LLMs with varying sizes yielded consistent and significant improvements. The ChatGPT's performance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This surpasses the qualification threshold (60) and exceeds the average human score of 68.70, affirming the effectiveness and robustness of the framework. It also enabled a smaller Baichuan2-13B to pass the examination, showcasing the great potential in low-resource settings. DISCUSSION AND CONCLUSION: This study shed light on the optimal practices to enhance the capabilities of LLMs in non-English medical scenarios. By synergizing medical knowledge through in-context learning, LLMs can extend clinical insight beyond language barriers in healthcare, significantly reducing language-related disparities of LLM applications and ensuring global benefit in this field.

Development of a Core Outcome Set for Family and Community Nursing: Protocol for a Delphi Study.

BACKGROUND: Family and community nurses (FCNs) play a crucial role in delivering primary care to patients within their homes and communities. A key aspect of their role involves various health interventions, which are influenced by their unique competencies, such as health promotion, advanced clinical knowledge, and strong interpersonal skills. However, it is essential to understand which specific health outcomes these interventions impact to better understand the relationship between FCNs' skills and the health results. OBJECTIVE: This study aims to outline the steps we will take to develop a set of core outcomes. These outcomes will be particularly sensitive to the health interventions carried out by FCNs, providing a clearer picture of their practice's impact. METHODS: A Delphi survey will be used for this research, conducted from January to December 2024. The process will involve 5 steps and input from 3 stakeholder categories. These stakeholders will help identify a preliminary list of outcomes that will form the basis of our core outcome set (COS). RESULTS: This guideline will be beneficial for a wide range of stakeholders involved in COS development, including COS developers, trialists, systematic reviewers, journal editors, policy makers, and patient groups. As of January 2024, we have successfully completed the first stage of the study, with the stakeholder group approving the reported outcomes and assigning participant lists for each stakeholder group. CONCLUSIONS: This study will provide a roadmap for identifying the key health outcomes influenced by the interventions of FCNs. The multistakeholder, multiphase approach will ensure a comprehensive and inclusive process. Ultimately, the findings will enhance our understanding of FCNs' impact on health outcomes, leading to more effective primary care strategies and policies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51084.

Neural machine translation of clinical text: an empirical investigation into multilingual pre-trained language models and transfer-learning.

Clinical text and documents contain very rich information and knowledge in healthcare, and their processing using state-of-the-art language technology becomes very important for building intelligent systems for supporting healthcare and social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning such as Transformer based structures. Furthermore, to address the language resource imbalance issue, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including (1) clinical case (CC), (2) clinical terminology (CT), and (3) ontological concept (OC) show that our models achieved top-level performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) outperformed the other two extra-large language models by a large margin in the clinical domain fine-tuning, which finding was never reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new language space Spanish that was not seen at the pre-training stage within WMT21fb itself, which deserves more exploitation for clinical knowledge transformation, e.g. to investigate into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformation. Our data is openly available for research purposes at: https://github.com/HECTA-UoM/ClinicalNMT.

Physicians' Knowledge of the Systematic ABCDE Approach in Riyadh, Saudi Arabia.

Purpose: To evaluate physicians' knowledge of the ABCDE (Airway, Breathing, Circulation, Disability, Exposure) approach components. Methods: A cross-sectional study was conducted in 2023 using an online questionnaire in order to collect data about the knowledge of the ABCDE approach's components among physicians in different specialties in Riyadh, Saudi Arabia. Results: The number of participants were 165 in total and the median knowledge score for all participants was 15.0, with an associated interquartile range (IQR) of 10.0 to 20.0. Intensive Care Medicine had the highest median knowledge score of 19.0 (IQR: 12.0-21.0), followed by Internal Medicine at 17.0 (IQR: 13.0-20.0). Conversely, Cardiology and Anesthesiology showed lower scores, with medians of 8.0 (IQR: 4.0-10.0) and 7.5 (IQR: 4.0-13.5), respectively (p = 0.011). Senior Registrars demonstrated the highest median knowledge score of 20.0 (IQR: 14.0-22.0), while Fellows had the lowest at 8.5 (IQR: 7.0-13.0) (p < 0.001). Practicing for 10 to 15 years and more than 15 years having medians of 20.0 (IQR: 16.0-23.0) and 19.0 (IQR: 17.0-22.0), respectively. However, participants with less experience, working for less than 5 years, had a median score of 12.0 (IQR: 8.5-16.5) (p < 0.001). Conclusion: Knowledge scores of physicians representing various medical specialties found diverse levels regarding the ABCDE approach. Knowledge scores were significantly influenced by the primary area of practice, level of experience, and duration worked in the profession, highlighting the need for tailored training and education across different specialties and career stages. On the other hand, future studies should concentrate on finding new factors that influence practice adherence to the ABCDE approach and tying theoretical knowledge to clinical practice.

Healthcare Professional's Knowledge of the Systemic ABCDE Approach: A Cross-Sectional Study.

Background and aim Assessing the knowledge of healthcare professionals regarding the Airway, Breathing, Circulation, Disability, and Exposure (ABCDE) approach is crucial since it prioritizes the initial assessment and treatment of patients who are critically ill, regardless of the underlying cause or their age. Since adherence requires knowledge, this study aimed to evaluate the knowledge level of the ABCDE approach among healthcare professionals. Methods This cross-sectional study among healthcare professionals was performed from April to August 2023 in Taif City, Saudi Arabia. The study included healthcare professionals employed in departments exposed to patients with critical illnesses and excluded those from other specialties and individuals from outside Taif City. Data was collected via Google Forms using a previously validated questionnaire designed to assess the ABCDE approach knowledge among healthcare professionals. Statistical analysis was conducted using IBM SPSS, version 26. Results The study included 242 healthcare professionals with a mean (SD) age of 35.77 (9.93) years. About half of the participants were female (52.5%) nurses (50.8%) and had been working in intensive care units (ICU) and neonate intensive care units (NICU) (41.4%). The mean (SD) of the participants' working experience was 9.28 (8.295) years. The overall mean test score was 52.94 % (SD 16.27). The mean knowledge score among males was significantly higher than females (56.37% vs. 49.85%, respectively) (p-value= 0.001). The mean knowledge score was significantly higher among medical specialists and residents (63.308% and 55.67%) than among nurses (46.34%) (p-value <0.001). Attending an advanced trauma life support course and theoretical lecture significantly impacted the total knowledge score among the participants (p-values= 0.001 and <0.001, respectively). The total knowledge significantly increased with age (r: 0.265, p-value <0.001). Years of experience correlated with total knowledge score; with increasing years of experience, the total knowledge was significantly increased (r: 0.248, p-value <0.001). Conclusion The ABCDE approach is a valuable tool for the initial examination and treatment of patients in acute medical and surgical emergencies. The findings indicate that there is a need for further awareness programs and training on the ABCDE approach, as the total knowledge score among healthcare professionals was found to be suboptimal. Further research is needed to assess the association between knowledge level and clinical performance in different healthcare settings within Saudi Arabia.

The Business Process Management for Healthcare (BPM+ Health) Consortium: motivation, methodology, and deliverables for enabling clinical knowledge interoperability (CKI).

OBJECTIVES: To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. METHODS: We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following a gap analysis between existing BPM+ Health languages and clinical practice guideline knowledge types. Proposal requests were developed based on these requirements, and submission teams were formed to respond to them. The resulting proposals were submitted to OMG for ratification. RESULTS: The BPM+ Health family of languages, which initially consisted of the Business Process Model and Notation, Decision Model and Notation, and Case Model and Notation, was expanded by adding 5 new language standards through the OMG. These include Pedigree and Provenance Model and Notation for expressing epistemic knowledge, Knowledge Package Model and Notation for supporting packaging knowledge, Shared Data Model and Notation for expressing ontic knowledge, Party Model and Notation for representing entities and organizations, and Specification Common Elements, a language providing a standard abstract and reusable library that underpins the 4 new languages. DISCUSSION AND CONCLUSION: In this effort, we adopted a strategy of separation of concerns to promote a portfolio of domain-agnostic, independent, but integrated domain-specific languages for authoring medical knowledge. This strategy is a practical and effective approach to expressing complex medical knowledge. These new domain-specific languages offer various knowledge-type options for clinical knowledge authors to choose from without potentially adding unnecessary overhead or complexity.

Effectiveness of Clinical Management of COVID-19 Based on Structured Clinical Knowledge and Process Paths.

This study examined the effectiveness of a systematic approach to the clinical management of COVID-19, focusing on nursing turnover. METHODS: Between 2017 and 2019, a clinical process support system based on structured clinical knowledge (Team Compass with the Patient Condition Adaptive Path System; TC-PCAPS) was developed, and implemented in hospitals. In 2020, the COVID-19 clinical management system (COVID-19-CMS) was developed. In this study, the effectiveness of implementing both systems was analyzed. The analysis covered hospitals N, T, and B, where TC-PCAPS implementation started in 2019, 2020, and 2022, respectively. Data for the period from 2018 to 2022 were collected and compared. RESULTS: Hospitals N and T implemented TC-PCAPS in the first year and the COVID-19-CMS in the following year. The nurse turnover rates of these hospitals were lower than those of the prefectures in which they were located. There was a trend towards a gradual reduction in nurse turnover. In contrast, hospital B, which had only just started to introduce these systems, saw a gradual increase in nurse turnover. CONCLUSION: The data collected from these three hospitals suggested that this systematic approach has the potential to reduce nurse turnover, in addition to the previously reported ability of TC-PCAPS to reduce nurse overtime. In Japan, there is a need to respond to future pandemics and reform the work styles of physicians and nurses. The abovementioned systematic approach has great potential for contributing to both of these aims.

From the Midtown Manhattan Study to the Epidemiologic Catchment Area Study: the advent of mechanical objectivity in psychiatry.

In this paper I compare the methodology of two of the most famous epidemiological studies: The Midtown Manhattan Study (1952-60) and the Epidemiologic Catchment Area Study (1980-5). At first sight, there are few features that distinguish them; both were studies of large samples of the general population; they both used highly sophisticated methods of data analysis and standardized instruments; and they involved interviewers who were not professional clinicians. However, if we carefully compare the protocols that define how 'clinical' information is collected, we realize that some important changes in methodology were not only due to practical necessities, but also involved an important transformation in the role of the interviewer and the skills traditionally associated with the clinician.

Neonatal Jaundice Management: Improving Clinical Knowledge of Jaundice for Improved Attitudes and Practices to Enhance Neonatal Care.

Neonatal jaundice is a common medical condition that affects neonates in the early days of life. Nurses and midwives play important role in the identification and management of neonatal jaundice and the promotion of good neonatal health and education. Their clinical knowledge of neonatal jaundice may influence their attitude and practices toward the identification and management of neonatal jaundice. The study results showed that the level of good knowledge, attitudes, and practices toward neonatal jaundice management was 69.30% (140/202), 64.90% (131/202), and 62.90% (127/202), respectively. The inferential statistics showed a positive association between good knowledge and attitudes toward neonatal jaundice and good practices of neonatal jaundice management. Suggestively, nurses and midwives who have and demonstrate better clinical knowledge and exhibit positive attitudes are more likely to implement appropriate practices for the management of neonatal jaundice. Healthcare providers should therefore invest in life-long learning activities for staff, especially in the study setting.

Shape Expressions (ShEx) schemas for the FHIR R5 specification.

This work continues along a visionary path of using Semantic Web standards such as RDF and ShEx to make healthcare data easier to integrate for research and leading-edge patient care. The work extends the ability to use ShEx schemas to validate FHIR RDF data, thereby enhancing the semantic web ecosystem for working with FHIR and non-FHIR data using the same ShEx validation framework. It updates FHIR's ShEx schemas to fix outstanding issues and reflect changes in the definition of FHIR RDF. In addition, it experiments with expressing FHIRPath constraints (which are not captured in the XML or JSON schemas) in ShEx schemas. These extended ShEx schemas were incorporated into the FHIR R5 specification and used to successfully validate FHIR R5 examples that are included with the FHIR specification, revealing several errors in the examples.

A Survey of Otolaryngology Residency Programs on Adapting to the United States Medical Licensing Examination (USMLE) Step 1 Transitioning to Pass/Fail.

Objectives In February 2020, the National Board of Medical Examiners (NBME) announced that the United States Medical Licensing Examination (USMLE) Step 1 licensing examination would change from a numerical score to Pass/Fail (P/F). After implementation, many believe that USMLE-Step 2-Clinical Knowledge (CK) will become an important metric for students applying to otolaryngology (ENT). The purpose of this study is to determine factors important to resident selection after these changes. Methods A survey containing 15 questions related to resident selection practices and how changing USMLE Step 1 to P/F would impact future resident selection was designed. It was distributed to all ENT residency programs accredited by the Accreditation Council for Graduate Medical Education (ACGME). Results Forty percent of programs responded; 66% (95% confidence interval (CI): 51.1%-78.4%) felt that changing Step 1 scoring would not lead to students being more prepared for clinical rotations; 55% believe class rank will increase in significance (95% CI: 35.7%-64.3%). There was also an increase in the importance of Step 2 CK, which had a mean ranking of 10.67 prior to changes in Step 1 scoring and increased to 7.80 after P/F. Conclusions The changes in Step 1 scoring will likely lead to increasing importance of other objective measures like class rank or Step 2 CK. This may defeat the intended purpose put forth by the NBME. Therefore, further guidance on measures correlated with student performance as a resident will be integral to the selection process.

Evidence Hub: A place to exchange medical knowledge and form communities.

Introduction: Medical knowledge is complex and constantly evolving, making it challenging to disseminate and retrieve effectively. To address these challenges, researchers are exploring the use of formal knowledge representations that can be easily interpreted by computers. Methods: Evidence Hub is a new, free, online platform that hosts computable clinical knowledge in the form of "Knowledge Objects". These objects represent various types of computer-interpretable knowledge. The platform includes features that encourage advancing medical knowledge, such as public discussion threads for civil discourse about each Knowledge Object, thus building communities of interest that can form and reach consensus on the correctness, applicability, and proper use of the object. Knowledge Objects are maintained by volunteers and published on Evidence Hub under GPL 2.0. Peer review and quality assurance are provided by volunteers. Results: Users can explore Evidence Hub and participate in discussions using a web browser. An application programming interface allows applications to register themselves as handlers of specific object types and provide editing and execution capabilities for particular object types. Conclusions: By providing a platform for computable clinical knowledge and fostering discussion and collaboration, Evidence Hub improves the dissemination and use of medical knowledge.

Clinical learning for pre-registration nursing students: a viva voce approach during COVID-19.

The COVID-19 pandemic restricted face-to-face contact between students and educators, limiting continual assessment of student's clinical skill development. This led to rapid transformational online adaptations to nursing education. This article will present and discuss the introduction of a clinical 'viva voce' approach, which has been used at one university to formatively assess students' clinical learning and reasoning skills using virtual methods. The Virtual Clinical Competency Conversation (V3C) was developed using the 'Think aloud approach' and involved facilitated one-to-one discussion based on two questions from a bank of 17 predefined clinically focused questions. A total of 81 pre-registration students completed the formative assessment process. Overall, feedback from students and academic facilitators was positive and facilitated both learning and consolidation in a safe and nurturing way. Further local evaluation is continuing to measure the impact of the V3C approach on student learning now that some aspects of face-to-face education have resumed.

Facilitating and Inhibiting Factors of Clinical Knowledge Sharing among Medical Specialists in University Hospitals.

Background: Clinical knowledge sharing (CKS) is one of the key points of knowledge management in the field of health and significantly increases the quality of care and patient safety. It also provides the achievement of an efficient system in hospitals and educational and treatment centers involved in clinical processes in order to make the best clinical decisions. The purpose of this research is to identify the factors that facilitate and inhibit CKS among medical specialists in the educational-treatment hospitals in Iran. Methods: This was an applied qualitative study with the conventional content analysis method conducted in 2022. The data collection tool was a semi-structured interview. The participants were 13 medical specialists and sub-specialists working in educational-treatment hospitals of the country, who were selected by purposeful and snowball sampling. The method of data analysis was based on Graneheim and Lundman's five-step method, which was followed by codes, sub-categories, main categories, and classifications. Results: After conducting the interviews and assessing their content, finally, 193 codes were extracted, which were identified in two general classification of facilitating and inhibiting factors with 92 and 101 concepts, respectively. Facilitating factors in the three main categories of "education in the context of culture, society and university", "planning and implementation management", and "behavioral-motivational factors" and inhibiting factors in the four main categories of "infrastructural, policy-making and cultural challenges", "technological and scientific infrastructural challenges", "personality-behavioral challenges", and "financial and non-financial motivations" were classified. Conclusions: The participants of the research pointed out the effective role of CKS in keeping them up-to-date in the use of diagnostic, therapeutic, and even drug prescribing methods. According to their belief, knowledge sharing (KS) in the clinical setting will reduce diagnostic errors and cause the primordial prevention of diseases as well as increase the knowledge and awareness of the society members.

Student Satisfaction and Self-Efficacy in a Novel Online Clinical Clerkship Curriculum Delivered During the COVID-19 Pandemic.

Purpose: The COVID-19 pandemic has caused significant disruption to medical education and clinical training and resulted in stressors that impede student learning. This study aimed to assess student satisfaction and self-efficacy in a novel online clinical clerkship curriculum delivered during the COVID-19 pandemic. Methods: Fourth- and fifth-year medical students completed an online survey in January 2021 covering the following areas: student satisfaction, self-efficacy, and perceived effectiveness of online versus face-to-face learning. Results: Just over half of students (51%) were satisfied with online clerkship delivery. However, fewer than half of students (46%) believed online learning effectively increased their knowledge, compared to 56% of students who believed face-to-face learning was effective. The perception of the effectiveness of online learning and face-to-face teaching for clinical skills was 18% and 89%, respectively (p < 0.0001). Few students perceived online teaching to be effective for developing social competencies (27%) compared to face-to-face instruction (67%) (p < 0.001). In addition, mean self-efficacy scores were higher for persons who perceived online teaching to be effective for increasing knowledge, improving clinical skills, and developing social competencies. Overall, students' perception of online learning was strongly associated with online self-efficacy. Conclusion: Student satisfaction and perceived self-efficacy in online learning were higher than reported acceptance of online clerkship curriculum.