Clinical Decision-making Ability Research Articles

A neurosurgical nursing training system based on 3D printing models: practice and exploration of precision medicine

BackgroundIn recent years, the concept of precision medicine has gradually permeated the field of nursing, presenting new challenges for neurosurgical nursing staff. Traditional teaching methods often struggle to adequately convey complex anatomical knowledge and nursing skills, thereby affecting the learning outcomes and clinical decision-making abilities of nurses. As an innovative educational tool, three-dimensional (3D) printing technology has demonstrated significant potential in medical education in recent years. Therefore, this project aims to explore the advantages and feasibility of using 3D printed models as a novel teaching tool in neurosurgical nursing.MethodsThe study involved 80 nursing personnel who participated in the neurosurgical rotation training at Shaoxing People’s Hospital from 2022 to 2023. The control group (40 individuals) used only traditional teaching tools such as textbooks, multimedia courseware, and laboratory equipment for theoretical and practical training based on the curriculum. The experimental group (40 individuals) incorporated a 3D printing model database as an innovative teaching tool on the basis of traditional teaching methods, and used patient-specific 3D printed models for precise nursing training. After the training, both groups were assessed for theoretical and practical abilities, professional values, job satisfaction, and the prognosis of intracranial aneurysm patients they cared for.ResultsThe experimental group demonstrated superior scores in theoretical and practical abilities, professional values, and job satisfaction compared to the control group. During the training period, patients with intracranial aneurysms receiving precision nursing care from the experimental group showed better quality of life and neurological functional outcomes compared to the control group.ConclusionsThe nursing training system based on 3D printing models can enhance the learning efficiency and nursing work satisfaction of nursing personnel. It may have a positive impact on the professional values of nursing personnel, thereby laying a solid foundation for providing high-quality, precise nursing services in the field of neurosurgery in the future.

간호대학생의 간호인계역량에 미치는 영향요인

Objectives This study attempted to investigate the factors affecting nursing students' nursing handover competence and to identify the relationship between clinical decision-making ability, clinical reasoning ability, and handover self-efficacy to serve as a basis for future handover education courses for nursing students. Methods This study is a descriptive survey study. 145 nursing students The data were collected from September 26, 2023 to October 13, 2023 from 145 nursing students at a university. The data were analyzed by frequency and percentage, independent t-test, one-way ANOVA, and Pearson Correlation Coefficient using SPSS/WIN 28.0 program. Results Nursing handoff competency was positively correlated with clinical decision-making ability (r=.384, p=.000), clinical reasoning ability (r=.541, p=.000), and handoff self-efficacy (r=.656, p=.000). The most significant predictors of nursing handoff competence were handoff self-efficacy (β=0.422, p<.001), clinical reasoning ability (β=0.270, p<.001), Satisfaction with Academic Major (β=0.192, p=.019), grade ((β=-.13, p=.036), and Handover Fear (β=-0.224, p=.007), with an explanatory power of 49.0%. Conclusions The factors that affect nursing handover competency are cognitive processes, such as problem solving, communication, judgment, and perception, and efforts are needed to maintain the quality and continuity of care. Therefore, it is necessary to develop teaching methods and build systems to improve clinical decision-making, clinical reasoning, handover self-efficacy, and enhance nursing handover competency.

Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.

A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.

Exploring the moderating role of health-promoting behaviours and self-compassion on the relationship between clinical decision-making and nurses' well-being.

Clinical decision-making is an essential part of the nursing role and has implications for both patient care and nurses' well-being. This study aimed to explore the relationship between nurses' perceptions of clinical decision-making ability and moral distress across a nursing population, and the potential link to self-compassion and health-promoting behaviours. A self-report questionnaire was distributed to a sample of nurses (N = 152) from April to September 2022. The survey explored nurses' perceptions of clinical decision-making ability, moral distress, physical activity, grazing, stress-eating, burnout and self-compassion. Perceived clinical decision-making ability was associated with moral distress experience, and both self-compassion and grazing moderated this relationship, independently. Findings highlight the link between nurses' perceptions of clinical decision-making ability and moral distress experience. Both eating behaviours and self-compassion influence the relationship between these two factors and identify potential areas that may support (and hinder) nurses' well-being through clinical decision-making. These findings reinforce the importance of healthy eating habits and being self-compassionate to prevent moral distress arising as a result of clinical decision-making.

Recognizing the involvement of students in nursing and physiotherapy contexts

This paper examines the critical role that preceptors in physiotherapy and nursing play in clinical settings, emphasising their contributions to patient care and professional growth. Students who participate in clinical internships gain invaluable practical experience that connects their theoretical understanding to real-world applications. Students who work in nursing contexts are exposed to high-pressure environments and develop their clinical decision-making and collaboration abilities through involvement in acute care. Students studying physiotherapy provide a substantial contribution to rehabilitation units, where their involvement aids in patients' recuperation and fosters the development of critical therapeutic skills. The best practices for integrating students into clinical teams are also included in this review, with a focus on the value of a structured orientation, ongoing mentorship, and creating a collaborative atmosphere. It also looks at the difficulties and impediments to successful student involvement, such as institutional limitations, psychological strain, and the requirement for strong coping skills. Retaining patient trust and providing high-quality care depend heavily on ethical factors like informed consent and confidentiality. The evaluation emphasises how clinical exposure helps students become career ready by preparing them for the challenges of professional practice in the long run. To maximise student experiences and results, interdisciplinary learning opportunities and efficient team integration are crucial. The review sheds light on these areas and emphasises the necessity of ongoing funding and innovation in clinical education in order to produce skilled, self-assured, morally upright healthcare workers.

Exploring the impact of collaborative learning on the development of critical thinking and clinical decision-making skills in nursing students: A quantitative descriptive design

This study aims to introduce collaborative learning as a method in the Mental Health and Diseases Nursing course for actively enrolled fourth-year nursing students. The objective is to assess the impact of collaborative learning on critical thinking and clinical decision-making among nursing students. Collaborative learning involves the use of small groups to help students identify effective ways of working together to enhance their learning outcomes. A quantitative research methodology was employed in this study. To evaluate the effectiveness of the collaborative learning program, a within-subject repeated-measures design was implemented. This study was conducted in the nursing department of a university in Turkey. The study included 96 female students (92.3 %) and 8 male students (7.7 %). Data collection involved the sociodemographic data form, the California Critical Thinking Disposition Inventory (CCTDI), and the Clinical Decision-Making in Nursing Scale (CDMNS). The students' pre-program CDMNS median score was 146.00 (117.00–177.00), and the post-program CDMNS median score was 147.50 (115.00–175.00). While there was an increase in the students' clinical decision-making scores after the program, this increase was not statistically significant (p > 0.05). The pre-program median score for CCTDI was 223.26 (176.87–296.02), and the post-program median score was 227.88 (188.87–359.00). The students' critical thinking disposition scores showed a statistically significant increase after the program (p < 0.05). The study results revealed notable enhancements among students who participated in courses utilizing the collaborative learning method. These enhancements included heightened academic performance, elevated levels of critical thinking, increased self-confidence, and improved clinical decision-making abilities.

Cultivating Student Competencies and Practical Exploration in the Pharmaceutical Service Teaching Model

With the increasing demand for healthcare and the rapid development of the pharmaceutical service field, pharmaceutical education faces new challenges and opportunities. Traditional pharmaceutical service te aching models can no longer fully meet the requirements of modern pharmaceutical talent cultivation, necessitating the integration of modern educational technologies and innovative teaching methods. This paper first provides an overview of the existing pharmaceutical service teaching models and then explores the cultivation of student competencies within this model, including systematic knowledge and skills development, formation of clinical decision-making abilities, enhancement of communication and collaboration skills, and education on professional ethics and regulatory compliance. Finally, this paper proposes innovative practices in the pharmaceutical service teaching model, such as innovations in practice bases and internship projects, the application of virtual reality and simulation technologies, and the development of interdisciplinary teaching projects, aiming to provide new ideas and methods for pharmaceutical education.

The relationship between clinical decision-making levels and self-efficacy levels of operating room nurses

BackgroundClinical decision-making and self-efficacy levels of operating room nurses are critical for patient care quality and safety. ObjectiveThis study aims to examine the relationship between clinical decision-making levels and self-efficacy among operating room nurses and to identify factors influencing these two key components of nursing practice. MethodsA cross-sectional descriptive design was used to survey 94 nurses working in surgical wards at Ankara University Medical Faculty Hospital. Data were collected using the Clinical Decision Making in Nursing Scale (CDMNS), General Self-Efficacy Scale (GSES), and a demographic information form. Data were analyzed using descriptive statistics, correlation analysis, and group comparisons. ResultsA significant positive correlation was found between clinical decision-making ability and self-efficacy (r = 0.355, p < .0001). Nurses with higher education levels demonstrated significantly greater clinical decision-making skills (p < .005). Nurse managers reported significantly higher self-efficacy levels compared to scrub/circulating nurses (p < .05). Age was positively correlated with self-efficacy (r = 0.373, p < .0001) but not with clinical decision-making ability. ConclusionThe research indicated that self-efficacy levels correspond with clinical decision-making capabilities of the operating room nurses. Higher education levels of the practitioners enhanced their clinical decision-making abilities. Nurse managers had higher self-efficacious beliefs than the scrub or circulating nurses. Age was positively associated with self-efficacy, but not with the scores on clinical decision-making abilities. The results of this study nurse leaders have higher GSES and CDMNS scores because they are experienced nurses with higher education degrees.

Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework

IntroductionIn low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs). ObjectivesThe primary objective was to explore IRs’ beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored. Materials and MethodsIn-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF. ResultsEleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs’ clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs’ understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited. ConclusionUsing TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

The effect of nomophobic behaviors among nurses on their clinical decision-making perceptions

AimThis study aimed to explore the impact of nomophobic behaviors among hospital nurses on their clinical decision-making perceptions. This understanding can offer insights to enhance the work environment, improve the clinical decision-making ability of nurses and guide medical institutions in the management of related equipment and policy development. BackgroundThe term "nomophobia" refers to the anxiety and fear individuals experience when they cannot use their smartphones or when smartphones are not accessible. Nursing clinical decision-making is a complex process, including a meticulous assessment of the patient's pathological condition and medical history, alongside the application of nursing knowledge and experiential learning rooted in critical thinking. The concept of clinical decision-making perceptions is defined as a deliberate cognitive understanding of one's decision-making processes, which significantly influences the clinical decision-making capabilities of healthcare professionals, thereby impacting the quality of patient care. The factors influencing these clinical decision-making perceptions have been the subject of extensive research. However, there is no Chinese research on the impact of nurses ' nomophobic behaviors on their clinical decision-making perception. DesignA cross-sectional descriptive survey using online-based delivery modes was used. MethodsA descriptive cross-sectional survey design was employed. Using convenience sampling, we surveyed the nurses from a tertiary hospital in Nanjing in May 2023. Data were gathered using a sociodemographic data form, the Nomophobia Questionnaire and the Clinical Decision-Making in Nursing Scale. Techniques including the independent sample t-test, one-way ANOVA, Pearson correlation analysis and linear regression analysis were employed to probe the degree of nomophobia and its effects on their perception of clinical decision-making. Out of 284 questionnaires gathered, 272 were deemed valid, resulting in a 95.8% effective response rate. ResultsThe data revealed that participants exhibited a medium level of nomophobia (54.01 ± 24.09) and clinical decision-making perceptions (144.94 ± 20.08). A robust negative correlation was discerned between nomophobia and clinical decision-making perceptions (r: −0.365, P<0.001). This study highlighted that as the degree of nomophobia intensified, nurses' clinical decision-making perceptions decreased with the increase in nomophobia. ConclusionNomophobic behaviors can hamper nurses' perception of clinical decision-making, potentially leading to inaccuracies or errors. Nurses must use mobile phones judiciously, practice self-regulation and mitigate the disruptive effects of nomophobia on their decision-making. In addition, medical institutions should foster relevant education or craft policies to regularize mobile phone use, augmenting nurses' efficiency and decision-making prowess, enhancing patient care quality, diminishing medical errors and ensuring patient health and safety.

Influences of Nursing Professionalism, Empathy, and Clinical Decision-Making Ability on Shared Decision-Making Awareness among Hemodialysis Nurses

Aim. To examine the relationships among nursing professionalism, empathy, and clinical decision-making ability and the factors influencing shared decision-making awareness in hemodialysis nurses. Background. Self-management and treatment for hemodialysis patients are essential for maintaining health and life in daily life. In this process, shared decision-making in which patients, nurses, and medical teams participate and make decisions together has a greater impact on the health recovery and improvement of quality of life for hemodialysis patients than for any other chronic disease patients. Methods. A cross-sectional descriptive design was employed. Participants were 145 nurses working in the hemodialysis centers at hospitals in Seoul and Gyeonggi-do. Measures included the general characteristics of study participants, nursing professionalism, empathy, clinical decision-making ability, and shared decision-making awareness. Data were collected from May to July, 2022, and multiple linear regression analysis was used to examine the predictive factors of shared decision-making awareness. Results. The strongest predictor was empathy, followed by clinical decision-making ability and the level of education. The explanatory power of the final regression model was 23%. Conclusions. Empathy towards hemodialysis patients was an important factor influencing the shared decision-making awareness in hemodialysis nurses. Implications for Nursing Management. In nursing management, nursing managers or nurses should pay attention to influencing factors to improve the shared decision-making awareness of hemodialysis nurses. Empathy towards hemodialysis patients need to be reinforced to improve the shared decision-making awareness of hemodialysis nurses.

Pan-Indian Clinical Registry of Invasive Fungal Infections Among Patients in the Intensive Care Unit: Protocol for a Multicentric Prospective Study.

Fungal infections are now a great public health threat, especially in those with underlying risk factors such as neutropenia, diabetes, high-dose steroid treatment, cancer chemotherapy, prolonged intensive care unit stay, and so on, which can lead to mycoses with higher mortality rates. The rates of these infections have been steadily increasing over the past 2 decades due to the increasing population of patients who are immunocompromised. However, the data regarding the exact burden of such infection are still not available from India. Therefore, this registry was initiated to collate systematic data on invasive fungal infections (IFIs) across the country. The primary aim of this study is to create a multicenter digital clinical registry and monitor trends of IFIs and emerging fungal diseases, as well as early signals of any potential fungal outbreak in any region. The registry will also capture information on the antifungal resistance patterns and the contribution of fungal infections on overall morbidity and inpatient mortality across various conditions. This multicenter, prospective, noninterventional observational study will be conducted by the Indian Council of Medical Research through a web-based data collection method from 8 Advanced Mycology Diagnostic and Research Centers across the country. Data on age, gender, clinical signs and symptoms, date of admission, date of discharge or death, diagnostic tests performed, identified pathogen details, antifungal susceptibility testing, outcome, and so on will be obtained from hospital records. Descriptive and multivariate statistical methods will be applied to investigate clinical manifestations, risk variables, and treatment outcomes. These Advanced Mycology Diagnostic and Research Centers are expected to find the hidden cases of fungal infections in the intensive care unit setting. The study will facilitate the enhancement of the precision of fungal infection diagnosis and prompt treatment modalities in response to antifungal drug sensitivity tests. This registry will improve our understanding of IFIs, support evidence-based clinical decision-making ability, and encourage public health policies and actions. Fungal diseases are a neglected public health problem. Fewer diagnostic facilities, scanty published data, and increased vulnerable patient groups make the situation worse. This is the first systematic clinical registry of IFIs in India. Data generated from this registry will increase our understanding related to the diagnosis, treatment, and prevention of fungal diseases in India by addressing pertinent gaps in mycology. This initiative will ensure a visible impact on public health in the country. DERR1-10.2196/54672.

Development of clinical decision-making among undergraduate nursing students: the effect of unfolding case-based learning.

This prospective cohort study evaluated the effect of unfolding case-based learning on undergraduate nursing students' self-perceived clinical decision-making ability. Students' self-reported responses to Jenkins's Clinical Decision Making in Nursing Scale were compared between the unfolding case-based learning cohort (n=140) and the comparison cohort (n=126) at a school of nursing in the United States. The results revealed similar students' responses between the two study cohorts. However, unfolding case-based learning significantly increased students' perceived proficiency in "search for information and unbiased assimilation of new information". Findings from the present study highlight possibilities presented by unfolding case-based learning in undergraduate nursing education. The study supports that unfolding case studies can be introduced early on, and then nurtured throughout the undergraduate program to influence the development of nursing students' clinical decision-making skills.

A predictive nomogram for surgical site infection in patients who received clean orthopedic surgery: a retrospective study

BackgroundSurgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI.MethodsA total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram.ResultsThe SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029–5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006–1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022–1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672–0.882) and 0.732 (95% CI 0.603–0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively.ConclusionsOperative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.

Classification and prognostic factors of patients with cervical spondylotic myelopathy after surgical treatment: a cluster analysis

Identifying potential prognostic factors of CSM patients could improve doctors’ clinical decision-making ability. The study retrospectively collected the baseline data of population characteristics, clinical symptoms, physical examination, neurological function and quality of life scores of patients with CSM based on the clinical big data research platform. The modified Japanese Orthopedic Association (mJOA) score and SF-36 score from the short-term follow-up data were entered into the cluster analysis to characterize postoperative residual symptoms and quality of life. Four clusters were yielded representing different patterns of residual symptoms and quality of patients’ life. Patients in cluster 2 (mJOA RR 55.8%) and cluster 4 (mJOA RR 55.8%) were substantially improved and had better quality of life. The influencing factors for the better prognosis of patients in cluster 2 were young age (50.1 ± 11.8), low incidence of disabling claudication (5.0%) and pathological signs (63.0%), and good preoperative SF36-physiological function score (73.1 ± 24.0) and mJOA socre (13.7 ± 2.8); and in cluster 4 the main influencing factor was low incidence of neck and shoulder pain (11.7%). We preliminarily verified the reliability of the clustering results with the long-term follow-up data and identified the preoperative features that were helpful to predict the prognosis of the patients. This study provided reference and research basis for further study with a larger sample data, extracting more patient features, selecting more follow-up nodes, and improving clustering algorithm.

Implementation of Competency-Based Education for Quality Midwifery Programs in Africa: Protocol for a Scoping Review.

Competency-based education (CBE) for midwifery programs is a system of academic instruction and evaluation that aims to achieve proficiency in midwifery student learning outcomes, which is based on students' ability to demonstrate the knowledge, attitudes, self-perceptions, and skills of a predetermined set of educational activities in theory and practice. CBE focuses on ensuring that midwifery students can develop critical thinking abilities, values, and the clinical decision-making abilities needed for the delivery of safe care in future practice. The objective of this scoping review is to map and synthesize existing literature on the implementation of CBE for midwifery programs and its sustainability in Africa. We will use the Arksey and O'Malley approach for scoping reviews for the research methodology. The 3-stage search process, proposed by the Joanna Briggs Institute, will be used to determine the eligibility of published and unpublished studies. PubMed, Science Direct, Web of Science, CINAHL, PsycINFO, and Scopus will be searched to screen published articles. ProQuest Dissertations and Theses and Google Scholar will be used to search for unpublished studies. Findings will only apply to studies conducted in Africa from 2010 to the present year in English. The 2 reviewers will work independently to carefully screen and compare the full text of the selected citations to the inclusion criteria. In the event of any disagreements between the 2 reviewers at any stage of the selection process regarding the inclusion of an article, this will be settled by discussion or consultation with a third reviewer. The extracted data will be presented using a PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review) flow diagram with an attached narrative summary. The review will summarize and disseminate findings on the implementation of the CBE for midwifery programs and its sustainability in Africa. It is intended that this scoping review will be completed within 6 months following the publication of this protocol. The conclusions from this scoping review will inform midwifery educators, institutions, policy makers, and other stakeholders on the strategies to implement and sustain CBE for midwifery programs in Africa. PRR1-10.2196/47603.

Development and Effects of a Virtual Reality Simulation Nursing Education Program Combined With Clinical Practice Based on an Information Processing Model.

The need to strengthen patient human rights and create a patient-centered healthcare environment is growing. Also as science and technology develop, new educational methods using virtual reality in nursing education are emerging. This study aimed to develop a virtual reality simulation nursing education program related to postoperative patient nursing based on an information processing model and to verify its effectiveness. Clinical practice-linked virtual reality simulation nursing education was conducted for a total of 4 weeks. Nursing students were divided into an experimental group (n = 22) experiencing virtual reality simulation combined with clinical practice and a control group (n = 22) having routine clinical practice. The analytical results of this study indicated that the information processing model-based virtual reality simulation nursing education program was effective in improving nursing students' performance confidence and clinical decision-making ability. Therefore, the virtual reality simulation program developed in this study can provide basic data for the development of a simulation curriculum in the future and can contribute to the development of clinical competency as a professional nurse by improving the performance confidence and clinical decision-making ability of nursing students.

How does ChatGPT-4 preform on non-English national medical licensing examination? An evaluation in Chinese language.

ChatGPT, an artificial intelligence (AI) system powered by large-scale language models, has garnered significant interest in healthcare. Its performance dependent on the quality and quantity of training data available for a specific language, with the majority of it being in English. Therefore, its effectiveness in processing the Chinese language, which has fewer data available, warrants further investigation. This study aims to assess the of ChatGPT's ability in medical education and clinical decision-making within the Chinese context. We utilized a dataset from the Chinese National Medical Licensing Examination (NMLE) to assess ChatGPT-4's proficiency in medical knowledge in Chinese. Performance indicators, including score, accuracy, and concordance (confirmation of answers through explanation), were employed to evaluate ChatGPT's effectiveness in both original and encoded medical questions. Additionally, we translated the original Chinese questions into English to explore potential avenues for improvement. ChatGPT scored 442/600 for original questions in Chinese, surpassing the passing threshold of 360/600. However, ChatGPT demonstrated reduced accuracy in addressing open-ended questions, with an overall accuracy rate of 47.7%. Despite this, ChatGPT displayed commendable consistency, achieving a 75% concordance rate across all case analysis questions. Moreover, translating Chinese case analysis questions into English yielded only marginal improvements in ChatGPT's performance (p = 0.728). ChatGPT exhibits remarkable precision and reliability when handling the NMLE in Chinese. Translation of NMLE questions from Chinese to English does not yield an improvement in ChatGPT's performance.

Evaluating the Clinical Decision-Making Ability of Large Language Models Using MKSAP-19 Cardiology Questions

Evaluating the Clinical Decision-Making Ability of Large Language Models Using MKSAP-19 Cardiology Questions