Healthcare Education Research Articles

REVOLUTIONIZING HEALTHCARE THROUGH AUGMENTED REALITY INTEGRATION IN MEDICAL TRAINING

Through the transformation of conventional anatomy education techniques, the incorporation of augmented reality (AR) into medical training is revolutionising healthcare education. This creative method uses augmented reality (AR) tools, such as Microsoft® HoloLens II, to improve anatomy lab courses and give students a more engaging and dynamic learning environment. Medical schools can simplify anatomy instruction by using holograms to replicate human dissection. This allows students to study intricate anatomical structures in a step-by-step three-dimensional presentation. In addition to optimising the amount of time spent on dissection, this move towards AR-based learning creates new opportunities for remote and independent learning that meet the needs and preferences of a wide range of learners. Furthermore, the challenges that traditional cadaver-based anatomy instruction faces are addressed by integrating AR into medical education. AR offers a thorough and captivating learning environment because it can present anatomical material in different sequences, from superficial to deep structures, or by creating functional body units. Clinical correlations can be easily incorporated into lessons to improve students' comprehension and memorization of anatomy. The future of augmented reality (AR) in medical education appears bright, with potential breakthroughs in pathology, histology, and surgical applications, despite obstacles like realism constraints and technological complexity. Key Words: Augmented Reality (AR), Medical Training, Healthcare Education, Microsoft HoloLens, Anatomy Education, Validation Process

Global consensus statement on simulation-based practice in healthcare

Simulation plays a pivotal role in addressing universal healthcare challenges, reducing education inequities, and improving mortality, morbidity and patient experiences. It enhances healthcare processes and systems, contributing significantly to the development of a safety culture within organizations. It has proven to be cost-effective and successful in enhancing team performance, fostering workforce resilience and improving patient outcomes.Through an international collaborative effort, an iterative consultation process was conducted with 50 societies operating across 67 countries within six continents. This process revealed common healthcare challenges and simulation practices worldwide. The intended audience for this statement includes policymakers, healthcare organization leaders, health education institutions, and simulation practitioners. It aims to establish a consensus on the key priorities for the broad adoption of exemplary simulation practice that benefits patients and healthcare workforces globally.Key recommendations Advocating for the benefits that simulation provides to patients, staff and organizations is crucial, as well as promoting its adoption and integration into daily learning and practice throughout the healthcare spectrum. Low-cost, high-impact simulation methods should be leveraged to expand global accessibility and integrate into system improvement processes as well as undergraduate and postgraduate curricula. Support at institutional and governmental level is essential, necessitating a unified and concerted approach in terms of political, strategic and financial commitment.It is imperative that simulation is used appropriately, employing evidence-based quality assurance approaches that adhere to recognized standards of best practice. These standards include faculty development, evaluation, accrediting, credentialing, and certification.We must endeavor to provide equitable and sustainable access to high-quality, contextually relevant simulation-based learning opportunities, firmly upholding the principles of equity, diversity and inclusion. This should be complemented with a renewed emphasis on research and scholarship in this field.Call for action We urge policymakers and leaders to formally acknowledge and embrace the benefits of simulation in healthcare practice and education. This includes a commitment to sustained support and a mandate for the application of simulation within education, training, and clinical environments.We advocate for healthcare systems and education institutions to commit themselves to the goal of high-quality healthcare and improved patient outcomes. This commitment should encompass the promotion and resource support of simulation-based learning opportunities for individuals and interprofessional teams throughout all stages and levels of a caregiver’s career, in alignment with best practice standards.We call upon simulation practitioners to champion healthcare simulation as an indispensable learning tool, adhere to best practice standards, maintain a commitment to lifelong learning, and persist in their fervent advocacy for patient safety.This statement, the result of an international collaborative effort, aims to establish a consensus on the key priorities for the broad adoption of exemplary simulation practice that benefits patients and healthcare workforces globally.

Educating patients on osteoporosis and bone health: Can "ChatGPT" provide high-quality content?

The rise of artificial intelligence (AI) models like ChatGPT offers potential for varied applications, including patient education in healthcare. With gaps in osteoporosis and bone health knowledge and adherence to prevention and treatment, this study aims to evaluate the accuracy of ChatGPT in delivering evidence-based information related to osteoporosis. Twenty of the most common frequently asked questions (FAQs) related to osteoporosis were subcategorized into diagnosis, diagnostic method, risk factors, and treatment and prevention. These FAQs were sourced online and inputted into ChatGPT-3.5. Three orthopedic surgeons and one advanced practice provider who routinely treat patients with fragility fractures independently reviewed the ChatGPT-generated answers, grading them on a scale from 0 (harmful) to 4 (excellent). Mean response accuracy scores were calculated. To compare the variance of the means across the four categories, a one-way analysis of variance (ANOVA) was used. ChatGPT displayed an overall mean accuracy score of 91%. Its responses were graded as "accurate requiring minimal clarification" or "excellent," with a mean response score ranging from 3.25 to 4. No answers were deemed inaccurate or harmful. No significant difference was observed in the means of responses across the defined categories. ChatGPT-3.5 provided high-quality educational content. It showcased a high degree of accuracy in addressing osteoporosis-related questions, aligning closely with expert opinions and current literature, with structured and inclusive answers. However, while AI models can enhance patient information accessibility, they should be used as an adjunct rather than a substitute for human expertise and clinical judgment.

Acceptance of virtual patients as a continuous professional development approach among practicing nurses in primary health care settings in a low-income country: a quasi-experimental posttest setup design

BackgroundVirtual patients are an educational technological approach used in healthcare education. Its distinctive features have rendered virtual patient technology appealing for the training of medical and healthcare students, particularly in the enhancement of clinical reasoning. Virtual patients are less often applied for continuous professional development for practicing healthcare providers, and there is a scarcity of studies exploring this possibility. This study aimed to assess the acceptability of nurses for using virtual patients as a continuous professional development approach.MethodThe study used a quasi-experimental posttest setup design. The study was conducted in ten primary healthcare settings in Rwanda. Among 76 nurses who consented to participate in the study, 56 completed the intervention and responded to the study questionnaire. Following a one-week program of continuous professional development on four non-communicable diseases, the study used a self-administered questionnaire based on the Technology Acceptance Model 3 to collect data. Descriptive analysis served as the primary method for analyzing participants’ responses. The study also used a correlation test to assess the relationship of variables.ResultsAcross all items in the questionnaire, the median response tended towards either agree or strongly agree, with only a minority number of participants expressing strong disagreement, disagreement, or neutrality. The results indicated a significant positive correlation between perceived usefulness and behavior intention (p < 0.001).ConclusionThe findings indicate an acceptability and behavioral intention of adopting virtual patients as an alternative continuous professional development approach among nurses working at health centers in Rwanda or other locations with similar contexts.

The potential of virtual healthcare technologies to reduce healthcare services’ carbon footprint

The COVID-19 pandemic demonstrated the potential to reduce our carbon footprint especially by reducing travel. We aim to describe healthcare and health education services’ contribution to the global climate emergency and identify the need for increased use of virtual health service delivery and undergraduate/postgraduate education to help reduce the impact of health service and health education delivery on the environment. Health care services, as one of the largest contributors to carbon emissions, must take steps to rapidly reduce their carbon footprint. Health services have unfortunately paid little attention to this issue until recently. Virtual healthcare and education have a valuable role in transition to a net carbon-zero outcome. Given the increasing use of and satisfaction with virtual health services such as telehealth, and the increase in virtual education opportunities, it is important that a concerted effort is undertaken to increase their use across health services and education in the future.

What are the barriers to health professionals' training on gender-affirming care from patients' and clinicians’ perspectives?

ObjectiveThe aim of the study is to identify the barriers to gender-affirming health care education for providers from the perspectives of patients and providers. MethodsA qualitative study based on grounded theory was conducted. Participants included transgender and gender diverse (TGD) patients seeking care, as well as resident physicians and attending physicians involved in care of patients seeking gender-affirming care. Semi-structured interviews were conducted over Zoom application and telephone calls. The study was conducted in Boston, Massachusetts, USA from November 2022 until February 2023. ResultsNine attending physicians, eight resident physicians, and fifteen patients were interviewed. Attending physicians noted barriers to include lack of formal training in medical school and residency, lack of adequate opportunities for faculty development to appropriately train resident physicians, lack of opportunities for trainees to provide dedicated clinical care, lack of community engagement initiatives, and need for additional training centered on cultural sensitivity and inclusivity. Resident physicians noted a lack of robust and longitudinal didactic curriculum, deficiency in dedicated clinical time, and inadequacy in interprofessional training as major barriers to their training. They noted that they generally felt unprepared to care for TGD patients. Patients' barriers included difficulty building trust in medical providers’ knowledge and skills, being addressed with incorrect names and pronouns, lacking a sense of belonging as a patient, as well as difficulty in arranging care due to lack of a centralized care system. ConclusionBarriers to gender-affirming education include lack of adequate and formal training, lack of professional development opportunities, inadequacy in a multidisciplinary approach to treatment and education, and inadequacy in cultural and sensitivity training. Findings of this qualitative study based on interviews may help facilitate addressing such barriers through creation of routine lecture-based didactic opportunities for providers, investment in faculty development, creation of gender-affirming clinics, providing opportunities for trainees to provide longitudinal care to TGD patients, creation of interdisciplinary training modules, community engagement, and implementation of a multidisciplinary care model, which may help improve gender-affirming care in the long-run.

Assessment of Nursing Skill and Knowledge of ChatGPT, Gemini, Microsoft Copilot, and Llama: A Comparative Study

Introduction Artificial intelligence (AI) has emerged as a transformative force in healthcare. This study assesses the performance of advanced AI systems—ChatGPT-3.5, Gemini, Microsoft Copilot, and Llama 2—in a comprehensive 100-question nursing competency examination. The objective is to gauge their potential contributions to nursing healthcare education and future potential implications. Methods The study tested four AI systems (ChatGPT 3.5, Gemini, Microsoft Copilot, Llama 2) with a 100-question nursing exam in February of 2024. A standardized protocol was employed to administer the examination, covering diverse nursing competencies. Questions derived from reputable clinical manuals ensured content reliability. The AI systems underwent evaluation based on accuracy rates. Results Microsoft Copilot demonstrated the highest accuracy at 84%, followed by ChatGPT 3.5 (77%), Gemini (75%), and Llama 2 (68%). None achieved complete accuracy on all questions. Each of the AI systems has answered at least one question that only they got correctly. Conclusion The variations in AI answers underscore the significance of selecting appropriate AI systems based on specific application requirements and domains, as no singular AI system consistently surpassed others in every aspect of nursing knowledge.

The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation.

A large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development. This study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings. The search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer. From ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies. This is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user's point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

Adapting to the Digital Age: An Evaluation of Online Learning Strategies in Public Health and Social Care Education

Introduction. The global shift towards digital education in the wake of the COVID-19 pandemic has reshaped the landscape of higher education. Public health and social care programmes have adapted to this transformation, embracing online learning strategies to engage students. This study focuses on the assessment of online learning experiences in public health and social care education, particularly during the post-COVID-19 period. Materials and Methods. This qualitative study employed semi-structured interviews and focus groups with 49 university students in England. These phases, conducted both in person and via video, were audio and video recorded and transcribed verbatim. A purposive sampling technique was employed to gather data, and framework analysis facilitated data management and interpretation. Results. The presentation highlights three main themes and 12 accompanying sub-themes that capture the clear preferences of students for both in-person and online learning methods. The findings elucidate the merits and demerits of online learning strategies in contrast to traditional face-to-face classes. Online classes offer distinct advantages in terms of flexibility, cost-effectiveness, time efficiency, and technological proficiency. However, they also bring to the fore concerns related to social isolation, mental health challenges, technical challenges, and distractions within the home environment. Conclusion. Considering the global transition to digital education, this study contributes to the dynamic discourse surrounding online learning in the post-COVID-19 era. The diverse preferences expressed by students highlight the importance of personalised and flexible teaching methods in online education, underlining the need for adaptability and inclusivity to meet the diverse needs of today’s learners.

Determinants of low birth weight and its effect on childhood health and nutritional outcomes in Bangladesh

BackgroundThe high incidence of low birth weight (LBW) is associated with an increased risk of infant mortality, adverse pregnancy outcomes for mothers, and a decline in overall health and well-being. The current study aimed to identify the various determinants of LBW and its effect on adverse health and nutritional outcomes of children aged 0–23 months in Bangladesh.MethodsBangladesh Demography and Health Survey (BDHS) 2017-18 data was used. A chi-square test and multivariable logistic regression analysis were used to find out the associations between independent variables and outcomes (e.g., LBW, child illness and undernutrition).ResultsThe overall prevalence of LBW among was 16.3%. Mother with no formal education (AOR = 2.64, 95% CI = 0.55–3.30, p = 0.01), female child (AOR = 1.31, 95% CI = 1.04–1.65, p = 0.023); and poorest economic status (AOR = 1.69, 95% CI = 1.13–2.51, p = 0.010), were identified significant determinants of LBW. Of home environment and hygiene factors, unimproved toilet facilities (AOR = 1.38, 95% CI = 1.03–1.84, p = 0.030) had a significant effect on LBW. In addition, children born with LBW were more likely to suffer fever (AOR = 1.26, 95% CI = 1.05–1.60, p = 0.050), stunting (AOR = 2.42, 95% CI = 1.86–3.15, p = < 0.001), wasting (AOR = 1.47, 95% CI = 1.02–2.25 p = 0.049), and underweight (AOR = 3.19, 95% CI = 2.40–4.23, p = < 0.001).ConclusionOne out of five children was LBW in Bangladesh. Maternal education, sex of child, wealth index, and toilet facilities had significant effects on LBW. In addition, LWB contributed to children’s poor health and nutritional outcomes. Enhancing maternal pregnancy, and child health outcomes necessitates policies addressing poverty, gender inequality, and social disparities. Key strategies include promoting regular prenatal care, early medical intervention, reproductive health education, and safe hygiene practices. To combat the negative impacts of LBW, a comprehensive strategy is vital, encompassing exclusive breastfeeding, nutritional support, growth monitoring, accessible healthcare, and caregiver education.

Social and Healthcare Educators’ Competence in Well-Being at Work – A Cross-Sectional Study

Social and Healthcare Educators’ Competence in Well-Being at Work – A Cross-Sectional Study

Competence Development Needs of Health Sciences Teacher Candidates and the Factors Connected to Those Needs

Health science teacher candidates should acquire skills for professional development during their education as teachers, both to ensure their professional competence and improve the quality of learning. This study aimed to identify the competence levels and competence development needs of health science teacher candidates, and the factors associated with those needs. The research applied cross-sectional design and was conducted in winter 2018–2019 in Finland. The Health and Social Care Educator’s Competence instrument and the Educators’ Professional Development instrument were used to collect data. Teacher candidates evaluated their competence as good into all areas of competence. Candidates’ level of need to develop pedagogical competence related to their level of competence in leadership and management, subject and curriculum, and digital collaborative learning. Candidates with a higher level of collaboration and societal competence needed less development to manage challenging situations in teaching. Candidates with more work experience as an educator needed less competence development in managing challenging situations and leadership support. Based on the results, micro level competence should be developed from the start of health science teaching studies, and the latter stages of their studies should develop their macro level competence.

Administrative and legal mechanism of public service provision in Ukraine

The article highlights the administrative and legal mechanism of providing public services in Ukraine. In particular, the study of theoretical and legal approaches to the concept of public services and their differentiation from other types of services provided by state authorities and local governments. It is noted that the definition of "public services” is significantly influenced by the scientific discipline in the context of which this phenomenon is studied. It was established that the concept of "public services” unites the broadest understanding of various services provided by the state or local authorities to citizens and legal entities. These can be health care services, education, transportation and social services, financial services and insurance, law enforcement services and administrative services, as well as other services aimed at meeting the needs of all members of society. At the same time, attention is drawn to the fact that the provision of public services must always be oriented to the category of "public interest”, which is a variable social value that, in certain political and legal conditions, reflects the interest of the entire or dominant part of society, as determined by legislation and implemented through the activities of public administration. A direct relationship has been established with other types of services that can be obtained in the field of public administration, in particular, state, municipal, administrative, and social services. The formation of the institute of public services in Ukraine and the peculiarities of its formation are disclosed. It is noted that today there is a considerable legislative basis for the provision of public services and a whole system of bodies that provide them. At the same time, it is stated that the scientific, legal, organizational and information support of the system of providing public services, the formation of mechanisms for scientific and informational monitoring of their provision, constantly needs improvement.

Evidence-based healthcare competence of social- and healthcare educators: A cross-sectional study.

The purpose of the study was to describe social and healthcare educators' evidence-based healthcare competence and explore the associated factors. A descriptive, cross-sectional study was carried out. The research spanned 5 universities, 19 universities of applied sciences, and 10 vocational colleges in Finland from September to December 2022. Social and healthcare educators (n = 256), of which 21 worked at universities, 176 worked at universities of applied sciences, and 49 worked at vocational colleges. Data collection employed a self-assessed instrument that was designed to measure evidence-based healthcare competence based on the JBI Model of Evidence-based Healthcare. Competence profiles were formed using K-cluster grouping analysis. The educators' self-evaluations of their level of evidence-based healthcare competence were generally at a satisfactory level, with subsequent analyses identifying four distinct profiles of evidence-based healthcare competence. The profiles demonstrated statistically significant differences in terms of evidence synthesis and evidence transfer competencies. The factors associated with evidence-based healthcare competence included level of education, the year in which a professional had obtained their highest degree, current organization of employment, and participation in continuing education. Educators require various types of support for developing high levels of evidence-based healthcare competence. The identification of distinct competence profiles can be pivotal to providing educators with training that is tailored to their exact needs to provide an individualized learning path. WHAT PROBLEM DID THE STUDY ADDRESS?: Educators value the role of evidence in teaching, which reinforces the need to integrate aspects of the JBI Model of evidence-based healthcare into educators' competencies. Aspects of the JBI Model of evidence-based healthcare have not been holistically measured, with only certain components of the model considered separately. Educators need to better understand the global healthcare environment so they can identify research gaps and subsequently develop healthcare systems through their educational role. Higher academic education, work experience, organizational support, and continuous education play essential roles in the development of educators' evidence-based healthcare competence. WHAT WERE THE MAIN FINDINGS?: Educators generally have high levels of competence in evidence-based healthcare. Educators have mastered the different components of the JBI model of evidence-based healthcare but need to improve in areas such as the transfer and implementation of evidence. WHERE AND ON WHOM WILL THE RESEARCH HAVE AN IMPACT?: Determining evidence-based healthcare competence profiles for educators can be used to provide individualized learning paths for the development of evidence-based healthcare competence. Educators need to further develop their competence in evidence-based healthcare to ensure successful implementation and high-quality education in the future. No patient or public contribution.

Partnership to tackle MH crisis for people with disabilities launched

The United Health Foundation, the philanthropic foundation of the UnitedHealth Group has awarded the nonprofit disability rights advocacy organization, The Arc of the United States, a three‐year, $2.5 million grant to improve mental health care for people with intellectual and developmental disabilities (IDD), a news release by The Arc stated. Up to 40% of people with IDD have co‐occurring mental health conditions, and this critical funding will help address their unmet mental health needs. Currently, only 1 in 10 children and adolescents with IDD and mental health disorders receive specialized services. These statistics highlight the need for training and awareness to ensure providers and caregivers are equipped to support the mental health needs of people with disabilities. This includes expanding access to quality mental health care services, improving coordination between disability and health systems, training over 2,000 providers and caregivers to recognize mental health needs in people with IDD, and launching public awareness campaigns to counteract stigma and misconceptions. A key component of the grant is partnering with the National Council for Mental Wellbeing to adapt its evidence‐based Mental Health First Aid program with information on IDD. Over three years, the United Health Foundation grant will help train caregivers, health care professionals, first responders, educators and family members to recognize the mental health needs of people with IDD and decrease the number of mental health crisis incidents experienced by this population.

ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study.

Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

Teaching Quality Analysis of Health Management Professionals in Colleges and Universities Based on Big Data Decision Tree Algorithm

In this study, we investigate whether the big data decision tree approach can be utilised to evaluate the effectiveness of teaching in higher education by health management professors. In light of the expanding need for high-quality healthcare education by the professionals, it is absolutely necessary to evaluate and enhance the teaching efficacy of healthcare management specialists. The application of big data analytics towards the creation of a method for the evaluation of instructors that is both objective and precise is the focus of this study. Every educational system may be broken down into its two primary components: teachers and pupils. The students are not only passive recipients of the instruction that their teachers provide. All of the desired instructional achievements must be driven by the effort of the students in order to fall within the category of "subjective initiative." Therefore, the first step in any educational activity should be encouraging pupils to build their own sense of initiative and intrinsic motivation to learn. This should be the case in any educational endeavour. If teachers have access to and make use of reliable employment data for statistical analysis, they will be able to have a more significant impact on the kids they teach. Based on a preliminary set of actual university employment data from 2012, this study compares the results of employing the C4.5 approach with a decision tree generating technique for the assessment of the teaching quality by the professionals. Both approaches were used to analyse the data. The findings demonstrate that a straightforward decision tree structure may be obtained using the decision tree approach that makes use of the multiscale rough set model. In addition, our approaches don't need to use overlapping data sets, and they're quite efficient from a computing standpoint. The relevance of this study lies in the fact that it sheds light on how the expertise and motives of teachers who are healthcare professionals to be precise, reflect on the learning of their students and their relevant field skills.

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review.

Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases-fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation-the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes- crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs.

Enhancing audiology students’ understanding of person-centered care: insights from an multi-national virtual student conference

Objective This project sought to investigate the impact of a multi-national peer learning initiative in facilitating a student-led conference on person-centred care (PCC). The primary objective was to assess students’ comprehension of PCC elements before and after engaging in the opportunity, with a concurrent evaluation of the efficacy of the opportunity. Design A mixed-methods study protocol was followed. Following the conference, participants completed a four-part survey including (a) demographics, (b) retrospective pre-post Likert scale, (c) Likert rating of conference experience and (d) five open-ended questions. Study sample One hundred and four participants (92.4% female) with a mean age of 21 years (0.07 SD) participated in the study. Results A significant difference in awareness pre-post conference was demonstrated across all topics (WSR, p < 0.001) with participants satisfied with the conference. Qualitative analysis revealed three main themes: (a) application of PCC; (b) perspectives of PCC; and () barriers to PCC; with nine sub-themes. Conclusion The conference was beneficial in enhancing students’ awareness of topics and principles of PCC. Innovative pedagogical approaches should be considered in order to enhance healthcare education allowing future clinicians to better meet the dynamic needs of their clients.

Innovation in Healthcare Education: Using “Shark Tank” Approaches to Educate Teams of Interprofessional Students in Health Equity

AbstractInspired by the television show Shark Tank, we developed a summer research program that brought together students from the Colleges of Medicine, Pharmacy, Business, and Communications, to collaborate, find creative solutions, and spark innovations in population health. Under the guidance of faculty, student teams conducted literature reviews and data-thon exercises to examine trends to identify health equity gaps. Students then worked collaboratively to develop and pitch innovative solutions in front of a panel of expert “sharks” for feedback and to gain financial support to advance their ideas.