Seminars in Medical Writing and Education
Not a member yet
520 research outputs found
Sort by
A Multidimensional Assessment of Generative AI Models in Theoretical and Clinical Nursing Education
Get PDFGenerative Artificial Intelligence (GAI) has rapidly emerged as an advanced technology with the potential to revolutionize various sectors, including education and healthcare. Despite the growing interest in applying GAI models to nursing education, there remains a lack of comprehensive research assessing their multidimensional impact. Research aims to determine the multifaceted impact of GAI on nursing education, focusing on its effectiveness and challenges. A mixed-methods approach was employed, combining qualitative interviews and focus groups with quantitative surveys and pre-and post-test assessments. Research involved nursing students, educators, and clinical practitioners from various institutions and healthcare settings, with a total of 820 participants gathered. Thematic analysis was employed to examine views, experiences, and issues pertaining to GAI in qualitative data. IBM SPSS software version 28 is used for the statistical analysis. Descriptive statistics were used to examine quantitative data to compile survey results and measures of academic achievement, while the findings of the pre- and post-tests were compared using paired t-tests to measure changes in knowledge and clinical skills. The GAI model is to increase student engagement (from 60% to 80%), improvement in personalized learning (from 65% to 87%), and an increase in theoretical knowledge retention (from 70% to 95%). The findings suggest that GAI enhances student engagement, offers personalized learning experiences, and improves theoretical knowledge retention. The research highlights the need for careful integration of GAI into nursing curricula and offers recommendations for addressing the ethical considerations and ensuring effective use in clinical training.
Comparative Analysis of Virtual Reality Simulation and Conventional Training in Temporal Bone Dissection
Get PDFA novel method for teaching a variety of medical operations is virtual reality (VR) simulation, which can potentially improve learning without the hazards of conventional hands-on training. A crucial ability in otolaryngology that calls for accuracy and skill is the dissection of cadaveric temporal bones. A total of 155 individuals with little to no prior knowledge of temporal bone dissection were randomized to either the VR group (85 participants), which trained under supervision using a VR simulator, or the traditional group (70 participants), which used models, videos, and small group instructions. Participants dissected a cadaveric temporal bone after training, and blinded assessors evaluated the results using six criteria: anatomical accuracy, technique, efficiency, overall performance, injury size, and end product. The results revealed that the VR group outperformed the traditional group, achieving significantly higher scores in the end product, causing fewer injuries to anatomical structures, and demonstrating better overall performance, with all differences being statistically significant. The research employed IBM SPSS statistics (version 26) for statistical analysis, and an independent t-test was used to compare the groups\u27 mean scores. The results indicated fair to moderate reliability when inter-rater reliability was evaluated using the Intra-class Correlation Coefficient (ICC) and the kappa statistic. These findings suggest that VR simulation is a more effective means of honing cadaveric temporal bone dissection abilities than traditional training techniques
Evaluating Physician-Patient Communication: Comparing Observer Ratings and Frequency-Based Measures
Get PDFEffective physician-patient communication is crucial for improving patient outcomes and satisfaction. However, assessing communication quality remains challenging due to the variety of available evaluation techniques. The research sought to contrast various methods to the measurement of physician-patient communication, including salient elements like patient participation, physician information transmission, emotional content, non-verbal communication, and rapport. 110 physician-patient consultations were analyzed utilizing two contrasting methods, such as frequency measures, to capture verbal and non-verbal action and professional observer standardized rater ratings. To compare these, high-level statistical processes, e.g., regression analysis and Spearman\u27s rank correlation, were used to cross-match the data obtained. The results revealed low to moderate correlations between self-reports of communication by patients and physicians, which were not statistically significant. However, stronger and statistically significant correlations were observed between observer ratings and frequency-based measures. These results indicate that although each measurement method measures different dimensions of communication, the methods are complementary, and are informative about the dynamics of communication between physicians and patients. The research also indicates the significance of rapport-building and non-verbal communication in effective communication. Frequency-based metrics were also identified as effective in assessing real-time communication dynamics, highlighting the benefits of employing various measurement methods for a more thorough evaluation of communication quality
Developing Patient-Centered Communication Models to Improve Patient Engagement and Enhance Clinical Efficiency
Get PDFThe importance of communication, centered around the patients, has proven effective in enhancing patient engagement and improving clinical efficiency despite restrictions of time and ability often preventing effective communication in circumstances surrounding trauma care. Breakdown of communication in such an environment shall bid a huge impact on the outcome of a patient; hence, that sets the background of developing a context within which communication strategies can happen to be responsive to the dynamics of the trauma team. Research recruited 126 medical hospital staff within trauma teams who had undergone communication training completely focused on patient-centered approaches. The training was to improve patient engagement and team communication during trauma encounters. Participants engaged in simulated trauma scenarios both before and after the training, which was followed by a 6-week follow-up. Data was analyzed with one way ANOVA with post-hoc Tukey’s test and regression analysis aimed at investigating the effect and sustainability of communication improvement. There was a significant improvement in communication and patient engagement behaviors after the training (p<0.05), and, although quite a renaissance regarding some loss of retention (p>0.05), those effects were maintained at the 6-week follow-up. Regression analysis showed that pre-training knowledge about communication and intensity of training for the retention of improved communication performance in the longer run were the two major factors. Research shows that brief training interventions can greatly improve trauma team communications for a long period of time, consequently leading to enhanced patient engagement as well as improved clinical efficiency.
Investigating the Role of Medical Informatics in Enhancing Healthcare Accessibility and Clinical Decision-Making
Get PDFMedical informatics plays a crucial role in transforming healthcare accessibility by integrating advanced technologies to improve patient care, streamline medical processes, and enhance decision-making. Medical informatics uses telemedicine and electronic health records (EHRs) to connect patients and healthcare professionals in response to the increasing need for effective healthcare services. The aim of this research is to investigate the role of medical informatics in enhancing healthcare accessibility and clinical decision-making through the use of digitized medical records. A total of 347 consultations were observed, with data collected using a work sampling technique during follow-up outpatient sessions at various hospitals. The data was analyzed using statistical methods, which include descriptive statistics, one-way ANOVA, Mann-Whitney U test and regression analysis. Findings reveal that the paper records revealed considerable disparities in consultation times across hospitals and specializations. Digital records showed notable disparities only between specializations. Consultation times were not significantly different between paper and digital records (p≥0.278), while time spent seeking computer records increased in several specialties. Digitized data, including remote access, quicker inpatient handovers, and improved record timelines, all contribute to better decision-making. This research highlights the digitized medical records, as a component of medical informatics, contribute to improved healthcare accessibility and clinical decision-making when combined with standardized operational procedures
Evaluating the Role of Informatics Systems in Early Detection and Monitoring of Dementia Progression
Get PDFDementia is a progressive neurodegenerative disorder that impairs cognitive function, memory, and daily activities, posing significant challenges for patients, caregivers, and healthcare systems. Early detection and continuous monitoring of dementia progression are essential for timely intervention, improved quality of life, and effective disease management. The objective of the research is to evaluate the role of informatics systems in the early detection and monitoring of dementia progression, particularly in rural populations. Clinical, behavioral, and lifestyle data from 486 dementia patients were efficiently collected and analyzed using SPSS software. The statistical methods applied included descriptive statistics, t-tests, chi-square tests, correlation, and regression analysis. The findings identified education level, sleep quality, psychological factors, behavioral patterns, and caregiving practices as significant influences on dementia progression. Patients with no formal educational attainment experienced a 10.3% faster cognitive decline than those with higher education. Structured caregiving while poor sleep increased cognitive decline by 32.9%. Additionally, depression accelerated deterioration by 35%, whereas low activity and moderate engagement slowed by 37.0% respectively. The statistical tests reveal relationships between key analysis variables and the progression of dementia. Decreased education and poor sleep quality hastened cognitive decline in cases of degenerative and vascular dementias. This research highlights the critical role of informatics systems in enhancing dementia diagnosis, facilitating personalized treatment, and improving long-term disease management through advanced data analysis and monitoring technologies
Advanced Predictive Modeling for Hypertension Risk Based on Health Indicators and Machine Learning Technique
Get PDFIntroduction: The condition of hypertension significantly accelerates the incidence of cardiovascular diseases and demands timely and proper measurement of the avoidable risk. Traditional techniques in the measurement of the pressure in the arteries provide accurate figures but are incapable of forecasting the risk of the development of hypertension.Aim: The goal was to establish an Efficient Pelican Optimized Dynamic Random Forest (EPO-DRF) model from health markers to forecast the hypertension probability.Methods: Patient information was extracted from clinical history, such as clinical predictors and lifestyle predictors of hypertension. Preprocessing, such as normalization and cleaning, was carried out to ensure precision and consistency. The significant predictors, such as age, cholesterol, blood sugar, and BMI, were determined. Optimum pelican optimization was used to increase the predictive efficiency by identifying the most significant predictors and removing redundant predictors.Result: To forecast the hypertension probability, the EPO-DRF model also displayed excellent outcomes, such as the F1-score (86.2%), the accuracy (90.4%), the sensitivity (87.5%), and the precision (85.7%). Classification performance and the most significant feature selection also underwent optimization in the course of the optimization to increase the efficacy of the model.Conclusion: The novel methodology arrived at an effective and efficient way to attain hypertension screening at an early stage, in alignment with preventive care practices and minimizing hypertension complications. It also helped healthcare analytics by having a precise predictive model to project future hypertension detection, making timely intervention and enhancing outcomes among the patients
Advanced Predictive Framework for Early Detection and Classification of Psychiatric Conditions Using EEG Data
Get PDFPsychiatric illnesses, such as depression, generalized anxiety disorder, and schizophrenia, tend to be characterized by mild neurophysiological markers that make early diagnosis difficult. The greatest limitation of present diagnostic approaches is the failure to detect such mild brainwave anomalies with good accuracy, especially during the early stages of the disorders. This research presents a new predictive model for the early classification and diagnosis of psychiatric diseases from Electroencephalogram (EEG) signals. The framework employs the use of the Archerfish Hunting Optimizer Tuned Spiking Neural Network (AHO-SNN). This hybrid approach combines the computational effectiveness of an evolution-inspired optimizer with spiking neural networks\u27 (SNNs) temporal processing ability. The AHO algorithm is used to fine-tune the SNN\u27s synaptic weights in order to make the SNN more sensitive to neural oscillations and cortical pathologies related to psychiatric disorders. The projected AHO-SNN results are precision 94%, f1-score 94%, accuracy 96%, and recall 92%. The outcomes reveal that the AHO-SNN approach obtains high diagnostic precision, separating psychiatric patients from healthy controls based on the patterns of neural activity, for instance, theta and alpha band anomalies. The technique has enormous potential to support improved early psychiatric diagnosis, facilitating timely interventions and customized treatment strategies. Future research will center on integrating multimodal biomarkers and real-time monitoring to further enhance diagnostic accuracy and increase clinical utility
The professional training of the psychopedagogue for socio-community psychopedagogical intervention. Epistemological foundations and their particularities
Get PDFThe training of professionals is a topic of growing interest and complexity. It refers to its impact on the achievement of objectives and goals in terms of sociocultural and educational policies, which contribute to sustainable human development and territorial social transformation. In Cuba, professional training is assumed to have a complex, systemic, dialectical and social nature, aimed at preparing the subject for self-transformation and through it, the active and creative transformation of its context. In the case of psychopedagogues, they are committed to their profession and to the transformation of the socio-community context, from which inter- and transdisciplinary views are required that allow the integration of all socio-community networks. This research is carried out with the objective of the foundations and particularities of what the socio-community psychopedagogical intervention has gone through in the training of psychopedagogues in Cuba. Theoretical and empirical methods were used that allowed the historical trend study. It is concluded that the training of the Pedagogy-Psychology professional is comprehensive and contextualized for socio-community psycho-pedagogical intervention, where its impact on preparation as a socio-community counselor is recognized
Teaching models in digital environments: analysis of the PLAGCIS case
Get PDFIntroduction:The teaching model mediated by virtuality in the Platform for Social Action, Management and Research (PLAGCIS) was analyzed from the perspective of teachers. The characteristics of the digital environment and its impact on educational practice were identified.Methods:A qualitative approach with an interpretive phenomenological design was adopted. In-depth interviews were conducted with teachers with experience in PLAGCIS and non-participant observation. The data were analyzed through categorization and theoretical triangulation.Results:Three key dimensions were identified in the teaching experience: autonomy and pedagogical flexibility, construction of an academic community, and challenges in virtual teaching. It was found that the platform allows the adaptation of pedagogical strategies and encourages collaboration between teachers, although it presents challenges in interaction with students and in technological training.Conclusions:PLAGCIS represents an innovative model of virtual teaching, with the potential to strengthen teaching practice in digital environments. However, it is necessary to improve training in technological tools and strengthen pedagogical interaction