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ChatGPT, a large language model, has gained significance in medical writing, particularly in case reports that document the course of an illness. This article explores the integration of ChatGPT and how ChatGPT shapes the process, product, and politics of medical writing in the real world. We conducted a bibliometric analysis on case reports utilizing ChatGPT and indexed in PubMed, encompassing publication information. Furthermore, an in-depth analysis was conducted to categorize the applications and limitations of ChatGPT and the publication trend of application categories. A total of 66 case reports utilizing ChatGPT were identified, with a predominant preference for the online version and English input by the authors. The prevalent application categories were information retrieval and content generation. Notably, this trend remained consistent across different months. Within the subset of 32 articles addressing ChatGPT limitations in case report writing, concerns related to inaccuracies and a lack of clinical context were prominently emphasized. This pointed out the important role of clinical thinking and professional expertise, representing the foundational tenets of medical education, while also accentuating the distinction between physicians and generative artificial intelligence.

Background/Objectives: To examine the relationships between state affect and sleep health at within- and between-person levels among health science students. Methods: A correlational design was used and 54 health science students were included. The participants completed baseline and 7-day ambulatory assessments in a free-living setting. Daily sleep and affect were measured using the Consensus Sleep Diary and Positive and Negative Affect Schedule. Mixed-effect models were used to examine the effects of affect on sleep health. Results: The participants were 19.8 (SD, 0.6) years and 92.6% were females. Approximately 40% had poor sleep quality. Controlling for the potential confounders (e.g., age, sex, and bedtime procrastination), higher within-person negative affect predicted shorter sleep duration, lower sleep efficiency, longer sleep onset latency, and less feeling rested. Higher between-person negative affect predicted shorter sleep duration. Higher within-person positive affect predicted longer sleep onset latency. Higher within- and between-person positive affect predicted more feeling rested. Conclusions: Negative affect was most consistently associated with sleep health at the individual level. Affect regulation should be considered when delivering personalized interventions targeting sleep health among health science students.

In health and social science and other fields where count data analysis is important, zero-inflated models have been employed when the frequency of zero count is high (inflated). Due to multiple reasons, there are scenarios in which an additional count value of k > 0 occurs with high frequency. The zero- and k-inflated Poisson distribution model (ZkIP) is more appropriate for such situations. The ZkIP model is a mixture distribution with three components: degenerate distributions at 0 and k count and a Poisson distribution. In this article, we propose an alternative and computationally fast expectation–maximization (EM) algorithm to obtain the parameter estimates for grouped zero and k-inflated count data. The asymptotic standard errors are derived using the complete data approach. We compare the zero- and k-inflated Poisson model with its zero-inflated and non-inflated counterparts. The best model is selected based on commonly used criteria. The theoretical results are supplemented with the analysis of two real-life datasets from health sciences.

A growing amount of research is being conducted on cultural intelligence (CQ), which is the ability to adjust and adapt successfully to a variety of surroundings. CQ is a vital quality for people in diversified societies, as are seen today. However, it is still unclear how an individual can develop or strengthen CQ because previous studies have shown that variables such as foreign experience and personality are not exclusively sufficient as CQ antecedents. If CQ can be treated health-scientifically, as the CQ developers argue, diet and exercise that are effective in keeping the body and brain healthy may also correlate with CQ. It is of great significance to clarify the relationship between lifestyle and CQ by applying recent previous research showing the relationship between CQ and both the brain and intelligence, as well as between lifestyle and both the brain and intelligence. Using data derived from 142 Japanese businesspersons, the results of structural equation modeling indicate that lifestyles (dietary balance and walking frequency) are significantly associated with CQ after controlling for personalities (curiosity and grit) and international experiences (length of studying and working abroad), which have been used as predictors of CQ in previous studies. Furthermore, the moderation test showed that the effect of dietary balance on increasing CQ is greater for people with less overseas experience, indicating that dietary balance compensates for lack of overseas experience in the effect of maintaining the level of CQ. These suggest the effectiveness of a health-scientific approach to the influencing factors of CQ. This study is the first to show that CQ is influenced by lifestyle such as walking and dieting, in addition to personality and overseas experience, and will contribute to the future development of health science and cross-cultural research.

We study a Gauss model (GM), a map from time to the bell-shaped Gaussian function to model the deaths per day and country, as a simple, analytically tractable model to make predictions on the coronavirus epidemic. Justified by the sigmoidal nature of a pandemic, i.e., initial exponential spread to eventual saturation, and an agent-based model, we apply the GM to existing data, as of 2 April 2020, from 25 countries during first corona pandemic wave and study the model’s predictions. We find that logarithmic daily fatalities caused by the coronavirus disease 2019 (Covid-19) are well described by a quadratic function in time. By fitting the data to second order polynomials from a statistical χ 2 -fit with 95% confidence, we are able to obtain the characteristic parameters of the GM, i.e., a width, peak height, and time of peak, for each country separately, with which we extrapolate to future times to make predictions. We provide evidence that this supposedly oversimplifying model might still have predictive power and use it to forecast the further course of the fatalities caused by Covid-19 per country, including peak number of deaths per day, date of peak, and duration within most deaths occur. While our main goal is to present the general idea of the simple modeling process using GMs, we also describe possible estimates for the number of required respiratory machines and the duration left until the number of infected will be significantly reduced.

For Germany, it is predicted that the first wave of the corona pandemic disease reaches its maximum of new infections on 11 April 2020 − 3.4 + 5.4 days with 90% confidence. With a delay of about 7 days the maximum demand on breathing machines in hospitals occurs on 18 April 2020 − 3.4 + 5.4 days. The first pandemic wave ends in Germany end of May 2020. The predictions are based on the assumption of a Gaussian time evolution well justified by the central limit theorem of statistics. The width and the maximum time and thus the duration of this Gaussian distribution are determined from a statistical χ 2 -fit to the observed doubling times before 28 March 2020.

The COVID-19 pandemic profoundly changed everyday life of social actors, which inferred mental health and well-being concerns. As students of health-related studies tend to adapt better to difficult circumstances, in this study, we explored the effect of the pandemic on sports science students’ well-being during the summer of 2022. The research was conducted in Slovenia and Serbia. The sample comprised n = 350 students. The PERMA-Profiler, a 15-item self-reported questionnaire, was adapted to assess well-being across five elements: positive emotions, engagement, relationships, meaning, and accomplishment. Data were collected with a questionnaire through the summer of 2022 (from May to July) and analyzed in SPSS, AMOS 26.0. The results revealed normal functioning (M = 7.72, SD ± 1.38) for the overall well-being of the students. Although all dimensions indicated high scores, relationships (M = 7.95, SD ± 1.63), meaning (M = 7.76, SD ± 1.69), and engagement (M = 7.73, SD ± 1.36) rated the highest. Furthermore, the instrument was acceptable, as the confirmatory factor analysis showed adequate reliability based on Cronbach’s alpha (15 items, α = 0.94) and strong internal correlations between the PERMA dimensions. This study contributes to the previously published research, emphasizing the positive responses and successful coping of sports science students in times of complex situations, such as the COVID-19 pandemic.

This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.

Graduate-entry, following a science degree, is the preferred pathway into many medical schools, however little is known about how the learning approaches of medical students compare to those of science students. This study compared the learning approaches and achievement orientations of science students with those aiming to enter graduate-entry medicine programs. The two factor study process questionnaire and the achievement goal orientation survey were used to compare students in; stage one: third year science students (n = 86) to graduate-entry medicine students (n = 158); stage two: applicants to graduate medicine (n = 84); stage three: first year science students (n = 363) to first year pre-medicine students (n = 68). Medical students and applicants to medicine demonstrated a greater preference for deep learning than third year science students (p < 0.0001). Pre-medicine students were similar to medical students. Medical students, applicants to medicine and pre-medicine students also all had a greater preference for a learning goal orientation. The preference for a deeper approach to learning and stronger learning goal orientation in students enrolled in medicine or aiming to gain entry to graduate medicine indicates a motivation towards the acquisition of knowledge. Medical educators need to ensure that students continue to develop positive and beneficial styles of learning to assist them to develop into life-long learners.

The growing use of information and communication technology has now expanded to health professionals in practice. This study aimed to highlight the current status of Information and Communication Technology (ICT) use in health sciences as reported in journal papers between 2002 and 2021. This paper presents the annual trends, top institutes and countries, citations, h-index, keywords distribution, and top authors in this research domain. The data were extracted from the Web of Science database, and R studio and Bibexcel tools were used for analysis. The study analyzed a total of 140 documents published over a span of two decades. Health Care Sciences Services (34) and Computer Science published the most health science articles (29). The USA (19) was the most productive country, followed by England (16) and the Netherlands (15). Berg M was the most productive author, with 36 articles. The results show that institutions such as Erasmus University and Duke University have published numerous articles on the topic, encouraged by specific R&D funding schemes, and made a significant contribution to the development of health sciences research. The findings of this study offer valuable information about international initiatives and projects relevant to the advancement of ICT in health science research, which may be utilized to pinpoint potential future study topics such as artificial intelligence development.

Due to the increased prevalence of chronic diseases, behavior changes are integral to self-management. Healthcare and other professionals are expected to support these behavior changes, and therefore, undergraduate students should receive up-to-date and evidence-based training in this respect. Our work aims to review the outcomes of digital tools in behavior change support education. A secondary aim was to examine existing instruments to assess the effectiveness of these tools. A PIO (population/problem, intervention, outcome) research question led our literature search. The population was limited to students in nursing, sports sciences, and pharmacy; the interventions were limited to digital teaching tools; and the outcomes consisted of knowledge, motivation, and competencies. A systematic literature review was performed in the PubMed, CINAHL, MEDLINE, Web of Science, SAGE, Scopus, and Cochrane Library databases and by backward citation searching. We used PRISMA guidelines 2020 to depict the search process for relevant literature. Two authors evaluated included studies using the Mixed Methods Appraisal Tool (MMAT) independently. Using inclusion and exclusion criteria, we included 15 studies in the final analysis: six quantitative descriptive studies, two randomized studies, six mixed methods studies, and one qualitative study. According to the MMAT, all studies were suitable for further analysis in terms of quality. The studies resorted to various digital tools to improve students' knowledge of behavior change techniques in individuals with chronic disease, leading to greater self-confidence, better cooperation, and practical experience and skills. The most common limitations that have been perceived for using these tools are time and space constraints.