Yoxas/statistical_literacy huggingface dataset:
In the digital era, digital literacy is a fundamental indicator of a nation's quality and plays a crucial role in public health. Exploring the theoretical mechanisms and effects of digital literacy on individuals' health is of great practical importance, advancing the initiatives of 'Digital China' and 'Healthy China'. The study utilised three-period survey panel data from the China Family Panel Study spanning 2016, 2018, and 2020 to measure and evaluate levels of digital literacy, physical health, mental health, healthy lifestyle, and integrated health among the participants. Subsequently, a series of empirical analyses were conducted to examine the general impact, heterogeneous effects and transmission pathways of digital literacy on various types of health levels. Digital literacy significantly enhances all aspects of respondents' health, and this conclusion remains valid even after conducting robustness tests and addressing endogeneity through variable substitution and selecting instrumental variables using the 2SLS method. Furthermore, examining heterogeneity by considering individual traits and the makeup of digital literacy reveals that the impact of digital literacy on individuals' health varies according to age, cultural background, personal income, and the components of digital literacy. Pathway analyses also demonstrate that medical accessibility, information access, social network, and planned behaviour are key routes through which digital literacy enhances the health of the population. It is imperative for the government to actively promote the advancement of the digital healthcare industry, while individuals should strive to enhance their digital literacy. By collectively focussing on these efforts, national health can be significantly improved.
In the dynamic landscape of medical education, recognizing and catering to the diverse learning styles of students are pivotal for fostering academic success. This study investigates the intricate relationship between learning styles and academic performance among medical students. A sample comprising 201 second-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students from two batches participated in this cross-sectional study. Utilizing the Grasha-Riechmann Student Learning Style Scales, students were categorized into six distinct learning styles: independent, avoidant, collaborative, dependent, competitive, and participatory. Academic performance was assessed through cumulative scores at the end of the academic year. Statistical analyses, including descriptive statistics, Spearman's correlation analysis, and the Kruskal-Wallis H test, were conducted using IBM SPSS Statistics for Windows, Version 25, (Released 2017; IBM Corp., Armonk, New York, United States). The findings revealed a rich diversity of learning styles among medical students, with independent learning emerging as the most prevalent style. However, intriguingly, no statistically significant difference in academic performance was discerned across the various learning styles. Nonetheless, correlation analysis uncovered weak positive correlations between independent, dependent, and participatory learning styles with academic performance, while an equally weak negative correlation was observed for the avoidant style. These results underscore the necessity for tailored educational strategies that can accommodate the heterogeneous learning preferences exhibited by medical students. While certain learning styles may be favoured by students, their adoption does not guarantee academic success. Thus, educators are urged to embrace flexible teaching methodologies to accommodate the diverse learning styles present within medical education, ultimately fostering student engagement and achievement. This study illuminates the imperative of understanding and addressing diverse learning styles among medical students, laying the foundation for further research into optimizing teaching methodologies in medical education.
In the era of extensive data collection, there is a growing need for a large scale data analysis with tools that can handle many variables in one modeling framework. In this article, we present our recent applications of Bayesian network modeling to pathology informatics. Bayesian networks (BNs) are probabilistic graphical models that represent domain knowledge and allow investigators to process this knowledge following sound rules of probability theory. BNs can be built based on expert opinion as well as learned from accumulating data sets. BN modeling is now recognized as a suitable approach for knowledge representation and reasoning under uncertainty. Over the last two decades BN have been successfully applied to many studies on medical prognosis and diagnosis. Based on data and expert knowledge, we have constructed several BN models to assess patient risk for subsequent specific histopathologic diagnoses and their related prognosis in gynecological cytopathology and breast pathology. These models include the Pittsburgh Cervical Cancer Screening Model assessing risk for histopathologic diagnoses of cervical precancer and cervical cancer, modeling of the significance of benign-appearing endometrial cells in Pap tests, diagnostic modeling to determine whether adenocarcinoma in tissue specimens is of endometrial or endocervical origin, and models to assess risk for recurrence of invasive breast carcinoma and ductal carcinoma in situ. Bayesian network models can be used as powerful and flexible risk assessment tools on large clinical datasets and can quantitatively identify variables that are of greatest significance in predicting specific histopathologic diagnoses and their related prognosis. Resulting BN models are able to provide individualized quantitative risk assessments and prognostication for specific abnormal findings commonly reported in gynecological cytopathology and breast pathology.
In the era of Gavi's 5.0 vision of "leaving no one behind with immunization", childhood routine vaccination in missed communities is considered as a priority concern. Despite having a success story at the national level, low uptake of immunization is still persistent in selected pocket areas of Bangladesh. However, prevalence and the associated factors of zero-dose (ZD) and under-immunization (UI) are still unknown at those geo-pockets of Bangladesh. Thus, the study aims to report and identify the factors associated with ZD and UI in selected geographical locations. This study used data from a Lot Quality Assurance Sampling (LQAS) survey where 504 households from 18 clusters of four hard to reach (HTR) and one urban slum were included. Caregivers of children aged 4.5 to 23 months were interviewed. Three outcome variables- ZD, UI and ZD/UI were considered and several related attributes were considered as independent variables. Data were analyzed through bivariate analysis, binary logistic regression and dominance analysis. Overall, 32% of the children were either ZD (8%) or UI (26%) in the selected areas. The adjusted odds of ZD/UI for urban slum and haor (wetlands) areas were 5.62 and 3.61 respectively considering coastal areas as reference. However, distance of nearest EPI center, availability of EPI card, age of caregivers, education and occupation of mother and number of earning members in household were influential factors for ZD/UI. According to dominance analysis, availability of EPI card can explain the most of the variation of ZD/UI in this study. The study findings highlight the high prevalence ZD/UI in certain geo-pockets of the country. It provided a powerful insight of current situation and associated factors in regards to ZD/UI in the country which will help policy-makers and programme managers in designing programmes to reduce missed communities in Bangladesh.
In the evolving HIV landscape, the Middle East and Northern Africa (MENA) grapples with data gaps, hindering the 95-95-95 targets. Lebanon, despite progress, falls short. Our study addresses Pre-exposure Prophylaxis (PrEP) gaps for effective HIV prevention. Surveying 410 participants via snowball sampling and an online questionnaire, we analyzed data with chi-square and regression over 5 months for insights into PrEP awareness and readiness. Summarizing the pivotal statistical outcomes of the study, 22.2% reported awareness of PrEP, showcasing diverse knowledge levels about its purpose and usage. Out of them, 57.1% expressed a willingness to use it; while a mere 5.5% have utilized it. The study reveals diverse demographic profiles, with age influencing PrEP awareness and willingness to use. Positive correlations exist between PrEP discussions within social circles, increased information availability, and elevated willingness. Financial considerations and accessibility at pharmacies emerge as critical determinants, guiding the development of targeted interventions. This analysis emphasizes tailored education, community initiatives, and policy enhancements to address PrEP challenges in Lebanon. Guiding public health initiatives, our study considers age, social dynamics, education, and accessibility in HIV prevention.
In the evolving landscape of cardiac rehabilitation (CR), adopting digital technologies, including synchronous/real-time digital interventions and smart applications, has emerged as a transformative approach. These technologies offer real-time health data access, continuous vital sign monitoring, and personalized educational enhanced patient self-management and engagement. Despite their potential benefits, challenges and limitations exist, necessitating careful consideration. Synchronous/real-time digital CR involves remote, two-way audiovisual communication, addressing issues of accessibility and promoting home-based interventions. Smart applications extend beyond traditional healthcare, providing real-time health data and fostering patient empowerment. Wearable devices and mobile apps enable continuous monitoring, tracking of rehabilitation outcomes, and facilitate lifestyle modifications crucial for cardiac health maintenance. As digital CR progresses, ensuring patient access, equitable implementation, and addressing the digital divide becomes paramount. Artificial intelligence holds promise in the early detection of cardiac events and tailoring patient-specific CR programs. However, challenges such as digital literacy, data privacy, and security must be addressed to ensure inclusive implementation. Moreover, the shift toward digital CR raises concerns about cost, safety, and potential depersonalization of therapeutic relationships. A transformative shift towards technologically enabled CR necessitates further research, focusing not only on technological advancements but also on customization to meet diverse patient needs. Overcoming challenges related to cost, safety, data security, and potential depersonalization is crucial for the widespread adoption of digital CR. Future studies should explore integrating moral values into digital therapeutic relationships and ensure that digital CR is accessible, equitable, and seamlessly integrated into routine cardiac care. Theoretical frameworks that accommodate the dynamic quality of real-time monitoring and feedback feature of digital CR interventions should be considered to guide intervention development.
In the few-shot common-localization task, given few support images without bounding box annotations at each episode, the goal is to localize the common object in the query image of unseen categories. The few-shot common-localization task involves common object reasoning from the given images, predicting the spatial locations of the object with different shapes, sizes, and orientations. In this work, we propose a common-centric localization (CCL) network for few-shot common-localization. The motivation of our common-centric localization network is to learn the common object features by dynamic feature relation reasoning via a graph convolutional network with conditional feature aggregation. First, we propose a local common object region generation pipeline to reduce background noises due to feature misalignment. Each support image predicts more accurate object spatial locations by replacing the query with the images in the support set. Second, we introduce a graph convolutional network with dynamic feature transformation to enforce the common object reasoning. To enhance the discriminability during feature matching and enable a better generalization in unseen scenarios, we leverage a conditional feature encoding function to alter visual features according to the input query adaptively. Third, we introduce a common-centric relation structure to model the correlation between the common features and the query image feature. The generated common features guide the query image feature towards a more common object-related representation. We evaluate our common-centric localization network on four datasets, i.e., CL-VOC-07, CL-VOC-12, CL-COCO, CL-VID. We obtain significant improvements compared to state-of-the-art. Our quantitative results confirm the effectiveness of our network.
In the field of alopecia areata research, various focuses including risk factors, epidemiology, molecular pathways, and treatment were constantly improving. However, to date, a bibliometric analysis summarizing the research trend is not available to date. The main objective of this study was to provide researchers with an overview of the research trend on alopecia areata in the past two decades. In Web of Science database, screening and extraction of studies related to alopecia areata has been performed. Within studies related to alopecia areata, the most cited 100 studies were appraised and the information of articles, including the citation amounts, keywords and publication types, was extracted for analyses. On average, each study in the top 100 list was cited 104.72 times. Within the top 100 list, the most focused fields were on the management of alopecia areata (34%), molecular mechanisms (28%) and epidemiological issues (23%). Approximately one third of the management-associated studies focused on Janus kinase (JAK) inhibitors (10 studies) and 5 studies focused on the efficacy of corticosteroids for alopecia areata. According to the results of the keyword analysis, JAK inhibitors had become the most mentioned keywords in the field of alopecia areata research since 2016. The top 100 most referenced papers in the field of alopecia areata mostly focused on essential aspects such as treatment options, pathogenesis, risk factors, and comorbidities. The results of the current study could be considered a potential resource for future research and patient care information.
In the field of diagnostic reasoning, it has been argued that memory activation can provide the reasoner with a subset of possible explanations from memory that are highly adaptive for the task at hand. However, few studies have experimentally tested this assumption. Even less empirical and theoretical work has investigated how newly incoming observations affect the availability of explanations in memory over time. In this article we present the results of 2 experiments in which we address these questions. While participants diagnosed sequentially presented medical symptoms, the availability of potential explanations in memory was measured with an implicit probe reaction time task. The results of the experiments were used to test 4 quantitative cognitive models. The models share the general assumption that observations can activate and inhibit explanations in memory. They vary with respect to how newly incoming observations affect the availability of explanations over time. The data of both experiments were predicted best by a model in which all observations in working memory have the same potential to activate explanations from long-term memory and in which these observations do not decay. The results illustrate the power of memory activation processes and show where additional deliberate reasoning strategies might come into play.
In the field of hospital medicine, there is both a limited pool of senior faculty to mentor the rapidly growing number of junior faculty and a lack of career development curricula focused on scholarly activities specific to the needs of the hospitalist. These deficits have resulted in a disproportionately low number of academic hospitalists being promoted to associate and full professor. We implemented a facilitated peer mentoring program with a dedicated curriculum to foster career advancement of academic hospitalists. We recruited 29 academic hospitalists and divided them into five small groups, each guided by one senior faculty. Peer members participated in a 9-month curriculum consisting of alternating large- and small-group sessions that reviewed topics important for academic advancement. Quantitative analysis assessed feasibility of the program, as measured by participation and knowledge improvement on curriculum topics, with pre- and postprogram surveys. Results demonstrated feasibility of the large-group sessions as measured through participation. Small-group participation was more variable. Pre- and postsurvey results showed significant knowledge improvement (p < .05) in nearly all of the curriculum topics. Currently, there is a gap in both mentorship and scholarly skills of academic hospitalists. Our facilitated peer mentoring program with a dedicated curriculum can be used as a framework for other hospitalist programs to support career development.
In the field of medical education, students faced significant challenges during the COVID-19 pandemic. Abrupt alterations were made in the form of preventative precautions. Virtual classes replaced onsite classes, clinical placements were canceled, and social distance interventions prevented face-to-face practical sessions. The present study aimed to assess students' performance and satisfaction before and after the transition of a psychiatry course from onsite to entirely online during the COVID-19 pandemic. A retrospective, non-clinical, and non-interventional comparative educational research study included all students registered in the psychiatric course for the academic year 2020 (pre-pandemic/onsite) and 2021 (during the pandemic/online).To assess students' satisfaction, we utilized the National Commission for Academic Accreditation and Assessment (NCAAA) students' satisfaction survey used by the academic quality unit at KSU, College of Medicine.Satisfaction was assessed in six domains: course organization, learning resources, faculty experiences, clinical teaching, practical sessions, and overall satisfaction. The reliability of the questionnaire was measured using Cronbach's alpha test.To assess their performance, students' grades from both periods were obtained from the exam center. A total of 193 medical students enrolled in the study; 80 received onsite learning and assessment, while another 113 received full online learning and assessment. The students' mean indicators of course satisfaction for the online courses exceeded their corresponding indicators significantly compared to the onsite courses. These indicators included students' satisfaction in terms of course organization, p < 0.001; medical learning resources, p < 0.050; faculty experience, p < 0.050; and overall course, p < 0.050. There were no significant differences regarding satisfaction in both practical sessions, p > 0.050, and clinical teaching, p > 0.050. The students' performance mean was significantly higher in the online (M = 91.76) compared to the onsite courses (M = 88.58) (p < 0.001), and the Cohen's D statistic showed there was a medium level of enhancement in students' overall grades (Cohen's d = 0.41). Students perceived the switch to online delivery methods very favorably. Students' satisfaction significantly improved regarding the themes of course organization, faculty experience, learning resources, and overall course satisfaction, while a similar level of adequate student satisfaction was maintained in terms of clinical teaching and practical sessions during the transition of the course to e-learning. In addition, the online course was associated with a trend toward higher students' grades. However, the assessment of the achievement of course learning outcomes and the maintenance of this positive impact warrants further investigation.
In the future, new variants of the SARS-CoV-2 virus might emerge and cause outbreaks. If this occurs, the implementation of non-pharmaceutical interventions (NPIs) can be reconsidered. Consideration of the potential benefits and harms of implementing NPIs, and ultimately deciding about implementing NPIs, is currently mainly executed by experts and governments. However, general literature on public engagement suggests that integrating public perspectives into decision-making can enhance the quality of decisions and foster greater public understanding of them. In this study, a deliberative mini-public was conducted to integrate this public perspective. The aim was to elicit public considerations regarding non-pharmaceutical interventions by asking a diverse group of citizens to participate as decision-makers and convene, learn and deliberate about implementing non-pharmaceutical interventions during a hypothetical outbreak of a new SARS-CoV-2 variant. Participants emphasized the importance of early implementation during the outbreak, to prevent exceeding healthcare capacity, long-term mental health issues, educational deficits, and bankruptcies. Additionally, participants stressed taking public support into account, and shared ideas on maintaining support. Furthermore, participants wanted to give citizens personal responsibility and freedom in making their own assessment regarding adherence to interventions and how much risk of infection they would be willing to accept. Participants also expressed the need for the government to adopt a learning attitude towards improvements in pandemic response, and to generate more focus on long-term strategies. The deliberative mini-public, revealed public considerations that reflected public values and needs. These considerations might be helpful in better aligning epidemic management policies with public perspectives. Regarding the deliberative mini-public, uncertainties remain about the design and impact on a bigger scale.