Yoxas/statistical_literacy huggingface dataset:
Stroke, a leading cause of morbidity and mortality globally, demands heightened awareness and knowledge for effective preventive strategies and tailored response. Sudan is classified as a low income country with a low rate of literacy, lack of knowledge, and awareness about diseases. Thus, this study aimed to assess stroke awareness and knowledge among Sudanese population, and identify the associated factors influencing awareness. A cross-sectional study conducted between October and November 2022 through a self-administered online survey distributed via various social media platforms. The study involved adults aged 18 years and above through snow-ball sampling technique. The survey covered general awareness and knowledge concerning stroke risk factors, consequences, and the appropriate responses taken during acute stroke attacks. A total of 410 participants were enrolled in the study, majority (93.4%) were from urban area and had university degree (92.4%). Furthermore, 92.2% were aware about stroke and 74.9% were able to recognize the symptoms of stroke. Only 40.2% identified all correct answers, 96.3, 92.3, and 95.1% recognized at least one risk factor, early symptom, and consequences, respectively. Females were significantly more than males able to identify at least one risk factor. Almost all participants (99.5%) perceived stroke as a serious disease (99.5%). Notably, 86.3% would promptly transport a suspected stroke patient to the hospital. The multivariable analysis showed that females versus males and patients with depression versus without depression had significantly higher odds to identify at least one risk factor (OR of 14.716 [95% CI 1.901; 113.908] and 0.241 [95% CI 0.059; 0.984], respectively). The study concluded that stroke knowledge and awareness among Sudanese population is suboptimal. Furthermore, early stroke recognition and intake of the appropriate management strategies are lacking which highlights the need for targeted education and awareness campaigns.
Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.
Strong policy guidance has recently emerged identifying focal points at multiple levels and across sectors to end the persistent HIV pandemic and related inequities. Reducing the policy-implementation gap, as with the evidence-policy gap, requires strategic alignment between interventional research and policy realms. Global- and national-level HIV policy indicate a need for community-led efforts to reduce HIV stigma, and increase uptake of HIV prevention tools. This study assesses a process-driven approach to facilitating community-led efforts to reduce HIV stigma, and build a generative context for community-led HIV prevention. The study intervention combines an adapted group-based microfinance process, a novel psychological curriculum, and leadership development at a scale now involving over 10,000 rural Kenyans across 39 villages. Consistent with interventional goals, and current relevant psychosocial theories, we find collective emotion, and HIV stigma (blame and discrimination) significantly improve with more time participating in the in the program and novel curriculum. Further, HIV stigma predicts subsequent reporting of ever being tested for HIV, and the intervention led to the development of "HIV prevention resource committees" - groups of participants committed to undergo training to reduce HIV stigma and prevent HIV within their communities. Implications for further research to reduce the HIV policy-implementation gap are discussed, directly within this interventional context and more generally.
Strong scientific writing skills are the foundation of a successful research career and require training and practice. Although these skills are critical for completing a PhD, most students receive little formal writing instruction prior to joining a graduate program. In 2015, the University of Iowa Medical Scientist Training Program (MSTP) addressed this issue by developing the scientific writing course Grant Writing Basics (GWB). Here we describe the structure of this course and its effectiveness. GWB is an interactive, workshop-based course that uses a National Institutes of Health (NIH) F30 predoctoral fellowship proposal as a platform for building writing expertise. GWB incorporates established pedagogical principles of adult learning, including flipped classrooms, peer teaching, and reiterative evaluation. Time spent in class centers on active student analysis of previously submitted fellowship applications, discussion of writing resources, active writing, facilitated small group discussion of critiques of student writing samples, revision, and a discussion with a panel of experienced study section members and a student who completed a fellowship submission. Outcomes of GWB include a substantial increase in the number of applications submitted and fellowships awarded. Rigorous evaluation provides evidence that learning objectives were met and that students gained confidence in both their scientific writing skills and their ability to give constructive feedback. Our findings show that investment in formal training in written scientific communication provides a foundation for good writing habits, and the knowledge and skills needed to succeed in this vital aspect of a scientific research career. Furthermore, they highlight that evaluation is valuable in guiding course evolution. Strategies embedded in GWB can be adapted for use in any graduate program to advance scientific writing skills among its trainees.
Structural and contextual factors such as limited work and housing opportunities negatively affect the health and well-being of newly settled refugee migrants in receiving high-income countries. Health promotion initiatives aiming at strengthening health and integration have been tried out within the Swedish Introduction program for refugee migrants. However, longitudinal evaluations of these interventions are rare. The aim of the current study was to compare the effectiveness of a regular and an extended civic orientation course with added health communication and examine whether the latter would improve self-rated health and psychological well-being, health literacy and social capital among newly settled refugee migrants in Sweden. Pre- and post-assessment questionnaires were collected from the intervention group receiving the extended course (n = 143) and a control group receiving the regular course (n = 173). Linear mixed models and chi-square analyses showed a significant increase with a small effect size (0.21) in health literacy in the intervention group. However, there were no significant changes in emotional and practical support, general self-rated health or psychological well-being. The findings indicate that added health communication provided embedded in the civic orientation course can increase health literacy. However, further longitudinal studies are needed to confirm the sustainability of the observed effect and examine whether these short-term improvements in health literacy translate to long-term advances in health and integration.
Structural gendered racism - the "totality of interconnectedness between structural racism and sexism" - is conceptualized as a fundamental cause of the persistent preterm birth inequities experienced by Black and Indigenous people in the United States. Our objective was to develop a state-level latent class measure of structural gendered racism and examine its association with preterm birth among all singleton live births in the US in 2019. Using previously-validated inequity indicators between White men and Black women across 9 domains (education, employment, poverty, homeownership, health insurance, segregation, voting, political representation, incarceration), we conducted a latent profile analysis to identify a latent categorical variable with k number of classes that have similar values on the observed continuous input variables. Racialized group-stratified multilevel modified Poisson regression models with robust variance and random effects for state assessed the association between state-level classes and preterm birth. We found four distinct latent classes that were all characterized by higher levels of disadvantage for Black women and advantages for White men, but the magnitude of that difference varied by latent class. We found preterm birth risk among Black birthing people was higher across all state-level latent classes compared to White birthing people, and there was some variation of preterm birth risk across classes among Black but not White birthing people. These findings further emphasize the importance of understanding and interrogating the whole system and the need for multifaceted policy solutions.
Structural health monitoring (SHM) is challenged by massive data storage pressure and structural fault location. In response to these issues, a bio-inspired memory model that is embedded with a causality reasoning function is proposed for fault location. First, the SHM data for processing are divided into three temporal memory areas to control data volume reasonably. Second, the inherent potential of the causal relationships in structural state monitoring is mined. Causality and dependence indices are also proposed to establish the mechanism of quantitative description of the reason and result events. Third, a mechanism of causality reasoning is developed for the reason and result events to locate faults in a SHM system. Finally, a deformation experiment conducted on a steel spring plate demonstrates that the proposed model can be applied to real-time acquisition, compact data storage, and system fault location in a SHM system. Moreover, the model is compared with some typical methods based on an experimental benchmark dataset.
Structural racism generates racial inequities in U.S. primary education, including segregated schools, inequitable funding and resources, racial disparities in discipline and achievement, and hostile racial climates, which are risk factors for adverse youth health and development. Black youth are disproportionately exposed to adverse school contexts that may become biologically embedded via stress-mediated epigenetic pathways. This study examined whether childhood exposure to adverse school contexts is associated with changes in epigenetic aging during adolescent development. DNA methylation-based epigenetic clocks were calculated from saliva samples at ages 9 and 15 among Black (n = 774) and White (n = 287) youth in the Future of Families and Child Wellbeing Study (2009-2015). We performed latent class analyses to identify race-specific primary school contexts using administrative data on segregation, discipline, achievement, resources, economic disadvantage, and racial harassment. We then estimated change in epigenetic age acceleration from childhood to adolescence across school typologies using GrimAge, PhenoAge, and DunedinPACE epigenetic clocks. Three distinct school contexts were identified for Black youth: segregated and highly-disadvantaged (17.0%), segregated and moderately-disadvantaged (52.1%), and integrated and moderately-disadvantaged (30.8%). Two school contexts emerged for White youth: integrated and unequal (46.5%) and predominantly White & advantaged (53.5%). At age 15, Black youth who attended segregated and highly-disadvantaged primary schools experienced increases in their speed of epigenetic aging with GrimAge and DunedinPACE. Slowed epigenetic aging with GrimAge was observed for Black youth who attended integrated and moderately-disadvantaged schools. School contexts were not associated with changes in epigenetic age acceleration for White youth. Our findings suggest that manifestations of structural racism in primary school contexts are associated with early-life epigenetic age acceleration and may forecast future health inequities.
Structural racism is how society maintains and promotes racial hierarchy and discrimination through established and interconnected systems. Structural racism is theorized to promote alcohol and tobacco use, which are risk factors for adverse health and cancer-health outcomes. The current study assesses the association between measures of state-level structural racism and alcohol and tobacco use among a national sample of 1,946 Black Americans. An existing composite index of state-level structural racism including five dimensions (subscales; i.e., residential segregation and employment, economic, incarceration, and educational inequities) was merged with individual-level data from a national sample dataset. Hierarchical linear and logistic regression models, accounting for participant clustering at the state level, assessed associations between structural racism and frequency of alcohol use, frequency of binge drinking, smoking status, and smoking frequency. Two models were estimated for each behavioral outcome, one using the composite structural racism index and one modeling dimensions of structural racism in lieu of the composite measure, each controlling for individual-level covariates. Results indicated positive associations between the incarceration dimension of the structural racism index and binge drinking frequency, smoking status, and smoking frequency. An inverse association was detected between the education dimension and smoking status. Results suggest that state-level structural racism expressed in incarceration disparities, is positively associated with alcohol and tobacco use among Black Americans. Addressing structural racism, particularly in incarceration practices, through multilevel policy and intervention may help to reduce population-wide alcohol and tobacco use behaviors and improve the health outcomes of Black populations.
Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation. This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies. We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health. While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health. The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.
Structured reporting (SR) not only offers advantages regarding report quality but, as an IT-based method, also the opportunity to aggregate and analyze large, highly structured datasets (data mining). In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.All structured reports for PE CT scans from the last 5 years (n = 2790) were extracted from the SR database and analyzed. The prevalence of PE was calculated for the entire cohort and stratified by referral type and clinical referrer. Distributions of the manifestation of PEs (central, lobar, segmental, subsegmental, as well as left-sided, right-sided, bilateral) were calculated, and the occurrence of right heart strain was correlated with the manifestation.The prevalence of PE in the entire cohort was 24% (n = 678). The median age of PE patients was 71 years (IQR 58-80), and the sex distribution was 1.2/1 (M/F). Outpatients showed a lower prevalence of 23% compared to patients from regular wards (27%) and intensive care units (30%). Surgically referred patients had a higher prevalence than patients from internal medicine (34% vs. 22%). Patients with central and bilateral PEs had a significantly higher occurrence of right heart strain compared to patients with peripheral and unilateral embolisms.Data mining of structured reports is a simple method for obtaining prevalence statistics, epidemiological data, and the distribution of disease characteristics, as demonstrated by the PE use case. The generated data can be helpful for multiple purposes, such as for internal clinical quality assurance and scientific analyses. To benefit from this, consistent use of SR is required and is therefore recommended. · SR-based data mining allows simple epidemiologic analyses for PE.. · The prevalence of PE differs between outpatients and inpatients.. · Central and bilateral PEs have an increased risk of right heart strain.. · Jorg T, Halfmann MC, Graafen D et al. Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms. Fortschr Röntgenstr 2024; DOI 10.1055/a-2301-3349.
Student academic performance is an important indicator of doctoral education quality, but limited research has focused on how multiple influential factors of doctoral students' academic performance work together. This study aims to explore the factors significantly affecting the academic performance of mathematics education doctoral students in Indonesia. Several factors were recognized from prior studies, such as the fear of delay, student engagement, parental support, teacher support, facilitating conditions, stress level, and well-being. An online questionnaire was designed and answered by a total of 147 mathematics education doctoral students. The partial least squares structural equation modeling (PLS-SEM) approach was adopted to analyze the questionnaire data. The results suggested that teacher support had the strongest positive effects on mathematics education doctoral students' academic performance in Indonesia. Student engagement was the most significant positive factor in improving doctoral students' well-being, while parental support could most significantly reduce their stress levels. Practically, these results are expected to provide implications to universities and supervisors regarding the improvement of doctoral students' well-being to promote their academic success and further the quality of doctoral programs in education. Theoretically, these results can also contribute to building an empirical model that can be used to explore and explain how multiple factors could affect doctoral students' academic performance in other contexts.
Student application packages for admission to the University of Georgia College of Veterinary Medicine currently include the following information: undergraduate grade point average (GPA), GPA in science courses, GPA in non-science courses, GPA for the last 45 hours (GPALast45hrs), Graduate Record Examination Quantitative and Verbal Reasoning (GRE-QV) score, GRE Analytical Writing (GRE-AW) score, and grades for 10 required prerequisite courses. From these data, an academics score of up to 70 points is calculated. Faculty reviewers also score each applicant up to a maximum of 30 points (FileScore), giving a total possible score of 100 points. Previous analyses demonstrated that the file score and academic variables are significantly related to first-year GPA of veterinary students; however, it is unknown how these variables relate to performance in clinical rotations. The present study pooled the two most recent graduating classes to compare each academic score component to student clinical rotation grades received during year 4 (CGrYr4) in the teaching hospital. Only one component of the student application packages-the pre-admission GRE-QV score-significantly correlated with CGrYr4.
Student applications for admission to the University of Georgia College of Veterinary Medicine include the following information: undergraduate grade point average (GPA), GPA in science courses (GPAScience), GPA for the last 45 credit hours (GPALast45hrs), results for the Graduate Record Examination Quantitative and Verbal Reasoning Measures (GRE-QV), results for the GRE Analytical Writing Measure (GRE-AW), and grades received for 10 required prerequisite courses. In addition, three faculty members independently review and score subjective information in applicants' files (FileScore). The admissions committee determines a composite Admission Score (AdmScore), which is based on GPA, GPAScience, GPALast45hrs, GRE-QV, GRE-AW, and the FileScore. The AdmScore is generally perceived to be a good predictor of class rank at the end of year 1 (CREY1). However, this has not been verified, nor has it been determined which components of the AdmScore have the strongest correlation with CREY1. The present study therefore compared each component of the AdmScore for correlation with CREY1, for the three classes admitted in 2015, 2016 and 2017 (Class15, Class16, Class17). Results suggest that only a few components of the application file are needed to make strong predictive statements about the academic success of veterinary students during the first year of the curriculum.