Yoxas/statistical_literacy huggingface dataset:
Despite the increasing number of sexual and gender minority (SGM) patients in the United States and designation by the National Institutes of Health as a population with health disparities, available tools are lacking to train medical students on appropriate care for this population. Therefore, we developed and implemented a novel, self-directed, 2-week online elective for undergraduate medical students. The objective of our study was to evaluate the effectiveness of this course in increasing medical students' competency and confidence in caring for SGM patients. We developed the curriculum using Kern's six-step model for curriculum development. We created anonymous pre- and postcourse surveys using the standardized Lesbian, Gay, Bisexual, Transgender Development of Clinical Skills Survey (LGBT-DOCSS) questionnaire to assess cultural competence, as well as a 5-point Likert-scored survey to assess self-perceived confidence in the care of SGM patients. We tested the statistical significance in pre- and postsurvey scores via paired sample t tests in R (R Project for Statistical Computing). We found statistically significant increases in the LGBT-DOCSS categories of clinical preparedness (P&lt;.001), basic knowledge (P&lt;.001), overall competency (P&lt;.001), and self-perceived confidence in caring for SGM patients (P&lt;.001, N=33). The course represents an effective solution for increasing medical students' self-perceived competence and confidence in caring for SGM patients. The flexibility and ease of the online format may be appealing to both students and institutions, and ultimately can serve to increase access to crucial content that is largely absent from current undergraduate medical education. Future evaluation efforts will be required to determine whether the course impacts long-term behavioral changes and outcomes.
Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.
Despite the longstanding psychosocial impact of the interactable conflict in Somalia for the last 30 years, there is lack of epidemiological studies of mental health conditions, especially at the population level. The aim of this study is to fill the epidemiological gap and provide population based data on mental health conditions in the South-Central region of Somalia. The specific objectives were: (1) To determine the epidemiological patterns of mental disorders in three sites; Baidoa, Dolow and Kismayo, (2) Understand the socio-demographic characteristics associated with mental health conditions in the study sites, and (3) To assess the correlates between psychological trauma and the mental wellbeing of the population. This was a cross-sectional study of 713 respondents recruited from the three sites namely Dolow, Baidoa and Kismayo. Data on sociodemographic characteristics and mental disorders were collected using the MINI and sociodemographic questionnaire. Basic descriptive statistics were used to summarize sociodemographic characteristics. Univariable and multivariable logistic regressions were used to examine factors associated with common mental disorders. Statistical significance was considered at a value of p <0.05. Participants' mean age was 32.6 (±10.7) years. More than half (58.5%) of the respondents were male. The overall prevalence of common mental disorders was 557 (78.1%) with panic disorder (39.3%), generalized anxiety disorders (34.9%), major depressive episode current (32.1) and PTSD (29.9%). According to the multivariable logistic regression analysis, being male AOR = 1.74 (95%CI = 1.25, 2.42), having a family size of more than 10 members AOR =1.37 (95% CI = 1.00, 1.89), being unemployed AOR = 1.90 (95%CI = 1.18, 3.06), experienced starvation AOR =3.46 (95%CI = 2.23, 5.37), khat use AOR = 5.87 (955 CI, 1.75-19.65), were identified as predicting factors for the common mental disorders among the study participants. There is a high prevalence of mental disorders with anxiety disorders being the commonest. Findings reflect earlier studies that showed higher rates in conflict and post-conflict settings. It also aligns with past studies in Somalia. As such, there is an urgent need to integrate mental health and psychosocial support within the primary healthcare and other service sectors such as education considering the vast majority of the population are young.
Despite the many advancements made in patient safety over the past decade, combating diagnostic errors (DEs) remains a crucial, yet understudied initiative toward improvement. This study sought to understand the perception of dental patients who have experienced a dental diagnostic failure (DDF) and to identify patient-centered strategies to help reduce future occurrences of DDF. Through social media recruitment, we conducted a screening survey, initial assessment, and 67 individual patient interviews to capture the effects of misdiagnosis, missed diagnosis, or delayed diagnosis on patient lives. Audio recordings of patient interviews were transcribed, and a hybrid thematic analysis approach was used to capture details about 4 main domains of interest: the patient's DDF experience, contributing factors, impact, and strategies to mitigate future occurrences. Dental patients endured prolonged suffering, disease progression, unnecessary treatments, and the development of new symptoms as a result of experiencing DE. Poor provider communication, inadequate time with provider, and lack of patient self-advocacy and health literacy were among the top attributes patients believed contributed to the development of a DE. Patients suggested that improvements in provider chairside manners, more detailed patient diagnostic workups, and improving personal self-advocacy; along with enhanced reporting systems, could help mitigate future DE. This study demonstrates the valuable insight the patient perspective provides in understanding DEs, therefore aiding the development of strategies to help reduce the occurrences of future DDF events. Given the challenges patients expressed, there is a significant need to create an accessible reporting system that fosters constructive clinician learning.
Despite the overwhelming evidence that multiple sclerosis is an autoimmune disease, relatively little is known about the precise nature of the immune dysregulation underlying the development of the disease. Reasoning that the CSF from patients might be enriched for cells relevant in pathogenesis, we have completed a high-resolution single-cell analysis of 96 732 CSF cells collected from 33 patients with multiple sclerosis (n = 48 675) and 48 patients with other neurological diseases (n = 48 057). Completing comprehensive cell type annotation, we identified a rare population of CD8+ T cells, characterized by the upregulation of inhibitory receptors, increased in patients with multiple sclerosis. Applying a Multi-Omics Factor Analysis to these single-cell data further revealed that activity in pathways responsible for controlling inflammatory and type 1 interferon responses are altered in multiple sclerosis in both T cells and myeloid cells. We also undertook a systematic search for expression quantitative trait loci in the CSF cells. Of particular interest were two expression quantitative trait loci in CD8+ T cells that were fine mapped to multiple sclerosis susceptibility variants in the viral control genes ZC3HAV1 (rs10271373) and IFITM2 (rs1059091). Further analysis suggests that these associations likely reflect genetic effects on RNA splicing and cell-type specific gene expression respectively. Collectively, our study suggests that alterations in viral control mechanisms might be important in the development of multiple sclerosis.
Despite the positive impact of trade liberalization on food availability in India, severe inequality in nutrition consumption at the district level persists. Empirical evidence on the relationship between trade liberalization and nutrition consumption inequality often offers a country-level perspective and generates disputed outcomes. The study aimed to explore the effects of trade liberalization on inequality in nutrition consumption at the district level in India and to examine the heterogeneity of the impact on different nutrition consumption. Our study employed the Gini Index to measure nutrition consumption inequality of 2 macronutrients and 5 micronutrients at the district level in India during 2009-2011, utilizing the comprehensive FAO/WHO individual food consumption data. The import tariff was adopted as a proxy for trade liberalization, as its externally imposed nature facilitates a causal interpretation. We further identified the direct causal relationship between food trade liberalization and inequality in nutrition consumption using a fixed effects model. The results show that more than 50% of the individuals in the survey districts did not meet the dietary standards for both macronutrients and micronutrients. Food trade liberalization hindered the improvement of inequality in nutrition consumption. As import tariffs were reduced by 1%, the inequality in intake of calories, zinc, vitamin B1, and vitamin B2 increased significantly by 0.45, 0.56, 0.48, and 0.66, respectively, which might be related to food market performance. The results also highlight the positive role of the gender gap, female-headed households, and caste culture on inequality in nutrition consumption in India. To ease the shock of liberalization and minimize its inequality effects, complementary measures should be adopted, such as improving food logistic conditions in poor areas, and nutrition relief schemes.
Despite the prevalence of non-English languages in the US population, existing medical training to teach communication with linguistically diverse communities is limited to electives or solely focuses on medical interpreting. Language-appropriate communication skills are seldom comprehensively integrated in medical education. This study describes the development and evaluation of an intervention to teach foundational language equity concepts. The authors implemented a pre-clinical language equity course at three medical school campuses between August 2020 and March 2022. Sessions focused on the impact of language in health, physician language proficiency standards, and working with medical interpreters. The study sought to (1) understand students' language skills and prior clinical experiences with patients with non-English language preference and (2) evaluate the curriculum's impact. Students self-reported their language skills and experiences as part of a voluntary pre-questionnaire. Pre and post-questionnaires evaluated knowledge, attitudes, and intent to apply language equity concepts. Descriptive statistics and chi-squared tests were used to examine trends; themes were identified from free-text responses. Overall, 301 students completed the course, 252 (83%) completed at least one questionnaire; for each session, between 35% and 46% of learners completed both pre and post-questionnaires. Three quarters (189/252) reported non-English languages. Over half (138/252) reported previous non-English language patient care, and 28% (62/224) had served as ad hoc (untrained) interpreters. Only two students (< 1%) had ever been assessed for medical language abilities. Students demonstrated improved post-course language equity knowledge, strategies for interpreter-mediated encounters, and likelihood to report a plan for language skills assessment (all p < .001). Most plans were multifaceted (61%, 38/62), involving goals like completing a language course, taking a proficiency exam, openly discussing skills and uncertainties with team members, and increasing professional interpreter utilization. A longitudinal language equity curriculum can be feasibly integrated in pre-clinical education, highlight the linguistic diversity of the student body, and serve as a first step in ensuring that all students have a strong language equity foundation prior to clinical rotations. Future steps include evaluating the intervention's potential long-term effects on professional interpreter utilization, student clinical performance, and institutional culture that promotes multilingualism.
Despite the progress made in this decade towards malaria elimination, it remains a significant public health concern in India and many other countries in South Asia and Asia Pacific region. Understanding the historical trends of malaria incidence in relation to various commodity and policy interventions and identifying the factors associated with its occurrence can inform future intervention strategies for malaria elimination goals. This study analysed historical malaria cases in India from 1990 to 2022 to assess the annual trends and the impact of key anti-malarial interventions on malaria incidence. Factors associated with malaria incidence were identified using univariate and multivariate linear regression analyses. Generalized linear, smoothing, autoregressive integrated moving averages (ARIMA) and Holt's models were used to forecast malaria cases from 2023 to 2030. The reported annual malaria cases in India during 1990-2000 were 2.38 million, which dropped to 0.73 million cases annually during 2011-2022. The overall reduction from 1990 (2,018,783) to 2022 (176,522) was 91%. The key interventions of the Enhanced Malaria Control Project (EMCP), Intensified Malaria Control Project (IMCP), use of bivalent rapid diagnostic tests (RDT-Pf/Pv), artemisinin-based combination therapy (ACT), and involvement of the Accredited Social Health Activists (ASHAs) as front-line workers were found to result in the decline of malaria significantly. The ARIMA and Holt's models projected a continued decline in cases with the potential for reaching zero indigenous cases by 2027-2028. Important factors influencing malaria incidence included tribal population density, literacy rate, health infrastructure, and forested and hard-to-reach areas. Studies aimed at assessing the impact of major commodity and policy interventions on the incidence of disease and studies of disease forecasting will inform programmes and policymakers of steps needed during the last mile phase to achieve malaria elimination. It is proposed that these time series and disease forecasting studies should be performed periodically using granular (monthly) and meteorological data to validate predictions of prior studies and suggest any changes needed for elimination efforts at national and sub-national levels.
Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.
Despite the relevance of basic numerical skills to later mathematical outcomes, there remains little consensus on which skills comprise the construct of early numeracy. Here, we evaluate recent studies that investigated this construct using various approaches. Findings from these studies indicated broad consensus in considering skills such as counting, number relations, and basic arithmetic, as central aspects of early numeracy. However, both the number and contents of identified factors varied considerably across studies. Although this review substantiated the critical relevance of certain basic numerical skills, it also highlights a need for future research to evaluate the structure of early numeracy in a more systematic and coordinated way to increase comparability and coherence across studies.
Despite the success of the growing data-driven landslide susceptibility prediction, the model training heavily relies on the quality of the data (involving topography, geology, hydrology, land cover, climate, and human activity), the structure of the model, and the fine-tuning of the model parameters. Few data-driven methods have considered incorporating 'landslide priors', as in this article the prior knowledge or statistics related to landslide occurrence, to enhance the model's perception in landslide mechanism. The main objective and contribution of this study is the coupling of landslide priors and a deep learning model to improve the model's transferability and stability. This is accomplished by selecting non-landslide sample grounded on landslide statistics, disentangling input landslide features using a variational autoencoder, and crafting a loss function with physical constraints. This study utilizes the SHAP method to interpret the deep learning model, aiding in the acquisition of feature permutation results to identify underlying landslide causes. The interpretation result indicates that 'slope' is the most influential factor. Considering the extreme rainfall impact on landslide occurrences in Hong Kong, we combine this prior into the deep learning model and find feature ranking for 'rainfall' improved, in comparison to the ranking result interpreted from a pure MLP. Further, the potency of MT-InSAR is utilized to augment the landslide susceptibility map and promote efficient cross-validation. A comparison of InSAR results with historical images reveals that detectable movement before their occurrence is evident in only a minority of landslides. Most landslides occur spontaneously, exhibiting no precursor motion. Comparing with other data-driven methods, the proposed methods outperform in accuracy (by 2 %-5 %), precision (by 2 %-7 %), recall (by 1 %-3 %), F1-score (by 8 %-10 %), and AuROC (by 2 %-4 %). Especially, the Cohen Kappa performance surpasses nearly 20 %, indicating that the knowledge-aware methodology enhances model generalization and mitigates training bias induced by unbalanced positive and negative samples.
Despite the well-established benefits of early initiation of breastfeeding and exclusive breastfeeding for the first six months to promote optimal neonatal and child health, evidence indicates that in Ethiopia, a significant number of newborns initiate breastfeeding late, do not adhere to exclusive breastfeeding (EBF) for the recommended duration, and instead are fed with bottles. To determine the proportion of delayed initiation of breast milk, exclusive breastfeeding, and its individual and community-level predictors among mothers in Ethiopia. A secondary data analysis was done using the 2019 Ethiopian Mini Demographic Health Survey data. We examined a weighted sample of 2,012 children born within the past 24 months and 623 children aged 0-5 months at the time of the survey. The data analysis was done using STATA version 15. To understand the variation in delayed initiation and exclusive breastfeeding, statistical measures such as the Intraclass correlation coefficient, median odds ratio, and proportional change in variance were calculated. We employed a multilevel mixed-effects logistic regression model to identify predictors for each outcome variable. Statistical significance was determined with a p-value < 0.05. The proportion of delayed initiation of breast milk and exclusive breastfeeding were 24.56 and 84.5%, respectively. Women aged 34-49 years old (AOR = 0.33: 95% CI; 0.15-0.72), having a television in the house (AOR = 0.74: 95%CI; 0.33-0.97), delivered by cesarean section (AOR = 3.83: 95% CI; 1.57-9.32), and resided in the Afar regional state (AOR = 1.43: 95%CI; 1.03-12.7) were significantly associated with delayed initiation of breast milk. On the other hand, attended primary education (AOR = 0.67: 95%CI; 0.35-0.99), secondary education (AOR = 0.34: 95%CI; 0.19-0.53), women whose household headed by male (AOR = 0.68; 95% CI; 0.34-0.97), and rural residents (AOR = 1.98: 95%CI; 1.09-3.43) were significantly associated with exclusive breastfeeding practice. Health promotion efforts that encourage timely initation of breast milk and promote EBF, focused on young mothers, those who gave birth through cesarean section, and those residing in urban and the Afar regional state. Furthermore, government health policymakers and relevant stakeholders should consider these identified predictors when revising existing strategies or formulating new policies.