Yoxas/statistical_literacy huggingface dataset:
Sociocultural engagement of students refers to broadening viewpoints and providing awareness of, and respect for, diverse backgrounds and perspectives. However, there are no existing validated instruments in the literature for measuring sociocultural engagement of health professions education (HPE) students. Therefore, the aim of this study is to develop and validate a questionnaire designed to assess sociocultural engagement among HPE students. The study included undergraduate HPE students (n = 683) at Gulf Medical University. The initial version of the sociocultural engagement of students' questionnaire (SESQ) was developed after extensive literature review and guided by the Global Learning Qualifications Framework. We then tested the content validity of the questionnaire by using focus group discussion with subject experts (n = 16) and pilot testing with students (n = 20). We distributed the content-validated version of the SESQ (16 items) to undergraduate students in six HPE colleges. To examine the construct validity and construct reliability of the questionnaire, we conducted exploratory factor analysis, followed by confirmatory factor analysis. Confirmatory factor analysis supported the two-factor structure which consists of 13 items with good fitness indices (χ2 = 214.35, df = 61, χ 2/df = 3.51, CFI = 0.98, RMSEA = 0.06, SRMR = 0.025, and AIC = 208.00). The two factors were sociocultural interactions (8 items) and sociocultural adaptation (5 items). The construct reliability of the total questionnaire is 0.97 and the two factors were 0.93 and 0.92 for sociocultural interactions and sociocultural adaptation, respectively. In addition, there were significant weak correlations between both factors of sociocultural engagement scores and student satisfaction with the university experience (r = .19 for each, P = .01). The sociocultural engagement of students' questionnaire exhibits good evidence of construct validity and reliability. Further studies will be required to test the validity of this questionnaire in other contexts.
Socioeconomic conditions are strongly associated with breast and cervical cancer incidence and mortality patterns; therefore, social protection programmes (SPPs) might impact these cancers. This study aimed to evaluate the effect of SPPs on breast and cervical cancer outcomes and their risk/protective factors. Five databases were searched for articles that assessed participation in PPS and the incidence, survival, mortality (primary outcomes), screening, staging at diagnosis and risk/protective factors (secondary outcomes) for these cancers. Only peer-reviewed quantitative studies of women receiving SPPs compared to eligible women not receiving benefits were included. Independent reviewers selected articles, assessed eligibility, extracted data, and assessed the risk of bias. A harvest plot represents the included studies and shows the direction of effect, sample size and risk of bias. Of 17,080 documents retrieved, 43 studies were included in the review. No studies evaluated the primary outcomes. They all examined the relationship between SPPs and screening, as well as risk and protective factors. The harvest plot showed that in lower risk of bias studies, participants of SPPs had lower weight and fertility, were older at sexual debut, and breastfed their infants for longer. No studies have yet assessed the effect of SPPs on breast and cervical cancer incidence, survival, or mortality; nevertheless, the existing evidence suggests positive impacts on risk and protective factors.
Socioeconomic inequalities in health persist in Spain. The DDHealth project aims to address two timely innovative aspects that have been postulated to contribute to socioeconomic inequalities in health. DDHealth aims to address two innovative and timely aspects that have been proposed to contribute to socioeconomic health inequalities. The first one is the socioeconomic digital divide, which refers to the greater capabilities and opportunities to access technology and use the internet among higher social classes compared to lower ones. The second aspect is health literacy, which refers to individuals' capacity to meet and understand the complex demands of health promotion and maintenance in modern society. The study conducted over 2,000 interviews among residents in Spain aged between fifty and seventy-nine years old from March to April 2022, using a computer-assisted telephone interviewing (CATI) approach. The questionnaire comprises four different modules: sociodemographic; digital divide; health; health literacy. The anonymized data are available through the following link: https://dataverse.csuc.cat/dataset.xhtml?persistentId=doi:10.34810/data765. DDHealth enables addressing innovative dimensions concerning the social determinants of health in Spain. The data are available to external researchers for scientific purposes upon request of a reasonable research proposal.
Socioeconomic inequalities in type 2 diabetes (T2D) are well established in the literature. However, within the background of changing work contexts associated with digitalization and its effect on lifestyle and sedentary behavior, little is known on T2D prevalence and trends among different occupational groups. This study aims to examine occupational sector differences in T2D prevalence and trends thereof between 2012 and 2019. The study was done on 1.683.644 employed individuals using data from the German statutory health insurance provider in Lower Saxony, the "Allgemeine Ortskrankenkasse Niedersachsen" (AOKN). Predicted probabilities for T2D prevalence in four two-year periods between 2012 and 2019 were estimated based on logistic regression analyses for nine occupational sectors. Prevalence ratios were calculated to illustrate the effect of time period on the prevalence of T2D among the nine occupational sectors. Analyses were stratified by gender and two age groups. Results showed differences among occupational sectors in the predicted probabilities for T2D. The occupational sectors "Transport, logistics, protection and security" and "Health sector, social work, teaching & education" had the highest predicted probabilities, while those working in the sector "Agriculture" had by far the lowest predicted probabilities for T2D. Over all, there appeared to be a rising trend in T2D prevalence among younger employed individuals, with gender differences among occupational sectors. The study displayed different vulnerability levels among occupational sectors with respect to T2D prevalence overall and for its rising trend among the younger age group. Specific occupations within the vulnerable sectors need to be focused upon in further research to define specific target groups to which T2D prevention interventions should be tailored.
Socioeconomic status (SES) has been linked to mortality rates, with family income being a quantifiable marker of SES. However, the precise association between the family income-to-poverty ratio (PIR) and all-cause mortality in adults aged 40 and older remains unclear. A cross-sectional study was conducted using data from NHANES III, including 20,497 individuals. The PIR was used to assess financial status, and various demographic, lifestyle, and clinical factors were considered. Mortality data were collected from the NHANES III linked mortality file. The study revealed a non-linear association between PIR and all-cause mortality. The piecewise Cox proportional hazards regression model showed an inflection point at PIR 3.5. Below this threshold, the hazard ratio (HR) for all-cause mortality was 0.85 (95% CI 0.79-0.91), while above 3.5, the HR decreased to 0.66 (95% CI 0.57-0.76). Participants with lower income had a higher probability of all-cause mortality, with middle-income and high-income groups showing lower multivariate-adjusted HRs compared to the low-income group. This study provides evidence of a non-linear association between PIR and all-cause mortality in adults aged 40 and older, with an inflection point at PIR 3.5. These findings emphasize the importance of considering the non-linear relationship between family income and mortality when addressing socioeconomic health disparities.
Socioeconomic status (SES) is negatively associated with innumerable health outcomes, including cognitive functioning. Yet much remains undiscovered about SES patterns in later-life cognition in low-and middle-income countries (LMICs). The purpose of this study was to examine the association between separate and combined socioconomic risks and cognitive impairment among older adults in India. Further, given gender disparities in later life cognitive functioning and SES, the study examines the associations between socioeconomic risks and cognitive impairment separately, for older men and women. Data come from the 2017-18, first wave of the Longitudinal Aging Study in India (LASI), with 31,464 older adults aged 60 years and above. Cognitive impairment was assessed using multiple broad measures of memory, orientation, arithmetic function, and visuo-spatial construction skills. We present descriptive statistics along with cross-tabulation of the outcome variable. Additionally, binary logistic regression analysis was used to test the association between outcome and explanatory variables. SES is measured using education, paid work status, and household wealth measured using monthly per-capita consumption expenditure (MPCE). A proportion of 7.14% of the older men and 20.03% of older women reported cognitive impairment. The odds of cognitive impairment were higher among uneducated older men and women, and older men and women in lowest wealth quintile. Surprisingly, older women without current or prior work history report lower odds of cognitive impairment compared to their peers in labor force. While odds of cognitive impairment are higher among non-working older men, this association is not statistically significant. In older men, the odds of cognitive impairment were 5.34, 7.14, and 13.05 times higher with one, two, and three risk factors, respectively, compared with those with no risk exposure. A similar trend was observed for women but with comparatively lower odds. Our findings underscore the need to distinguish between varying elements of SES to construct "upstream" health policies and programs that redistribute resources. In particular, the findings support the use of multiple SES indicators in identifying older adults most susceptible to cognitive deficits, and planning gender-based interventions to improve cognitive health in late life.
Sodium-Glucose Cotransporter 2 Inhibitors (SGLT2i) and Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA) improve cardiorenal outcomes in patients with type 2 diabetes. Equitable use of SGLT2i and GLP-1 RA has the potential to reduce racial and ethnic health disparities. We evaluated trends in pharmacy dispensing of SGLT2i and GLP-1 RA by race and ethnicity. Retrospective cohort study of patients (≥18 years) with type 2 diabetes using 2014-2022 electronic health record data from six US care delivery systems. Entry was at earliest pharmacy dispensing of any type 2 diabetes medication. We used multivariable logistic regression to evaluate the association between pharmacy dispensing of SGLT2i and GLP1-RA and race and ethnicity. Our cohort included 687,165 patients (median 6 years of dispensing data; median 60 years; 0.3% American Indian/Alaska Native (AI/AN), 16.6% Asian, 10.5% Black, 1.4% Hawaiian or Pacific Islander (HPI), 31.1% Hispanic, 3.8% Other, and 36.3% White). SGLT2i was lower for AI/AN (OR 0.80, 95% confidence interval 0.68-0.94), Black (0.89, 0.86-0.92) and Hispanic (0.87, 0.85-0.89) compared to White patients. GLP-1 RA was lower for AI/AN (0.78, 0.63-0.97), Asian (0.50, 0.48-0.53), Black (0.86, 0.83-0.90), HPI (0.52, 0.46-0.57), Hispanic (0.69, 0.66-0.71), and Other (0.78, 0.73-0.83) compared to White patients. Dispensing of SGLT2is, and GLP-1 RAs was lower in minority group patients. There is a need to evaluate approaches to increase use of these cardiorenal protective drugs in patients from racial and ethnic minority groups with type 2 diabetes to reduce adverse cardiorenal outcomes and improve health equity. Patient-Centered Outcomes Research Institute and National Institutes of Health.
Softball athletes require multiple fitness traits (e.g., strength, speed, power) and sport-specific skills (e.g., hitting, throwing) for success. Lower-body strength could underpin these qualities; this has received little analysis among high school female athletes. This research investigated correlations between absolute and relative lower-body strength with age, linear speed, lower-body power, and throwing and hitting velocity in high school girls softball athletes. Archival data collected from 34 high school girls softball players (age=14.91±1.00 years; height=1.66±0.07 m; body mass=63.21±9.59 kg) from a private strength and conditioning facility was analyzed. The data included: age, height, and body mass; 0-9.14 and 0-18.29 m sprint interval times; standing broad jump (SBJ) distance (lower-body power); batted ball exit (i.e., hitting) and throwing velocity; and absolute and relative three-repetition maximum (3RM) front squat and hexagonal bar deadlift (HBD). Pearson's correlations (p<0.05) derived relationships between absolute and relative strength with the fitness and sport-specific tests. The results indicated significant relationships between the 3RM HBD with age (r=0.389) and hitting velocity (r=0.418). The 3RM front squat related to the SBJ (r=0.422) and hitting velocity (r=0.457). Relative 3RM HBD correlated with the 0-18.29 m sprint interval (r=-0.349). These results suggested that a strength and conditioning program that improves the lower-body strength of high school girls softball players could contribute to faster sprinting speed, further horizontal jumps, and greater hitting velocity. The results from this study highlights the value of strength enhancement in high school girls softball athletes and provides support for strength and conditioning program provision for these individuals.
Soil organic matter (SOM) is a key soil sorbent with a large number of reactive binding sites that may complex with metals, controlling their fate, transport, and bioavailability in soil. However, due to the complex and heterogeneous nature of SOM, it is not easy to probe its physical and chemical properties at the molecular level. In this study, an a priori method was developed to predict the molecular properties of SOM, which incorporated computational molecular modeling, SPARC Performs Automated Reasoning in Chemistry's (SPARC's) chemical reactivity models, and linear free energy relationships (LFERs). Specifically, the method uses SOM models simulated by molecular dynamics modeling based on the experimental elemental composition and functional group information of SOM. For the molecular characteristics, the molecular H/C and O/C ratios, molecular weight, aromatic index, and double bond equivalence of the SOM molecules were calculated. For the proton binding constants, the SPARC was used to calculate the microscopic pKa values of every binding sites of individual molecules of the SOM model. Based on the pKa values, the metal binding constants for individual monodentate binding sites were calculated using the Irving-Rossotti LFERs for different heavy metals. The results agreed reasonably with the default values used in the Windermere Humic Aqueous Model (WHAM) (VI) for the investigated metals. The theoretical SOM models, to some extent, represented the average properties of the investigated SOM. Overall, this study gives new quantitative and molecular insight into the structure and chemical properties of SOM. Detailed deprotonation and metal-SOM complexation information was gained for individual SOM binding sites. Such feasible and straightforward predictive scheme is useful to assess the risk of heavy metals in various aquatic and terrestrial environment involving heterogeneous natural organic matter.
Soil-transmitted helminthiasis (STH) are a major public health problem in Sub-Saharan Africa. In Kenya, the National School Based Deworming Program (NSBDP) was launched in 2012 with a goal of reducing STH prevalence in school-aged children (SAC) to <1%, however monitoring and evaluation results have consistently showed > 20% prevalence in Narok County. We conducted a study to investigate factors associated with STH infections among SAC in Narok County. A cross-sectional study was conducted among 514 SAC from five schools in Trans Mara West sub-county, Narok County. The sub-county was selected because it had participating schools within the NSBDP with a high prevalence of STH infection. Participants were selected using systematic random sampling. Stool samples collected from participants were examined for STH eggs using Kato-Katz technique. An open data kit questionnaire was used to collect socio-demographics, household, and STH knowledge information from 139 of the 514 SAC. Descriptive statistics was used to summarize the data, prevalence and mean intensity of infections were calculated, and logistic regression used to determine factors associated with STH infections. The overall prevalence of any STH infection was 24.6% (95%CI: 21.1-28.6). Trichuris trichiura 14.4% (95%CI: 11.7-17.8), Ascaris lumbricoides 12.5% (95%CI: 9.9-15.7) and hookworm 0%. From multivariable analysis the only factors significantly associated with increased risk of STH infection were, children attending Karda and Nkarano schools with aOR = 5.29 (95%CI: 1.45-19.24); p = 0.011 and aOR = 4.53 (95%CI: 1.29-15.97); p = 0.019 respectively. For A. lumbricoides, children attending Nkarano School were associated with a significant risk of infection with aOR = 7.81 (95%CI: 1.81-33.63); p = 0.006. Despite the ongoing work of NSBDP, the STH prevalence is still ≥ 20% in Trans Mara West sub-county Narok County, among SAC. This underscores the need for continued annual MDA. Additionally, if possible, drug combinations may effectively manage T. trichiura, the region's most common helminth. The study found a correlation between children attending specific schools and STH infection risk, suggesting the importance of health education and improved water, sanitation, and hygiene practices holistically both in schools and associated catchment areas that can act as STH reservoirs to alleviate the burden of STH.
Soil-Vegetation-Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth's system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model's usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models' inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.
Solving Bayesian problems poses many challenges, such as identifying relevant numerical information, classifying, and translating it into mathematical formula language, and forming a mental representation. This triggers research on how to support the solving of Bayesian problems. The facilitating effect of using numerical data in frequency format instead of probabilities is well documented, as is the facilitating effect of given visualizations of statistical data. The present study not only compares the visualizations of the 2 × 2 table and the unit square, but also focuses on the results obtained from the self-creation of these visualizations by the participants. Since it has not yet been investigated whether the better correspondence between external and internal visualization also has an effect on cognitive load when solving Bayesian tasks, passive and active cognitive load are additionally measured. Due to the analog character and the proportional representation of the numerical information by the unit square, it is assumed that the passive cognitive load is lower when using the unit square as visualization than when using the 2 × 2 table. The opposite is true for active cognitive load.
Solving ill-posed inverse problems typically requires regularization based on prior knowledge. To date, only prior knowledge that is formulated mathematically (e.g., sparsity of the unknown) or implicitly learned from quantitative data can be used for regularization. Thereby, semantically formulated prior knowledge derived from human reasoning and recognition is excluded. Here, we introduce and demonstrate the concept of semantic regularization based on a pre-trained large language model to overcome this vexing limitation. We study the approach, first, numerically in a prototypical 2D inverse scattering problem, and, second, experimentally in 3D and 4D compressive microwave imaging problems based on programmable metasurfaces. We highlight that semantic regularization enables new forms of highly-sought privacy protection for applications like smart homes, touchless human-machine interaction and security screening: selected subjects in the scene can be concealed, or their actions and postures can be altered in the reconstruction by manipulating the semantic prior with suitable language-based control commands.