Yoxas/statistical_literacy huggingface dataset:
Greater understanding of the association between strabismus and mental health conditions across sociodemographic backgrounds may inform strategies to improve mental well-being in this population. To describe the association of strabismus with mental health conditions in a diverse cohort of US adults. This cross-sectional study used data from the National Institutes of Health's All of Us Research Program, an ongoing program launched in 2015. The study included 3646 adults (aged ≥18 years) with strabismus and 3646 propensity score-matched controls. Statistical analysis was conducted from September 12, 2023, to January 29, 2024. Adults with strabismus were propensity score matched on age, gender, race and ethnicity, income, educational level, and insurance status in a 1:1 ratio with adults without strabismus. The prevalences of anxiety, depression, substance use and addiction, bipolar disorder, and schizophrenia spectrum disorder among adults with strabismus were compared with controls. Logistic regression was used to evaluate the association of mental health conditions with sociodemographic factors in each group. This study included 3646 adults with strabismus (median age, 67 years [IQR, 53-76 years]; 2017 women [55%]) and 3646 propensity score-matched controls (median age, 67 years [IQR, 53-76 years]; 2017 women [55%]). Individuals with strabismus had higher prevalences of anxiety (1153 [32%] vs 519 [14%]; difference, 17%; 95% CI, 15%-19%; P < .001), depression (1189 [33%] vs 514 [14%]; difference, 19%; 95% CI, 17%-20%; P < .001), substance use and addiction (116 [3%] vs 51 [1%]; difference, 2%; 95% CI, 1%-3%; P < .001), bipolar disorder (253 [7%] vs 101 [3%]; difference, 4%; 95% CI, 3%-5%; P < .001), and schizophrenia spectrum disorder (103 [3%] vs 36 [1%]; difference, 2%; 95% CI, 1%-3%; P < .001) compared with individuals without strabismus. Among adults with strabismus, higher odds of mental health conditions were associated with younger age (odds ratio [OR], 1.11 per 10-year decrease; 95% CI, 1.06-1.16 per 10-year decrease), female gender (OR, 1.62; 95% CI, 1.41-1.85), Black or African American race and ethnicity (OR, 1.22; 95% CI, 1.01-1.48), low income (OR, 3.06; 95% CI, 2.56-3.67), and high school education or less (OR, 1.58; 95% CI, 1.34-1.85). In a diverse and nationwide cohort, adults with strabismus were more likely to have mental health conditions compared with adults without strabismus. Further investigation into the risk factors for poor mental health among adults with strabismus across sociodemographic backgrounds may offer novel opportunities for interventions to improve mental well-being in this population.
Green consumers increasingly consider animal welfare (AW) in their decision-making, demonstrating a growing awareness of ethical considerations beyond conventional environmental concerns. However, with an increase in greenwashing, skepticism has grown among consumers. Regardless of manufacturers' efforts to enhance consumer awareness via green advertising, consumer skepticism toward these advertisements creates doubt and consequently reduces positive attitudes and intentions to buy green products. This study investigated the variables that impact Vietnamese consumers' decision-making processes toward green beauty care products. Specifically, we focused on the role of AW concerns and skepticism toward green advertising. For this study, we adopted the timulus-response organism (SOR) framework, which is known for its ability to analyze the impact of environmental stimuli (S) on personal perceptions (O), leading to specific responses (R). We elucidated the relationship between concern for AW and green advertising skepticism (GAS) as stimulus factors, green brand trust (GBT) and attitude toward green products (ATT) as organism factors, and three sub-dimensions of behavioral intentions (BI): green purchase intention (GPI), word-of-mouth (WOM), and willingness to pay (WPP) as response factors. We explored the mediating effect of ATT on the relationship between GAS-GPI and AW-GPI. This study involved 386 Vietnamese participants surveyed using convenience sampling. Smart PLS v3.2.9 software was used to analyze the data using structural equation modeling. The hypothesis test using PLS-SEM confirmed that (a) GBT had a positive effect on BI and ATT; (b) ATT had a positive effect on BI; (c) GAS had a negative effect on GBT and WPP, but not on GPI and WOM; and (d) AW had a positive effect on GPI and WOM, but not on ATT and WPP. Surprisingly, this study found insufficient statistical evidence to support the mediating role of ATT in the relationship between GAS-GPI and AW-GPI. This study highlights the importance of integrated advertising and education campaigns in increasing customer awareness toward green products and the significance of comprehending the cultural context when developing marketing strategies, especially in emerging markets such as Vietnam, where environmental concerns are skeptical and AW issues are relatively new. The study delved into the Vietnam market and specifically examined beauty care products labeled as "not tested on animals." Additionally, we addressed a gap in the existing research by investigating the combined influence of AW concerns and GAS on the formation of green behavioral intentions (GBI).
Green innovation is imperative for the high-quality and sustainable development of the modern logistics industry. It is also key for achieving the goals of peak emissions and carbon neutrality. This study provides a way of thinking about the evaluation of the green innovation level of the logistics industry. The variance inflation factor-variance coefficient method was employed to construct an evaluation index system of the regional logistics green innovation level (RLGIL) from three dimensions. Empirical data were collected from statistical yearbooks covering 30 provinces in China from 2013 to 2017. Thereafter, the combination weighting-based GRA-TOPSIS method was applied to evaluate the RLGIL, and the spatial distribution differences and spatiotemporal evolution characteristics of inter-provincial green innovation levels were analyzed. The RLGILs in the 30 provinces were found to be generally unbalanced, and the differences between the eastern and western regions were significant. Guangdong, Jiangsu, and Zhejiang had stronger RLGILs, whereas most other provinces did not reach the average level. The RLGIL of the 30 provinces had a high positive spatial correlation and spatial aggregating effect. From a national perspective, the values for the RLGIL were generally higher in the eastern and southern regions and lower in the western and northern regions. Although significant differences were found in the RLGIL of these provinces, the overall development trend was stable.
Grit, a combination of enduring effort and persistent interest, is key to long-term goals. The training of preservice physical education (PE) teachers is vital for child development, emphasizing the need to assess their resilience and commitment. However, research is limited regarding how grit influences motivation and achievement goals in PE. The purpose of this study was to explore how the grit dimensions of preservice PE teachers impact their motivation and achievement goals, which may subsequently shape their future career intentions of becoming PE teachers. A total of 279 preservice physical education (PE) teachers (69.5% males; 26.9% PE graduate program) from five South Korean universities participated in the study. They completed validated questionnaires measuring grit, motivation, achievement goal orientations, and career intentions. Descriptive statistics, correlation analysis, and structural equation modeling (SEM) were used to examine variable relationships and test the hypothesis model. Correlation analysis indicated a spectrum of relationships between facets of grit (perseverance of effort and consistency of interests), motivational parameters, and career intention, with both positive and negative correlations ranging from weak to moderate (r ranging from 0.119 to 0.425, p < 0.05-0.01). SEM confirmed the model's goodness-of-fit (χ2/df = 1.928, RMSEA = 0.058, IFI = 0.92, TLI = 0.91, CFI = 0.92). Path analysis showed that both perseverance of effort and consistency of interests significantly influenced motivational mechanisms (β ranging from -0.34 to 0.57, p < 0.05-0.01), both directly and indirectly, which then notably impacted career intentions (β = 0.10, p < 0.05). Notably, both grit dimensions significantly impacted mastery approach goals (β ranging from 0.49 to 0.56, p < 0.01). Mastery approach goals, in turn, had a substantial impact on intrinsic motivation (β = 0.27, p < 0.01), which subsequently significantly influenced career intentions (β = 0.32, p < 0.01). The study illuminated the complex relationships between grit dimensions, motivation, achievement goals, and career intentions of future PE teachers. SEM validation confirmed grit's direct and indirect influence on goal orientations and motivation, underscoring the importance of incorporating grit-building strategies alongside mastery approach goals in preservice PE programs to enhance resilience, dedication, and long-term career commitment.
Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution.
Groundwater is a critical resource globally, and understanding groundwater processes is vital to ensure sustainable management practices. However, there are many widely held misconceptions and inaccuracies about groundwater, and we currently lack tools to measure groundwater knowledge across large populations and measure how groundwater knowledge relates to management decisions or behaviors. Here, we present a survey instrument, the Groundwater Concept Inventory (GWCI), that has been designed for general audiences to measure groundwater knowledge comparable to that in an introductory geoscience curriculum. The GWCI was developed using ∼1200 responses using an online platform, Amazon Mechanical Turks, to represent a general population. Responses were evaluated using the Rasch model that configures a relationship between person-ability and item-difficulty. We found that the study population displayed similar misconceptions about groundwater compared with previous literature, and that age and education were not strong predictors of GWCI scores. The GWCI can be used by researchers to understand links between knowledge and behavior, and also by other stakeholders to quantify misconceptions about groundwater and target resources for a more informed public.
Groundwater pollution identification is an inverse problem. When solving the inverse problem using regular methods such as simulation-optimization or stochastic statistical approaches, requires repeatedly calling the simulation model for forward calculations, which is a time-consuming process. Currently, the problem is often solved by building a surrogate model for the simulation model. However, the surrogate model is only an intermediate step in regular methods, such as the simulation-optimization method that also require the creation and solution of an optimization model with the minimum objective function, which adds complexity and time to the inversion task and presents an obstacle to achieving fast inversion. In the present study, the extreme gradient boosting (XGBoost) method and the back propagation neural network (BPNN) method were used to directly establish the mapping relationships between the output and input of the simulation model, which could directly obtain the inversion results of the variables to be identified (pollution sources release histories and hydraulic conductivities) based on actual observational data for fast inversion. In addition, to consider the uncertainty of observation data noise, the inversion accuracy of the two machine learning methods was compared, and the method with higher precision was selected for the uncertainty analysis. The results indicated that both the BPNN and XGBoost methods could perform inversion tasks well, with a mean absolute percentage error (MAPE) of 4.15% and 1.39%, respectively. Using the BPNN, with better accuracy for uncertainty analysis, when the maximum probabilistic density value was selected as the inversion result, the MAPE was 2.13%. We obtained the inversion results under different confidence levels and decision makers of groundwater pollution prevention and control can choose different inversion results according to their needs.
Group B streptococcus is a major cause of neonatal disease. Natural history studies have linked maternally transferred anti-group B streptococcus capsular polysaccharide antibodies with protection against infant group B streptococcus disease. Previous studies of capsular polysaccharide antibody concentration in European populations have used maternal (not infant) sera and a non-standardised assay. This study aimed to evaluate anti-capsular polysaccharide IgG concentrations associated with protection against invasive group B streptococcus disease in Finnish infants. In this retrospective case-control study, we used cord sera from the Finnish DIPP study repository, which was obtained between Jan 1, 1995, and Dec 31, 2017. We included infants aged 6 months or younger with group B streptococcus infection (cases) and healthy infants (controls). We enrolled infants with invasive neonatal group B streptococcus (55 cases) and matched controls (229 controls) aged 6 months or younger after identification from Finnish health registers. We measured anti-capsular polysaccharide IgG (serotypes Ia-V) concentration using a standardised immunoassay and we estimated its relationship to disease risk using a Bayesian model. We used the derived risk-concentration curve to predict potential efficacy of six-valent group B streptococcus capsular polysaccharide vaccine (GBS6) based on previously reported immunogenicity data. Most (32 [58%] of 55 cases) group B streptococcus cases were due to serotype III and anti-serotype III streptococcus capsular IgG concentrations were higher in serotype III-matched controls than in cases (p<0·001). 0·120-0·266 μg/mL serotype III-specific IgG was estimated to confer 75-90% risk reduction against serotype III disease. A universal risk-concentration curve, aggregating results across all six serotypes, yielded similar results. Application of this curve to GBS6 immunogenicity data predicted maternal immunisation to be more than 80% efficacious for prevention of infant group B streptococcus disease. Higher neonatal anti-capsular polysaccharide serum IgG concentration at birth correlated with reduced risk of infant group B streptococcus disease in Finland. Based on these results, a maternal group B streptococcus capsular conjugate vaccine currently in development is predicted to be efficacious. Pfizer.
Growing cannabis use among young adults in the United States surpasses research and public understanding, raising health concerns despite potential benefits. Limited research focuses on their knowledge, attitudes, risks, and motivations, especially in states with limited legalization. This study explores cannabis knowledge and attitudes among healthy young adult cannabis consumers to understand their risk and benefit perceptions. Data include a subsample of participants in the Herbal Heart Study, a cohort to examine subclinical cardiovascular risk among healthy young adult (18-35 years old) cannabis consumers and non-consumers. A qualitative thematic analysis of the interviews was performed using a deductive approach driven by the theory of the Health Belief Model to generate categories and codes. Dedoose was used to organize transcripts and coding. A total of 22 young adult cannabis consumers (M age = 25.3, SD = 4.4) were interviewed between May 5, 2021- September 23, 2022. Participants were predominantly female (n = 13) and Hispanic (n = 9) or non-Hispanic Black (n = 7). Five themes were identified: perceived health benefits and risks associated with cannabis use, motivation for cannabis use, knowledge of cannabis, and perceived barriers to cannabis use. Participants discussed knowledge, positive/negative attitudes toward cannabis, and perceived risks/benefits based on personal experience and gathered information. Some showed knowledge deficits, and most wanted more health-related cannabis research. Given the current climate of rising cannabis legalization, availability of novel cannabis products, and societal acceptance, further research and evidence-based cannabis literacy for young adults are essential to keep pace with liberalization trends.
Growing demand for nurses in the sports medicine field has raised awareness in preparing prospective nurses with the basic concepts of sports nursing. Recognizing this need, nursing study program need to continue to evolve and the first step begin with the analysis of the major concepts related to various domains involved in the curriculum. Stakeholders are the most important people to get involved in the curriculum development process. The purpose of this paper is to explore stakeholders' recommendation for establishing a strategy that would best enable the development of sports-focused undergraduate nursing curriculum. We conducted a focus group discussion using a a series of open-ended questions. Nineteen stakeholders participated in this study. Data was analyzed using a thematic analysis. The results of this study found three themes consisting of sports nursing, advanced sports nursing, and sports in the discipline of nursing. On the theme of sports nursing, several sub-themes were obtained, the concept of sports nursing, the basics of sports physiology, adaptation of physiology to sports, psychology and sports injuries, problems in sports, education in sports, pathology in sports, and assistance in sports injuries. Identifying, understanding, and managing Stakeholder involvement in curriculum development is the main driver for creating nursing study programs that focus on sports nursing. Nursing can contribute to sports and create more specific areas of expertise. Sports nursing.
Growing evidence associates air pollution exposure with various psychiatric disorders. However, the importance of early-life (eg, prenatal) air pollution exposure to mental health during youth is poorly understood, and few longitudinal studies have investigated the association of noise pollution with youth mental health. To examine the longitudinal associations of air and noise pollution exposure in pregnancy, childhood, and adolescence with psychotic experiences, depression, and anxiety in youths from ages 13 to 24 years. This cohort study used data from the Avon Longitudinal Study of Parents and Children, an ongoing longitudinal birth cohort founded in 1991 through 1993 in Southwest England, United Kingdom. The cohort includes over 14 000 infants with due dates between April 1, 1991, and December 31, 1992, who were subsequently followed up into adulthood. Data were analyzed October 29, 2021, to March 11, 2024. A novel linkage (completed in 2020) was performed to link high-resolution (100 m2) estimates of nitrogen dioxide (NO2), fine particulate matter under 2.5 μm (PM2.5), and noise pollution to home addresses from pregnancy to 12 years of age. Psychotic experiences, depression, and anxiety were measured at ages 13, 18, and 24 years. Logistic regression models controlled for key individual-, family-, and area-level confounders. This cohort study included 9065 participants who had any mental health data, of whom (with sample size varying by parameter) 51.4% (4657 of 9051) were female, 19.5% (1544 of 7910) reported psychotic experiences, 11.4% (947 of 8344) reported depression, and 9.7% (811 of 8398) reported anxiety. Mean (SD) age at follow-up was 24.5 (0.8) years. After covariate adjustment, IQR increases (0.72 μg/m3) in PM2.5 levels during pregnancy (adjusted odds ratio [AOR], 1.11 [95% CI, 1.04-1.19]; P = .002) and during childhood (AOR, 1.09 [95% CI, 1.00-1.10]; P = .04) were associated with elevated odds for psychotic experiences. Pregnancy PM2.5 exposure was also associated with depression (AOR, 1.10 [95% CI, 1.02-1.18]; P = .01). Higher noise pollution exposure in childhood (AOR, 1.19 [95% CI, 1.03-1.38]; P = .02) and adolescence (AOR, 1.22 [95% CI, 1.02-1.45]; P = .03) was associated with elevated odds for anxiety. In this longitudinal cohort study, early-life air and noise pollution exposure were prospectively associated with 3 common mental health problems from adolescence to young adulthood. There was a degree of specificity in terms of pollutant-timing-outcome associations. Interventions to reduce air and noise pollution exposure (eg, clean air zones) could potentially improve population mental health. Replication using quasi-experimental designs is now needed to shed further light on the underlying causes of these associations.
Growing evidence exists about the candidate factors of childhood cognitive performance, but mainly limited to single-exposure studies. We sought to systematically and simultaneously identify and validate a wide range of potential modifiable factors for childhood cognitive performance. We used data from five waves of data from the China Family Panel Studies (CFPS-2010, 2012, 2014, 2016 and 2018). Our analytical sample was restricted to those children aged 2-5 at baseline with valid exposure information. A total of 80 modifiable factors were identified. Childhood cognitive performance was assessed using vocabulary and mathematics test at wave 5. We used an environment-wide association study (EnWAS) to screen all exposure-outcome associations independently and used the least absolute shrinkage and selection operator (LASSO) variable selection algorithm to identify factors associated with cognitive performance. Multivariable linear model was then used to evaluate causal relationships between identified factors and cognitive performance. Of the 1305 participants included in the study (mean ± SD, 3.5 ± 1.1 years age at baseline, 45.1% girls). Eight factors were retained in the LASSO regression analysis. Six factors across community characteristics (percentage of poverty in the community; percentage of children in the community), household characteristics (family size), child health and behaviors (mobile internet access), parenting behaviors and cognitive enrichment (parental involvement in child' s education), and parental wellbeing (paternal happiness) domains were significantly associated with childhood cognition. Using a three-stage approach, this study validates several actionable targets for improving childhood cognitive performance.