Yoxas/statistical_literacy huggingface dataset:
Data plots are widely used in science, journalism and politics, since they efficiently allow to depict a large amount of information. Graphicacy, the ability to understand graphs, has thus become a fundamental cultural skill comparable to literacy or numeracy. Here, we introduce a measure of intuitive graphicacy that assesses the perceptual ability to detect a trend in noisy scatterplots ("does this graph go up or down?"). In 3943 educated participants, responses vary as a sigmoid function of the t-value that a statistician would compute to detect a significant trend. We find a minimum level of core intuitive graphicacy even in unschooled participants living in remote Namibian villages (N = 87) and 6-year-old 1st-graders who never read a graph (N = 27). The sigmoid slope that we propose as a proxy of intuitive graphicacy increases with education and tightly correlates with statistical and mathematical knowledge, showing that experience contributes to refining graphical intuitions. Our tool, publicly available online, allows to quickly evaluate and formally quantify a perceptual building block of graphicacy.
Data visualizations have proliferated throughout the COVID-19 pandemic to communicate information about the crisis and influence policy development and individual decision-making. In invoking exponential growth, mathematical modelling, statistical analysis, and the like, these data visualizations invite opportunities for mathematics teaching and learning. Yet data visualizations are social texts, authored from specific points of view, that narrate particular, and often consequential, stories. Their fundamental reliance on quantification and mathematics cements their social positioning as supposedly objective, reliable, and neutral. The reading of any data visualization demands unpacking the role of mathematics, including how data and variables have been formatted and how relationships are framed to narrate stories from particular points of view. We present an approach to a critical reading of data visualizations for the context of mathematics education that draws on three interrelated concepts: mathematical formatting (what gets quantified, measured, and how), framing (how variables are related and through what kind of data visualization), and narrating (which stories the data visualization tells, its potential impacts and limits). This approach to reading data visualisations includes a process of reimagining through reformatting, reframing and renarrating. We illustrate this approach and these three concepts using data visualizations published in the New York Times in 2020 about COVID-19. We offer a set of possible questions to guide a critical reading of data visualizations, beyond this set of examples.
Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.
Day-of-surgery cancellations impede healthcare access and contribute to inequities in pediatric healthcare. Socially disadvantaged families have many risk factors for surgical cancellation, including low health literacy, transportation barriers, and childcare constraints. These social determinants of health are captured by the Childhood Opportunity Index (COI) 2.0, a national quantification of neighborhood-level characteristics that contribute to a child's vulnerability to adversity. We studied the association of neighborhood opportunity with pediatric day-of-surgery cancellations. We conducted a retrospective cohort study of children younger than 18 years of age scheduled for ambulatory surgery at a tertiary pediatric hospital between 2017 and 2022. We geocoded the primary address to determine COI 2.0 neighborhood opportunity. We used log-binomial regression to estimate the relative risk of day-of-surgery cancellation comparing different levels of neighborhood opportunity. We also estimated the relative risk of cancellations associated with race and ethnicity, by neighborhood opportunity. Overall, the incidence of day-of-surgery cancellation was 3.8%. The incidence of cancellation was lowest in children residing in very high opportunity neighborhoods and highest in children residing in very low opportunity neighborhoods (2.4% vs 5.7%, p<0.001). The adjusted relative risk of day-of-surgery cancellation in very low opportunity neighborhoods compared to very high opportunity neighborhoods was 2.24 (95%CI: 2.05-2.44, p<0.001). We found statistical evidence of an interaction of COI with race and ethnicity. In very low opportunity neighborhoods, Black children had 1.48 times greater risk of day-of-surgery cancellation than White children (95%CI: 1.35-1.63, p<0.001). Likewise, in very high opportunity neighborhoods, Black children had 2.17 times greater risk of cancellation (95%CI: 1.75-2.69, p<0.001). We found a strong relationship between pediatric day-of-surgery cancellation and neighborhood opportunity. Black children at every level of opportunity had the highest risk of cancellation, suggesting that there are additional factors that render them more vulnerable to neighborhood disadvantage.
Daytime sleepiness is an important health problem. However, the dimensionality of the Epworth Sleepiness Scale (ESS) in older adults remains unclear. This study aimed to determine the prevalence of ESS-defined excessive daytime sleepiness in older adults. Furthermore, the dimensionality of ESS and its respective correlates were also compared. This is a community-based survey in which community-dwelling older adults aged ≥ 65 years participated. Excessive daytime sleepiness was assessed using the ESS and was defined as an ESS score of > 10. Exploratory factor analysis was performed to identify the ESS factors. Multiple logistic regression analysis was used to examine the independent correlates of the ESS-defined and factor-specific correlates of excessive daytime sleepiness. In total, 3978 older adults participated in this study. The mean age was 76.6 ± 6.7 years, with 53.8% ≥ 75 years, and 57.1% were female. The prevalence of ESS-defined excessive daytime sleepiness was 16.0%. An exploratory factor analysis revealed two factors in the ESS, which were designated as 'passive' and 'active' according to the soporific levels of ESS items loaded in each factor. Multiple logistic regression showed that male, illiteracy, depression, disability, short sleep duration and no exposure to hypnotics were risk indicators for ESS-defined excessive daytime sleepiness. However, the correlates for passive and active factor-defined excessive daytime sleepiness differ in pattern, especially in variables related to education, exercise, mental health, and sleep. The prevalence of ESS-defined excessive daytime sleepiness is high, and its correlates vary among older adults. This study also suggests a dual ESS structure in community-dwelling older adults.
Dealing with a diverse population is one of the most challenging topics in medicine, with consequences for health disparities as evidenced by poorer health of marginalised groups. An urgent need exists to build a diversity-responsive curriculum in medical education. At the core of such a curriculum are experiential learning and a focus on self-awareness and reflexivity via small group trainings. This Show and Tell paper describes the development and qualitative evaluation of such a training, that was implemented at the University of Antwerp in Belgium, presenting answers to some of the gaps and challenges described in the literature. This training is guided by three visual reflection tools - the kaleidoscope, the iceberg and the communication compass - to inspire learners on how to deal with the diversity of their future patients. The content, method, and educational aim of this hands-on training are described. We discuss some of the challenges the educational methods pose on reflexivity and awareness, looking at the lessons learned based on participants' feedback. While the visual reflection tools offer a dynamic space to broaden the way we look at patients, it remains imperative to create a safe environment for discussing tensions, sharing difficult topics and being aware of different voices. Taking time (space for discussion, small groups, training of faculty) and allowing for continuous reflection of the educators are key in the development of diversity-responsive education.
Dealing with overconsumption and the waste crisis requires consumers to make a drastic lifestyle change, adopting more circular consumption patterns that support the prevention of waste generation. The goal of our study was to explore the range of precycling behaviors aimed at preventing waste generation in households. We tested the hypotheses of the Motivation Opportunity Ability framework using a nationally representative survey of consumers residing in Finland (n = 1,000). Respondents reported a high level of motivation, moderate level of opportunity and ability and engaged in precycling occasionally. Using exploratory factor analysis, we identified three types of precycling behaviors: long-term planning, resourceful behavior, and reuse for circularity. Multiple linear regression indicated that motivation (frugality and minimalism), opportunity (distance to circular economy services), ability (skills related to the circular economy), and precycling were associated. These findings suggest that policymakers need to focus on increasing consumers' precycling skills and improving the accessibility and availability of precycling services.
Dealing with uncertainty and different degrees of frequency and probability is critical in many everyday activities. However, relevant information does not always come in the form of numerical estimates or direct experiences, but is instead obtained through qualitative, rather vague verbal terms (e.g., "the virus often causes coughing" or "the train is likely to be delayed"). Investigating how people interpret and utilize different natural language expressions of frequency and probability is therefore crucial to understand reasoning and behavior in real-world situations. While there is considerable work exploring how adults understand everyday uncertainty phrases, very little is known about how children interpret them and how their understanding develops with age. We take a developmental and computational perspective to address this issue and examine how 4- to 14-year-old children and adults interpret different terms. Each participant provided numerical estimates for 14 expressions, comprising both frequency and probability phrases. In total we obtained 2856 quantitative judgments, including 2240 judgments from children. Our findings demonstrate that adult-like intuitions about the interpretation of everyday uncertainty terms emerge fairly early in development, with the quantitative estimates of children converging to those of adults from around 9 years on. We also demonstrate how the vagueness of verbal terms can be represented through probability distributions, which provides additional leverage for tracking developmental shifts through cognitive modeling techniques. Taken together, our findings provide key insights into the developmental trajectories underlying the understanding of everyday uncertainty terms, and open up novel methodological pathways to formally model the vagueness of probability and frequency phrases, which are abundant in our everyday life and activities.
Death literacy is defined as a set of knowledge and skills that make it possible to gain access to, understand, and act upon end-of-life and death care options. This study was conducted to test the validity and reliability of the 29-item original version of the Death Literacy Index (DLI) in Turkish society. The scale was applied on a sample of 436 Turkish adults determined using the snowball sampling method. Explanatory and confirmatory factor analyses were performed. The scale consisted of 6 subscales. The test-retest analysis of this study showed a strong and highly significant positive correlation between overall DLI (.98) and its subscales (.73-.98). The total Cronbach's alpha reliability coefficient of DLI was found to be .90. It was observed that the Turkish version of DLI was a valid and reliable tool and suitable for use in Turkish society.
Deaths from COVID-19 likely exceeded official statistics in Zambia because of limited testing and incomplete death registration. We describe a sentinel COVID-19 mortality surveillance system in Lusaka, Zambia. We analyzed surveillance data on deceased persons of all ages undergoing verbal autopsy (VA) and COVID-19 testing at the University Teaching Hospital (UTH) mortuary in Lusaka, Zambia, from April 2020 through August 2021. VA was done by surveillance officers for community deaths and in-patient deaths that occurred <48 hours after admission. A standardized questionnaire about the circumstances proximal to death was used, with a probable cause of death assigned by a validated computer algorithm. Nasopharyngeal specimens from deceased persons were tested for COVID-19 using polymerase chain reaction and rapid diagnostic tests. We analyzed the cause of death by COVID-19 test results. Of 12,919 deceased persons at UTH mortuary during the study period, 5,555 (43.0%) had a VA and COVID-19 test postmortem, of which 79.7% were community deaths. Overall, 278 (5.0%) deceased persons tested COVID-19 positive; 7.1% during waves versus 1.4% during nonwave periods. Most (72.3%) deceased persons testing COVID-19 positive reportedly had fever, cough, and/or dyspnea and most (73.5%) reportedly had an antemortem COVID-19 test. Common causes of death for those testing COVID-19 positive included acute cardiac disease (18.3%), respiratory tract infections (16.5%), other types of cardiac diseases (12.9%), and stroke (7.2%). A notable portion of deceased persons at a sentinel site in Lusaka tested COVID-19 positive during waves, supporting the notion that deaths from COVID-19 might have been undercounted in Zambia. Many had displayed classic COVID-19 symptoms and been tested before death yet nevertheless died in the community, potentially indicating strained medical services during waves. The high proportion of cardiovascular diseases deaths might reflect the hypercoagulable state during severe COVID-19. Early supportive treatment and availability of antivirals might lessen future mortality.
Deaths of Despair (DoD) are socially patterned causes of death encompassing drug and alcohol misuse and suicide. DoDs are strongly associated with socioeconomic disadvantage. England has high levels of inequalities, so we hypothesised the existence of marked geographical variations in DoD. We aimed to yield new knowledge on the spatial distribution of DoD, and area-level socioeconomic factors that predict DoD risk in England. This observational study was conducted using ICD-10 coded deaths for 307 local authorities in England during 2019-21. Deaths were grouped to non-overlapping categories of drug-related death, alcohol-specific death, and suicide. The mean contributions of each of these causes to the total number of DoD in England were calculated with Poisson exact confidence intervals. Standardised mortality ratios (SMRs) for DoD were generated for each local authority population. A multivariable regression model for DoD risk was developed using 25 socioeconomic variables. An estimated 46 200 people lost their lives due to DoD between Jan 1, 2019, and Dec 31, 2021. Regional SMRs ranged from 57·4 (SD 16·1) in London to 144·1 (SD 26·8) in the northeast of England (p<0·0001). Alcohol-specific deaths were the largest contributor of DoD, accounting for 44·1% of DoD (95% CI 43·5-44·8), followed by drug-related death (28·1%, 27·7-28·6) and suicide (27·7%, 27·2-28·2). Living in the North, living alone, White British ethnicity, lower inward migration, economic inactivity, income deprivation in older people, employment in elementary occupations, unemployment, and education deprivation in adults were significantly associated with higher DoD rates in England. DoD in England are spatially patterned, with northern regions experiencing a considerably higher burden of mortality from these avoidable causes. A key limitation is ecological bias. This study provides novel insights into area-level risk factors for DoD in England. National Institute for Health and Care Research (NIHR) Applied Research Collaboration Greater Manchester (ARC-GM).
Debates about the modifiability of cognitive ability have been largely resolved by reports of successful 'thinking skills' interventions. However, such interventions are very diverse and generalization of effects relatively little explored. This study investigated whether a thinking skills intervention involving collaborative interactive dialogue could lead not only to gains in measured verbal cognitive ability but also generalization to non-verbal and quantitative reasoning ability. Randomly selected intervention children were aged 10 at pre-test (N=105, four classes/schools). Controls followed a normal curriculum (N=72, three classes/two schools). Intervention children engaged in collaborative enquiry for 1 hour per week over 16 months. The control group received normal classroom experiences. The Cognitive Abilities Test was administered before and after the intervention. Intervention pupils showed significant standardized gains in verbal and also in non-verbal and quantitative aspects of reasoning, consistent across intervention schools. Boys and girls made significant gains. The highest quartile of pre-test ability showed the smallest gains. Controls did not gain in any aspect. Philosophical enquiry involving interactive dialogue led not only to significant gains in measured verbal cognitive ability but also generalization to non-verbal and quantitative reasoning ability, consistent across schools and largely irrespective of pupil gender and ability. The effect sizes from this large-scale field trial in one local authority exceeded those reported in the literature. Implications for theory building, replicability and sustainability are addressed.
Decades of research have established that spatial skills correlate with numerical skills. However, because both spatial and numerical skills are multidimensional, we sought to determine how specific spatial skills relate to specific numeracy skills. We used a cohort-sequential design, assessing a large diverse sample of students (N = 612, initially in pre-kindergarten [pre-K]-3rd grade, 4-9 years of age) at four time points spanning 2 years. We examined how initial levels of five spatial skills (visuospatial working memory [VSWM], mental transformation, mental rotation, proportional reasoning, and analog magnitude system [AMS] acuity) related to initial levels and growth rates in exact and approximate calculation skills, and we further investigated number line estimation as a potential mediator. We found unique patterns of relations between spatial skills and numeracy. Initial levels of mental rotation, proportional reasoning, and AMS acuity related to initial levels of exact calculation skill; initial levels of AMS acuity related to initial levels of approximate calculation; and initial levels of proportional reasoning related to initial levels of number line estimation. VSWM and mental transformation did not relate to numeracy skills after controlling for other spatial skills. Initial levels of number line estimation related to both exact and approximate calculation after controlling for spatial skills. Notably, neither spatial skills nor number line estimation predicted growth in exact or approximate calculation skills. These results indicate that there is specificity in the time-invariant relations between spatial skills and numeracy, and they suggest that researchers and educators should treat spatial skills and numeracy as multidimensional constructs with complex and unique interrelations.
Decades of research indicate that some of the epistemic practices that support scientific enquiry emerge as part of intuitive reasoning in early childhood. Here, we ask whether adults and young children can use intuitive statistical reasoning and metacognitive strategies to estimate how much information they might need to solve different discrimination problems, suggesting that they have some of the foundations for 'intuitive power analyses'. Across five experiments, both adults (N = 290) and children (N = 48, 6-8 years) were able to precisely represent the relative difficulty of discriminating populations and recognized that larger samples were required for populations with greater overlap. Participants were sensitive to the cost of sampling, as well as the perceptual nature of the stimuli. These findings indicate that both young children and adults metacognitively represent their own ability to make discriminations even in the absence of data, and can use this to guide efficient and effective exploration.