Yoxas/statistical_literacy huggingface dataset:
Many children treated for cancer experience a negative impact on their academic performance; however, most studies of children treated for sarcomas have not investigated academic performance. Our aim was to explore how Swedish children treated for sarcomas perform academically, as well as how they adjust to life afterwards. We compared 167 pediatric sarcoma survivors with 776 matched, non-sibling controls without a history of cancer, in a retrospective cohort study using data from nationwide registries. Primary outcomes were grades at the end of compulsory education, high school eligibility, post-compulsory education (i.e., education after school Year 9), employment, and sickness or activity compensation. Pediatric sarcoma survivors were more likely to be ineligible for high school (odds ratio [OR] 1.76; p = .045) and more likely to fail Swedish (OR 2.12; p = .046), mathematics (OR 2.27; p = .011), and/or physical education (OR 2.24; p = .004), compared with controls. Survivors were less likely to have been employed (OR 0.58; p = .027) and received sickness or activity compensation more often (OR 2.49; p = .008) compared with controls. Pediatric sarcoma survivors have poorer academic performance compared to peers without cancer in multiple school subjects. Survivors seem to catch up during post-compulsory education, but might struggle to find employment.
Many cities with traffic congestion lack accessibility assessments accounting for traffic congestion and equity considerations but have disaggregated georeferenced municipal-level open data on health services, populations, and travel times big data. We convened a multistakeholder intersectoral collaborative group that developed a digital, web-based platform integrating open and big data to derive dynamic spatial-temporal accessibility measurements (DSTAM) for haemodialysis services. We worked with stakeholders and data scientists and considered people's places of residence, service locations, and travel time to the service with the shortest travel time. Additionally, we predicted the impacts of strategically introducing haemodialysis services where they optimise accessibility. Cross-sectional analyses of DSTAM, accounting for traffic congestion, were conducted using a web-based platform. This platform integrated traffic analysis zones, public census and health services datasets, and Google Distance Matrix API travel-time data. Predictive and prescriptive analytics identified optimal locations for new haemodialysis services and estimated improvements. Primary outcomes included the percentage of residents within a 20-min car drive of a haemodialysis service during peak and free-flow traffic congestion. Secondary outcomes focused on optimal locations to maximise accessibility with new services and potential improvements. Findings were disaggregated by sociodemographic characteristics, providing an equity perspective. The study in Cali, Colombia, used geographic and disaggregated sociodemographic data from the adjusted 2018 Colombian census. Predicted travel times were obtained for two weeks in 2020. There were substantial traffic variations. Congestion reduced accessibility, especially among marginalised groups. For 6-12 July, free-flow and peak-traffic accessibility rates were 95.2% and 45.0%, respectively. For 23-29 November, free-flow and peak traffic accessibility rates were 89.1% and 69.7%. The locations where new services would optimise accessibility had slight variation and would notably enhance accessibility and health equity. Establishing haemodialysis services in targeted areas has significant potential benefits. By increasing accessibility, it would enhance urban health and equity. No external or institutional funding was received.
Many college students with TBI rely on external strategies and supports to compensate for persistent memory, organization, and planning deficits that interfere with recalling and executing daily tasks. Practitioners know little, however, about the supports students with TBI choose for this purpose, the reasoning behind their choice, or preferred features of selected supports. The purpose of this study was to explore these issues. We collected and analyzed quantitative and qualitative data from eight college students with TBI for completion of a concurrent triangulation mixed-methods design. Data analysis included evaluation and triangulation of participant demographic information, survey responses about persistent post-injury symptoms, transcripts from semi-structured interviews about cognitive support devices and strategies, and ranking results about specific compensatory tools. Results suggest that college students with TBI prefer high-tech external supports-sometimes with the addition of low-tech, paper supports-to assist them in managing daily tasks. This preference related to features of portability, accessibility, and automatic reminders. An electronic calendar was the most-preferred high-tech support, and a paper checklist was the most-preferred low-tech support. Rehabilitation professionals should consider implementing high-tech supports with preferred characteristics during treatment given the preferences of students with TBI and the consequent likelihood of their continued long-term use following reintegration to community settings.
Many college students, especially first-generation and underrepresented racial/ethnic minority students, desire courses and careers that emphasize helping people and society. Can instructors of introductory science, technology, engineering, and math (STEM) courses promote motivation, performance, and equity in STEM fields by emphasizing the prosocial relevance of course material? We developed, implemented, and evaluated a prosocial utility-value intervention (UVI): A course assignment in which students were asked to reflect on the prosocial value of biology or chemistry course content; our focus was on reducing performance gaps between first-generation and continuing generation college students. In Studies 1a and 1b, we piloted two versions of a prosocial UVI in introductory biology (N = 282) and chemistry classes (N = 1,705) to test whether we could encourage students to write about the prosocial value of course content. In Study 2, we tested a version of the UVI that combines personal and prosocial values, relative to a standard UVI, which emphasizes personal values, using a randomized controlled trial in an introductory chemistry course (N = 2,505), and examined effects on performance and motivation in the course. In Study 3, we tested the prosocial UVI against a standard UVI in an introductory biology course (N = 712). Results suggest that the prosocial UVI may be particularly effective in promoting motivation and performance for first-generation college students, especially those who are more confident that they can perform well in the class, reflecting a classic expectancy-value interaction. Mediation analyses suggest that this intervention worked by promoting interest in chemistry. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Many commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, called Profile-Wise Analysis (PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self-contained document that can be studied together with relatively straightforward Julia code provided on GitHub . Working with simple mathematical models allows the PWA workflow prediction intervals to be compared with gold standard full likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly-encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.
Many countries are becoming increasingly culturally and linguistically diverse due to globalisation and migration. The global shortage of nurses and the consequent migration of nursing professionals is leading to increasing cultural and linguistic diversity in health care and nursing education. Nurse educators play a significant role in supporting nursing students' competence and working life readiness. To describe nurse educators' experiences and perceptions regarding competence and competence development of CALD nursing students. The study was conducted using a descriptive qualitative approach utilising individual thematic interviews for data collection. A purposive sample consisting of 20 volunteer nurse educators from five higher education institutions was used for the purpose. Data was collected in semi-structured interviews based on a literature review of previous studies. The data were analysed using inductive content analysis. The COnsolidated criteria for REporting Qualitative research (COREQ) criteria were used in the reporting of this study. According to the content analysis, three main categories describing aspects related to the competence and competence development of CALD nursing students were identified: 1) educator's competence to support student's learning process and competence development, 2) development of supportive structures and safe learning environments, and 3) student-specific competence and competence development. The educators emphasised that setting clear goals, providing encouragement, having an open discussion, and student-centered approach to support their learning process and create a safe learning environment. Supporting the learning process of CALD nursing students and developing safe learning environments were considered essential for the development of competence. These areas must be supported by developing educators' pedagogical and cultural competence and providing sufficient resources for both students and educators. In addition, the curriculum must be designed to support these aspects.
Many countries' curriculum reforms focus on developing the next generations' competencies of self-directed learning (SDL) to address rapid social changes and sustainable environmental development. Taiwan's curriculum reform corresponds with the global trend in education. The latest curriculum reform, which proposed a 12-year basic education, was implemented in 2018 and included SDL explicitly in its guidelines. The reformed curriculum guidelines have been followed for over 3 years. Thus, it is necessary to conduct a large-scale survey to examine its impact on Taiwanese students. However, existing research instruments help provide a generalized analysis of SDL and have yet to be designed specifically for SDL of mathematics. Therefore, we developed a mathematics SDL scale (MSDLS) and examined its reliability and validity in this study. Subsequently, MSDLS was utilized to investigate Taiwanese students' SDL of mathematics. The MSDLS consists of four sub-scales with 50 items. It has acceptable reliability, validity, and measurement invariance across gender and grade groups. The MSDLS was administered online to 5,575 junior high school students, and 5,456 valid responses were collected. The findings highlight the gender and grade differences in SDL of mathematics. Male students are higher than female students in many factors. It is noted that the SDL in mathematics does not increase with grade. In sum, the MSDLS is a helpful instrument for examining secondary school students' SDL of mathematics.
Many defects occur during the mass production of precision electrical components. To control and manage them, process variables (PVs), such as the temperature, pressure, flow rate, and liquid level, are measured and time-series data analyzed. However, identification of point of defects is difficult as any operation can cause defects and multiple equipment units are used in parallel for some operations. This study considers the combination of unfavourable conditions between operations to predict the defect rate (DR) of products. A dataset measured in an actual mass-production process for precision electrical components is analysed to predict the DR of the products. Data analysis is performed on a dataset generated from an actual mass-production process for precision electrical components, and machine learning models. are constructed using ensemble learning methods, such as random forests, the gradient boosting decision tree, XGBoost, and LightGBM. Conventional univariate analyses only show a maximum correlation coefficient of 0.17 with a DR and process variables (PVs). In this study, we improved the correlation coefficient to 0.73 using a multivariate analysis, including the data of PVs that are not considered important in the process, and appropriately transformed PVs based on the domain knowledge of the process. Furthermore, PVs that were closely related to the DR could be diagnosed based on the feature importance of the constructed machine-learning models. This study confirms the importance of using domain knowledge to improve the prediction ability of machine learning models and the interpretation of constructed models.
Many developing countries are using various forms of social protection strategies to combat food insecurity. The Productive Safety Net Program (PSNP) is a major policy initiative launched by the Ethiopian government and partners to help millions of chronically food insecured rural people. The main objective of this research is to assess the contributions of PSNP to households' food security and asset building in the selected drought prone woreda (district) of northeast Ethiopia. The research applied a cross sectional research design and a mixed research approach. The data was collected from 142 randomly selected household heads of three selected Kebeles (a small administrative unit) clustered into three agro-climatic zones of the woreda, using key informant interviews, focus group discussions, and document reviews. The methods used in the analysis were descriptive statistics (percentage and mean) and inferential statistics (chi-square, t-test, and binary logistic regression model). Twelve variables were selected to explain factors affecting the food security level of the PSNP beneficiary households. Out of these nine variables were identified as significant. The result revealed that the sample households that joined the PSNP have improved in food consumption status, livestock holding, housing conditions, and agricultural input utilization. The binary regression model depicted that household age, household size, education, farmland size, agricultural inputs and livestock as the main determinants. The result also revealed a lack of community participation in the decision-making, payment delays, a low level of gender awareness, and poor planning processes as the main challenges. Therefore, improving the management, revising the strategies particularly; the tools, guidelines and manuals of the PSNP is essential for best performance to address the food security challenges in general and the beneficiaries in particular.
Many diabetes mellitus (DM) patients suffer from multimorbidity. Understanding the DM multimorbidity network should be given priority. The purpose of this study is characterize the DM multimorbidity network in people over 50 years. Data on 75 non-communicable diseases (NCDs) were extracted from electronic medical records of 309,843 hospitalized patients older than 50 years who had at least one NCD. The association rules analysis was used as a novel classification method and combined with the Chi-square tests to identify associations between NCDs and DM. A total of 12 NCDs were closely related to DM, {cholelithiasis, DM} was an unexpected combination. {dyslipidemia, DM} and {gout, DM} had the largest lift in the male and female groups, respectively. The negative related group included 7 NCDs. There were 9 NCDs included in the strong association rules. Most combinations were different by age and sex. In males, the strongest rule was {peripheral vascular disease (PVD), dyslipidemia, DM}, while {hypertension, dyslipidemia, chronic liver disease (CLD), DM} was the strongest in females. In patients younger than 70 years, hypertension, CLD, and dyslipidemia were the most dominant NCDs in the DM multimorbidity network. In patients 70 years or older, chronic kidney disease (CKD), CVD, CHD, and heart disease (HD) frequently co-occurred with DM. Future primary healthcare policies for DM should be formulated based on age and sex. In patients younger than 70 years, more attention to hypertension, CLD, and dyslipidemia is required, while attention to CKD, CVD, CHD and HD is needed in patients older than 70 years.
Many diagnostic tests are currently being performed around the world to detect SARS-CoV-2 infection. Positive and negative test results are not one hundred percent accurate, but have far-reaching consequences. There are false positives (test positive, uninfected) and false negatives (test negative, infected). A positive/negative result does not necessarily mean that the test subject is actually infected/non-infected. This article has two objectives: 1. to explain the most important characteristics of diagnostic tests with binary outcome 2. to point out problems and phenomena of interpretation of diagnostic tests, on the basis of different scenarios. Presentation of the basic concepts of the quality of a diagnostic test (sensitivity, specificity) and pre-test probability (prevalence of test group). Calculation (including formulas) of further important quantities. In the basic scenario, sensitivity is 100%, specificity 98.8%, and pre-test probability of 1.0% (10 infected persons per 1,000 tested). For 1,000 diagnostic tests, the statistical mean is 22 positive cases, 10 of which are true-positive. The positive predictive probability is 45.7%. The prevalence calculated from this (22/1,000 tests) overestimates the actual prevalence (10/1,000 tests) by a factor of 2.2. All cases with a negative test outcome are true negative. The prevalence has a strong influence on the positive and negative predictive value. This phenomenon occurs even with otherwise very good test values of sensitivity and specificity. At a prevalence of only 5 infected persons per 10,000 (0.05%), the positive predictive probability drops to 4.0%. Lower specificity amplifies this effect, especially with small numbers of infected persons. If the sensitivity or specificity is below 100%, diagnostic tests are always error-prone. If the prevalence of infected persons is low, a large number of false positive results are to be expected - even if the test is of good quality with a high sensitivity and especially a high specificity. This is accompanied by low positive predictive values, i. e. positive tested persons are not infected. A false positive test result in the first test can be clarified by carrying out a second test.
Many early learners need individualized support when regular teaching is not readily available. Here we present results of a progressive digital game that was played at home rather than under the supervision of the teacher. "NumberBeads" was designed to help low attaining learners, but also typical early learners. The game required learners to construct objects-sets and digits-to match a target object, and was played at home using an online platform. The participants were first-graders (n=140) enrolled in 10 classrooms randomly assigned to two groups. One group played NumberBeads, requiring students to construct solutions in a microworld of sets and digits. Another group played a similar game, NumberChoice, which contained the same elements, sets and digits, but used multiple-choice questions with right/wrong feedback. Results showed that learners using both games were able to complete the game unsupervised at home, but there was greater improvement with NumberBeads especially with learners identified as low attaining. Overall these findings support the feasibility of boosting early numeracy at home using a game designed to help basic numerical competence that requires no teacher supervision, and very little guidance by parents or carers.
Many education interventions in emergency nursing are aimed at changing nurse behaviours. This scoping review describes and synthesises the published research education interventions and emergency nurses' clinical practice behaviours. Arksey and O'Malley's methodological framework guided this review, which is reported according to Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). CINAHL, MEDLINE complete, ERIC, and Psycinfo were searched on 3 August 2023. Two pairs of researchers independently conducted all screening. Synthesis was guided by the Behaviour Change Wheel and Bloom's Taxonomy of Educational Objectives. Twenty-five studies were included. Educational interventions had largely positive effects on emergency nurses' clinical practice behaviours. Ten different interventions were identified, the most common was education sessions (n = 24). Seven studies reported underpinning theoretical frameworks. Of the essential elements of behaviour change, seven interventions addressed capability, four addressed motivation and one addressed opportunity. Mapping against Bloom's taxonomy, thirteen studies addressed analysis, eleven studies addressed synthesis and two studies addressed evaluation. Few studies addressed elements of behaviour change theory or targeted cognitive domains. Future studies should focus on controlled designs, and more rigorous reporting of the education intervention(s) tested, and theoretical underpinning for intervention(s) selected.