Yoxas/statistical_literacy huggingface dataset:
Critical thinking is a common and important learning outcome in college curricula. Case-based and problem-based learning can be used to assess and foster critical thinking skills. HNF 250-Contemporary Issues in Human Nutrition is a critical thinking course developed during the redesign of a nutritional sciences major program. Course assignments were designed to assess the course and nutritional sciences major learning outcomes. The nutrition and health claim assignment is scaffolded across the academic semester as three assignments: 1) bibliography assignment; 2) poster presentation; and 3) paper. Course lectures and materials have been designed to prepare students for completion of each assignment. The assignments have been modified over time based on classroom observations and student performance. In 2021, the course learning outcomes were examined by assessing several assignments including the nutrition and health claim poster and paper. Course learning outcome benchmarks using these assessments generally included 80% of students achieving an 80% for each criterion. Results revealed that students were not meeting most of these assessment benchmarks during the 2021 iteration, although benchmark data from other course assessments were more satisfactory. It is possible that the transition from a virtual to an in-person format negatively influenced student performance on these course learning outcomes. This course and the nutrition and health claim assignment example can provide a course design and learning outcome assessment framework for other higher education critical thinking courses.NEW & NOTEWORTHY This paper describes how a scaffolded nutrition and health claim assignment is used to teach critical thinking skills among nutritional sciences students and examine the program's learning outcomes. Further, this course example is to serve as an model for STEM majors on how to incorporate case-based and problem-based learning strategies into an undergraduate course.
Critical thinking is a key element of complex problem-solving that facilitates health professionals' clinical practice and professional behavior. Despite the importance of critical thinking, the measurement of critical thinking skills and its associations with academic performance remains a challenge. The aim of this study was to determine the associations between critical thinking and academic performance among undergraduate dental hygiene students. All 63 third- and fourth-year baccalaureate degree dental hygiene students (Class of 2018 and Class of 2017, respectively) in The Ohio State University Dental Hygiene Program were invited to participate in this quantitative, cross-sectional research study. Critical thinking was measured with the California Critical Thinking Disposition Inventory (CCTDI) and the Health Sciences Reasoning Test with Numeracy (HSRT-N). A total of 45 students completed the two tests, for an overall response rate of 71.4%. For the Class of 2018, the response rate was 90.6% (n=29), and for the Class of 2017, the response rate was 51.6% (n=16). The results showed the CCTDI and HSRT-N scores were higher for the Class of 2017 than the Class of 2018, suggesting that critical thinking skills were being developed across the course of the curriculum. The CCTDI was not significantly correlated with or a predictor of academic performance. The HSRT-N was more correlated with clinical performance than was the CCTDI, and HSRT-N components were found to be predictors of clinical performance. Continued attention is needed in the development and measurement of critical thinking skills to improve the academic and clinical performance of undergraduate dental hygiene students.
Critical thinking is considered as the essential set of skills for medical practice, particularly during emergencies. However, there is lack of conceptual clarity around it and it was not explicitly included in the undergraduate medical curriculum in India. The present study has been planned to assess the critical thinking disposition and clinical reasoning skills among medical undergraduate students in a medical college in West Bengal, India. The perceived definition and attributes of critical thinking in medical education will be explored and the contexts where application of critical thinking skills may be crucial for medical practice will be identified. The content validity index, test-retest agreement; internal consistency and construct validity of the Critical Thinking Disposition Assessment Questionnaire (CTDAQ) will be assessed through step-by-step procedure. CTDAQ and the case-based objective-type questions for the clinical reasoning skills will be applied to around 200 medical undergraduate students. Their perception and experience on critical thinking in medical education will be assessed with structured open-ended questions. In-depth interviews with medical teachers of the second and third phases of undergraduate medical curriculum will be conducted to assess their perception and experiences on critical thinking. The quantitative analysis will be conducted with MS excel and R software using the relevant packages. The qualitative data will be transcribed and translated in English, close to the participants' verbatim. The thematic analysis will be conducted with inductive coding and memoing. The study will be conducted maintaining ethical standards for epidemiological studies. The present study, one of the first a few studies in India, will help to meet the conceptual gap in understanding the attributes of critical thinking, its association with clinical reasoning and the contexts of preferred application in medical practice.
Critiques of coefficient alpha as an estimate of scale reliability are widespread in the literature. However, the continuous overuse of this statistic in mathematics education research suggests a disconnection between theory and practice. As such, this article argues, in a non-technical way, for the limited usefulness of coefficient alpha, its overuse, and its alternatives in estimating scale reliability. Coefficient alpha gives information only about the degree of the interrelatedness of a set of items that measures a construct. Contrary to the widely circulated misconceptions in mathematics education research, a high coefficient alpha value does not mean the instrument is reliable, and it does not imply the instrument measures a single construct. Coefficient alpha can only be dependable as an estimate of reliability under verifiable and restrictive conditions. I expose these conditions and present steps for their verification in empirical studies. I discuss some alternatives to coefficient alpha with references to non-technical articles where worked examples and programming codes are available. I hope this exposition will influence the practices of mathematics education researchers regarding estimation of scale reliability.
Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (Meher), the Ethiopian Crop Type 2020 (EthCT2020) dataset compiled from multiple sources provides 2,793 harmonized, quality-controlled, and georeferenced in-situ samples on annual crop types (7 crop groups; 22 crop classes) at smallholder field level across the complex and highly fragmented agricultural landscape of Ethiopia. The focus was on rainfed, wheat-based farming systems. A nationwide ground data collection campaign (GDCC; Source 1) was designed using a stratification approach based on wheat crop calendar information, and 1,263 in-situ data samples were collected in selected sampling regions. This in-situ data pool was enriched with 1,530 wheat samples extracted from a) the Wheat Rust Toolbox (WRTB; Source 2; 734 samples), a database for wheat disease surveillance data [1] and b) an inhouse farm household survey database (FHSD; Source 3; 796 samples). Obtained field data was labelled according to the Joint Experiment for Crop Assessment and Monitoring (JECAM) guidelines for cropland and crop type definition and field data collection [2] and the FAO Indicative Crop Classification [3]. The EthCT2020 dataset underwent extensive processing including data harmonization, mixed pixel assessment through visual interpretation using 5 m Planet satellite image composites, and quality-control using Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is unique in terms of crop diversity, pixel purity, and spatial accuracy while targeting a countrywide distribution. It is representative of Ethiopia's complex and highly fragmented agricultural landscape and can be useful for developing new machine learning algorithms for land use land cover mapping, crop type mapping, agricultural monitoring, and yield forecasting in smallholder cropping systems. The dataset can also serve as a baseline input parameter for crop models, climate models, and crop disease and pest forecasting systems.
Cross-cultural research with patient-reported outcomes measures (PROMs) assumes that the PROM in the target language will measure the same construct in the same way as the PROM in the source language. Yet translation methods are rarely used to qualitatively maximise construct equivalence or to describe the intents of each item to support common understanding within translation teams. This study aimed to systematically investigate the utility of the Translation Integrity Procedure (TIP), in particular the use of item intent descriptions, to maximise construct equivalence during the translation process, and to demonstrate how documented data from the TIP contributes evidence to a validity argument for construct equivalence between translated and source language PROMs. Analysis of secondary data was conducted on routinely collected data in TIP Management Grids of translations (n = 9) of the Health Literacy Questionnaire (HLQ) that took place between August 2014 and August 2015: Arabic, Czech, French (Canada), French (France), Hindi, Indonesian, Slovak, Somali and Spanish (Argentina). Two researchers initially independently deductively coded the data to nine common types of translation errors. Round two of coding included an identified 10th code. Coded data were compared for discrepancies, and checked when needed with a third researcher for final code allocation. Across the nine translations, 259 changes were made to provisional forward translations and were coded into 10 types of errors. Most frequently coded errors were Complex word or phrase (n = 99), Semantic (n = 54) and Grammar (n = 27). Errors coded least frequently were Cultural errors (n = 7) and Printed errors (n = 5). To advance PROM validation practice, this study investigated a documented translation method that includes the careful specification of descriptions of item intents. Assumptions that translated PROMs have construct equivalence between linguistic contexts can be incorrect due to errors in translation. Of particular concern was the use of high level complex words by translators, which, if undetected, could cause flawed interpretation of data from people with low literacy. Item intent descriptions can support translations to maximise construct equivalence, and documented translation data can contribute evidence to justify score interpretation and use of translated PROMS in new linguistic contexts.
Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.
Cross-sectional definitions of successful cognitive aging have been widely utilized, but longitudinal measurements can identify people who do not decline. We performed this study to contrast maintenance with declining trajectories, including clinical conversion. We included baseline cognitively unimpaired Alzheimer's Disease Neuroimaging Initiative participants with 3 or more cognitive testing sessions (n = 539, follow-up 6.1 ± 3.5 years) and calculated slopes of an episodic memory composite (MEM) to classify them into two groups: maintainers (slope ≥ 0) and decliners (slope < 0). Within decliners, we examined a subgroup of individuals who became clinically impaired during follow-up. These groups were compared on baseline characteristics and cognitive performance, as well as both cross-sectional and longitudinal Alzheimer disease (AD) biomarker measures (beta-amyloid [Aβ], tau, and hippocampal volume). Forty-one percent (n = 221) of the cohort were MEM maintainers, and 33% (n = 105) of decliners converted to clinical impairment during follow-up. Compared to those with superior baseline scores, maintainers had lower education and were more likely to be male. Maintainers and decliners did not differ on baseline MEM scores, but maintainers did have higher non-MEM cognitive scores. Maintainers had lower baseline global Aβ, lower tau pathology, and larger hippocampal volumes than decliners, even after removing converters. There were no differences in rates of change of any AD biomarkers between any cognitive trajectory groups except for a higher rate of hippocampal atrophy in clinical converters compared to maintainers. Using longitudinal data to define cognitive trajectory groups reduces education and sex bias and reveals the prognostic importance of early onset of accumulation of AD pathology. ANN NEUROL 2024.
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.
Cross-species differences in drug transport and metabolism are linked to poor translation of preclinical pharmacokinetic and toxicology data to humans, often resulting in the failure of new chemical entities (NCEs) during clinical drug development. Specifically, inaccurate prediction of renal clearance and renal accumulation of NCEs due to differential abundance of enzymes and transporters in kidneys can lead to differences in pharmacokinetics and toxicity between experimental animals and humans. We carried out liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protein quantification of 78 membrane drug-metabolizing enzymes and transporters (DMETs) in the kidney membrane fractions of humans, rats, and mice for characterization of cross-species and sex-dependent differences. In general, majority of DMET proteins were higher in rodents than in humans. Significant cross-species differences were observed in 30 out of 33 membrane DMET proteins quantified in all three species. Although no significant sex-dependent differences were observed in humans, the abundance of 28 and 46 membrane proteins showed significant sex dependence in rats and mice, respectively. These cross-species and sex-dependent quantitative abundance data are valuable for gaining a mechanistic understanding of drug renal disposition and accumulation. Further, these data can also be integrated into systems pharmacology tools, such as physiologically based pharmacokinetic models, to enhance the interpretation of preclinical pharmacokinetic and toxicological data.
Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.
Culinary interventions (cooking classes) are a potential educational tool for salt reduction in the home diet, but their content has never been reported in detail. This study aimed to develop a cooking class for salt reduction, describe its rationale and structure so that other parties could replicate it, and preliminarily assess its impact on salt intake. A multidisciplinary research team developed a cooking class package to reduce salt content in the Japanese home diet. The package comprised its developmental policy, teaching methodology, a menu and recipes, and an implementation manual and aimed to allow third parties to replicate and modify the content. The team took the following step-by-step developmental approach. First, traditional home meals were modeled to create strategies contributing to a target of 2 g salt/meal. Then, educational topics were developed through these strategies, and finally, a dietitian produced menus and prepared documents for the class. The impact of the cooking class was assessed in a nonrandomized study of community residents. The outcome was differences in urinary salt excretion before and after the intervention. General linear models were used to account for the possible confounders. The authors assumed 4-7 g salt/meal from analyzing typical Japanese home diets and developed 3 strategies: (1) restricting salt content in the main dish, (2) maintaining good tastes without salty dishes, and (3) balancing nutrition with low-salt dishes. On the basis of these strategies, the authors selected a total of 5 educational topics that participants could learn and apply at home: 1a, a simple and reliable technique to limit salt in a serving; 2a, excluding salty dishes; 2b, staple foods with notable flavor and aroma; 3a, flavoring without salt in side dishes; and 3b, ingredients that should be used intentionally. The team dietitian translated these educational topics into a menu and recipes for hands-on training and prepared a manual for conducting the class. The class developed using this approach was successfully overseen by a dietitian outside the research team. In the validation study, the intervention group (n=52) showed a greater decrease in urinary salt excretion than the control group (n=46), with an adjusted difference of -1.38 g (p=0.001). The authors developed a cooking class package for salt reduction so that third parties could replicate and modify the class. The significant salt reduction noted in this study warrants further studies to apply this cooking class to other populations.