Yoxas/statistical_literacy huggingface dataset:
The relationship between reading ability and health, known as health literacy, broadly reflects the skills and competencies required to operate within the healthcare environment. It is only recently that we have seen attempts to conceptualize health literacy in the context of medication use by using terms such as medication literacy. Health literacy changes over one's lifetime and is dependent on factors such as numeracy, education, income, gender and country. Low health literacy and low medication literacy have been identified as significant risk factors for poor health outcomes of adults and children. With an evaluation of common tools used to assess health literacy and medication literacy, the aim of this review is to describe the shared responsibility between patients and healthcare providers for the communication of health and medication information. Key strategies and interventions to improve two-way communication between patients and healthcare providers are highlighted, with a focus on how health literacy can impact child health outcomes.
The relationship between social class and mental illness stigma has received little attention in recent years. At the same time, the concept of mental health literacy has become an increasingly popular way of framing knowledge and understanding of mental health issues. British Social Attitudes survey data present an opportunity to unpack the relationships between these concepts and social class, an important task given continuing mental health inequalities. Regression analyses were undertaken which centred on depression and schizophrenia vignettes, with an asthma vignette used for comparison. The National Statistics Socio-economic Classification, education and income were used as indicators of class. A number of interesting findings emerged. Overall, class variables showed a stronger relationship with mental health literacy than stigma. The relationship was gendered such that women with higher levels of education, especially those with a degree, had the lowest levels of stigma and highest levels of mental health literacy. Interestingly, class showed more of an association with stigma for the asthma vignette than it did for both the depression and schizophrenia vignettes, suggesting that mental illness stigma needs to be contextualised alongside physical illness stigma. Education emerged as the key indicator of class, followed by the National Statistics Socio-economic Classification, with income effects being marginal. These findings have implications for targeting health promotion campaigns and increasing service use in order to reduce mental health inequalities.
The relative stringency of risk regulation across countries may have significant implications for public health and environmental outcomes, as well as for economic and trade impacts. In this study, we build on prior literature-which has often employed qualitative case studies, and has often focused on comparing the United States and Europe-by using a quantitative evidential reasoning approach to compare the relative stringency of federal/central level written rules for 45 randomly selected environmental risks in the United States and China. We find that, on average, in this sample of 45 environmental risks, the written rules for environmental risk regulation were more stringent in the United States than in China. Within this sample, we find that relative stringency was selective, leaning in both directions, as the United States and China each regulated some risks more stringently than the other; for example, the US written rules were more stringent for risks of toxic chemicals and most air pollutants, whereas China's written rules were more stringent for risks in agriculture. We also observe nuanced differences in relative regulatory stringency within sectors and risks; even where one country regulated one risk more stringently, the other country may regulate certain aspects of that risk more stringently. We comment on possible explanations for the patterns we observe. Our methods and findings may contribute to better understanding of comparative risk regulation across the United States and China, and worldwide. We also recognize that in addition to the written rules studied here, countries may also vary in their implementation.
The relevance of anatomical dissection in instructing anatomy to medical, dental, and other health science students is indisputable. Ethiopian anatomists encountered challenges in obtaining human bodies for anatomy education and research, both prior to and following the COVID-19 outbreak. The challenges intensified during the pandemic, significantly affecting anatomy education in Ethiopia. This study seeks to investigate the sources of bodies for anatomy, spanning the periods before and after COVID-19, with a particular focus on identifying the primary challenges associated with sourcing of bodies in Ethiopia. Fifty (50) anatomists completed a survey distributed to ten (10) randomly chosen medical institutions in Ethiopia. The survey gathered information on the body profile (number of bodies, age, sex, sources, and methods of body disposal), and the challenges faced during the sourcing of bodies in the years 2018 and 2023. A total of sixty-three (63) bodies were used by the sampled medical institutions between 2018 and 2023 academic years. All (100%) of the bodies used were unclaimed human bodies. Most (66.7%) of these bodies were males. The majority (65.5%) of these bodies were sourced from Tikur Anbessa hospital in Addis Ababa. None (0%) of the sampled medical institutions had body donation programs. Disposal of human tissues encompassed various methods, including the retention of skeletons, prosection of vital organs, and burial of remaining tissues. Economic constraints and the absence of a legal framework document were the main challenges in acquiring bodies in the pre-pandemic period. The COVID-19 pandemic prevention policies and the civil war further exacerbated the challenges in sourcing of bodies for anatomy dissections during the post-pandemic period. The reliance on unclaimed human bodies for anatomy education and research in Ethiopian medical institutions mirrors a common practice across many African countries. The authors suggest the development of a legislative framework or operational guidelines, coupled with empowering the medical institutions to outsource their own funding that will ultimately lead to an increased number of bodies available for anatomical dissection. Over time, implementation and promotion of body donation programs may also resolve body shortages for anatomy education in Ethiopia.
The relevance of the study is due to the fact that the Russian healthcare in terms of the pace and quality of digitalization lags behind the healthcare systems of other developed and developing countries. The low level of digital literacy of medical staff does not allow them to develop the proper level of competencies for working with high-tech equipment, software applications and various telemedicine services. This, in turn, reduces both the quality and efficiency of medical care for the population. The purpose of the article is to develop a profile of digital competence of medical staff. The methodology of the article is based on the use of methods of statistical analysis of public data and sociological research. The sample of the sociological study was formed from medical staff in the regions of Moscow, Tula, Bryansk and Sverdlovsk. The pace and level of digitalization of the Russian healthcare is several orders of magnitude lower than the healthcare systems of the United States, South Korea, Japan and some other OECD countries. The development of digital competencies and a high level of digital literacy are observed among those medical workers who, firstly, operate in high-tech medical organizations, and, secondly, among those medical workers whose organizations are located close to regional economic centers. Russian healthcare needs additional investments, which can be directed both to digitalization and to the development of digital literacy of medical staff. To assess digital literacy, a special digital competence profile of middle and senior medical personnel was developed.
The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model's functional feasibility.
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to assess the suitability of input ultrasound cine loops collected using a handheld echocardiography device for processing by an automated quantification algorithm (e.g. ejection fraction estimation). POC echocardiograms (n=885 DICOM cine loops from 175 patients) from two sites were collected using a handheld ultrasound device and annotated for image quality at the frame-level. Attributes of high-quality frames for left ventricular (LV) quantification included a temporally-stable LV, reasonable coverage of LV borders, and good contrast between the borders and chamber. Attributes of low-quality frames included temporal instability of the LV and/or imaging artifacts (e.g., lack of contrast, haze, reverberation, acoustic shadowing). Three different neural network architectures were investigated - (a) frame-level convolutional neural network (CNN) which operates on individual echo frames (VectorCNN), (b) single-stream sequence-level CNN which operates on a sequence of echo frames (VectorCNN+LSTM) and (c) two-stream sequence-level CNNs which operate on a sequence of echo and optical flow frames (VectorCNN+LSTM+Average, VectorCNN+LSTM+MinMax, and VectorCNN+LSTM+ConvPool). Evaluation on a sequestered test dataset containing 76 DICOM cine loops with 16,914 frames showed that VectorCNN+LSTM can effectively utilize both spatial and temporal information to regress the quality of an input frame (accuracy: 0.925, sensitivity = 0.860, specificity = 0.952), compared to the frame-level VectorCNN that only utilizes spatial information in that frame (accuracy: 0.903, sensitivity = 0.791, specificity = 0.949). Furthermore, an independent sample t-test indicated that the cine loops classified to be of adequate quality by the VectorCNN+LSTM model had a statistically significant lower bias in the automatically estimated EF (mean bias = - 3.73 ± 7.46 %, versus a clinically obtained reference EF) compared to the loops classified as inadequate (mean bias = -15.92 ± 12.17 %) (p = 0.007). Thus, cine loop stratification using the proposed spatiotemporal CNN model improves the reliability of automated point-of-care echocardiography image interpretation.
The remarkable ascent of entrepreneurship witnessed as a scientific field over the last 4 decades has been made possible by entrepreneurship's ability to absorb theories, paradigms, and methods from other fields such as economics, psychology, sociology, geography, and even biology. The respectability of entrepreneurship as an academic discipline is now evidenced by many other fields starting to borrow from the entrepreneurship view. In the present paper, seven examples are given from this "pay back" development. These examples were first presented during a seminar at the Erasmus Entrepreneurship Event called what has the entrepreneurship view to offer to other academic fields? This article elaborates on the core ideas of these presentations and focuses on the overarching question of how entrepreneurship research impacts the development of other academic fields. We found that entrepreneurship research questions the core assumptions of other academic fields and provides new insights into the antecedents, mechanisms, and consequences of their respective core phenomena. Moreover, entrepreneurship research helps to legitimize other academic fields both practically and academically.
The remarkable capabilities of generative artificial intelligence and large language models (LLMs) such as ChatGPT have delighted users around the world. Educators have regarded these tools as either a cause for great concern, an opportunity to educate students on cutting-edge technology, or often some combination of the two. Throughout the Fall 2023 semester, we explored the use of ChatGPT (and Bard, among other LLMs) in a graduate level numerical and statistical methods course for PhD-level bioengineers. In this article we share examples of this ChatGPT content, our observations on what worked best in our course, and speculate on how bioengineering students may be best served by this technology in the future.
The repercussions of food insecurity are widely recognized to negatively impact overall health and are influenced by a complex interplay of physiological, psychological, social, and environmental factors. This study examined the disparities in food consumption and literacy between among food security households and food insecurity households using data from the Korea Rural Economic Institute's 2022 Consumer Behavior Survey for Food, which involved 3,321 respondents. Food security households had a greater understanding of and better attitude toward healthier food choices than food insecurity households. Economic ability was identified as having the most significant association with food purchasing behavior, with food security households spending more on average than food insecurity households. Structural equation modeling demonstrated the association of knowledge and attitude with dietary implementation and underscored the significance of consumer literacy as a factor related to willingness to pay for healthier foods. This study underscores the intertwined relationships among financial capacity, knowledge, and health-conscious dietary choices. It also suggests the need for targeted interventions addressing economic and educational gaps to foster healthier food consumption patterns across different socioeconomic contexts.
The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.
The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30-40% of customers participated in the DSM curriculum, the generation cost was reduced by 10-13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy.
The research aimed to determine the influence of a protocol designed for use in the domain of upper limb hypertonia due to brain injury on novice and expert occupational therapy clinical reasoning. Individual, structured repertory grid interviews were completed with 13 novice and eight expert occupational therapists prior to, and following, exposure to a domain-specific clinical reasoning protocol. Data were subjected to quantitative analyses (Principal Components Analysis, Generalised Procrustes Analysis). Novice participants demonstrated statistically significant change in the structure of their clinical reasoning following exposure to the protocol (P < 0.004). Prior to exposure, novices relied on therapy tasks, the problem-solving process, environmental factors and standard practice to structure their reasoning. Following exposure, novices' clinical reasoning changed to more closely reflect experts' reasoning. Thus, a 'structured approach' and (theoretical) practice perspectives became evident. Prior to exposure to the protocol, experts structured reasoning in terms of (personal and theoretical) practice perspectives, therapy tasks and the scope of such tasks (either 'general' or 'specific'). Following exposure, therapist/client collaboration and upper-limb-related constructs emerged as being used to structure experts' reasoning, although these changes were not statistically significant. A protocol designed for guiding clinical reasoning in the context of upper limb hypertonia was perceived to be conceptually useful by novice and expert occupational therapists.
The research investigated topic priorities for data literacy training for biomedical researchers and staff. An electronic survey was used to assess researchers' level of knowledge related to data literacy skills and the relevance of these skills to their work. Most respondents did not have any formal training in data literacy. Respondents considered most tasks highly relevant to their work but rated their expertise in tasks lower. Among this group, researchers have diverse data literacy training needs. Librarians' expertise makes them well suited to provide such training.
The research was conducted to measure the effect of health literacy-grounded web-based education program on health literacy, nutrition and exercise behaviours in adolescents. The increasing prevalence of obesity in adolescents is explained by a sedentary lifestyle and unhealthy diet. Health literacy is essential for adolescents to maintain their healthy behaviours. The research was conducted as a parallel group randomized controlled trial. The study was completed by 114 adolescents (experimental = 42, control = 72). The Personal Information Form, the Health Literacy Scale for School-Age Children and the Nutrition and Exercise Subscales were used to collect data. A web-based education program was implemented. Two-way mixed design ANOVA with Bonferroni test was used to examine the group, time and group * time interaction of the measurements. There was no statistically significant difference between the experimental and control groups regarding health literacy level, nutrition and exercise behaviours pre-test mean scores (p > 0.05). In the post-test, the mean scores of health literacy and nutrition behaviours in the experimental group were higher than the control group (p < 0.001). However, there was no significant difference in exercise behaviours (p > 0.05). The power and effect size in mean scores changes for health literacy (99.8%, η2 = 0.039) and nutrition (99.8%, η2 = 0.039) were moderate. The study concluded that health literacy-grounded web-based education program increased the health literacy levels of adolescents and improved their nutritional behaviours; however, there was no sufficient change in exercise behaviours.
The researcher aimed to identify how rehabilitation professionals engage in their peer leadership network during the first year following leader development training for the purpose of understanding the networking experiences, development of the peer leadership network, and expansion of collective leadership in an organization. A sequential exploratory mixed method design including Q-Methodology and focus group interviews identified the experiences of 11 rehabilitation professionals in an urban rehabilitation hospital during the first year following leader development training. Three themes were identified. These include: (a) an opportunity to connect, (b) a community of leaders, and (c) a healthy peer leadership network emerged from the data analysis. These results indicated that shared experiences and opportunities to connect in a robust peer leadership network can influence the growth of all leaders independent of their current leadership or networking competency. The opportunity to connect for shared discussions in a healthy peer leadership network can accentuate the learning following leader development curriculum as individual leaders develop leadership and as collectives advance organizational outcomes. Healthcare organizations should facilitate connections in a healthy leadership network to develop individual and collective leadership in an organization.