Yoxas/statistical_literacy huggingface dataset:
Bats are a reservoir for many viruses causing haemorrhagic fevers. Proximity to bats is a risk factor for virus spillover to animals and humans. We conducted this study to assess knowledge, perceptions, and exposure to bats in communities living near bat roosts in Bundibugyo District, Uganda. A cross-sectional study using mixed methods with both quantitative and qualitative data was conducted between September and December 2022. Participants for the quantitative data (survey) (n = 384) resided near bat caves and/or roost sites and were selected using multistage random sampling. The survey investigated participants' prior exposure to bats, as well as knowledge and perceptions of bat exposure. Logistic regression was used to determine factors associated with bat exposure. Participants for the qualitative data (focus group discussions) (n = 10, 6-8 participants each) were purposely selected based on engagement in guano mining, hunting, and farming activities. Perceived risk associated with bat-related activities were identified and ranked in the focus group discussions using participatory epidemiology tools. In total, (214/384, 55.7%) had a history of bat exposure and (208/384, 54.2%) had poor knowledge of risk factors associated with bat exposure. Increased exposure to bats was associated with being male (OR = 1.6; 95% CI: 1.0, 2.4 p-value = 0.038), staying in urban areas (OR = 1.9; p-value = 0.010), hunting (OR = 10.9; p-value = 0.024), and positive perception to bat guano being safe as fertiliser (OR = 2.5; p-value = 0.045). During the proportional piling process, a total of 7 risk factors were identified by 10 groups with hunting during an outbreak and consumption of bats being the most frequently identified. Overall, there was a strong statistical agreement in the ranking across the 10 focus groups (W = 0.52; p < 0.01; n = 10). Based on the provided data, the adjusted odds ratio of 0.7 for the good measures (p-value = 0.112), suggests a potential protective effect on the risk of bat exposure. Communities living around bat roosts frequently come into contact with bats, yet there is inadequate awareness regarding the behaviors that can lead to the transmission of bat- borne diseases to humans. It is essential to undertake educational initiatives and preventive measures to minimise the risks of bat-related infections. The need for targeted health communication and education efforts to address these knowledge gaps and promote an accurate understanding of bats and disease transmission. Understanding of diseases associated with bats will minimize bat-related health risks especially in communities engaged in wildlife hunting.
Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.
Bayes' Theorem provides a rationality-standard for information search when there are two mutually exclusive hypotheses and one or more statistical cues pertaining to the likelihoods of the hypotheses. Prior research shows that when people already have a cue pertaining to a hypothesis and are asked to seek additional information to help decide which hypothesis is correct, they tend to exhibit a specific form of pseudodiagnosticity: Rather than seek information that would assess the same cue relative to an alternative hypothesis, they tend to seek information about how a second cue would pertain to the first hypothesis. For example, if people are told that 70% of genuine paintings are landscapes, they then seek to know the percentage of genuine paintings that are watercolor rather than the percentage of fake paintings that are landscapes. However, this response pattern has sometimes been violated in a way that may depend on the cues' numerical values (e.g., 70% vs. 30%), thus raising a question as to the nature of the bias: Does the selection bias characterize the search process per se, or does it reflect the manner in which people utilize already-obtained percentage information? To address these issues, we employed a novel, search-only judgment paradigm in which people were asked to search for cues and to select them without ever obtaining the cues' percentage values. The results confirmed a tendency toward same-hypothesis pseudodiagnosticity both in primary (i.e., most-preferred) and secondary preference, and supported a model in which pseudodiagnosticity can proceed with or without numerical cue data.
Bayesian analysis is one of the topics included in the subject "Environmental Risk Analysis" taught at the Faculty of Agronomy of the University of Buenos Aires, Argentina. Traditionally, we use a tree of probabilities to organize basic concepts necessary to understand this analysis (for example, conditional probability, excluding events, independence, etc.). Then, we deal with concepts of Bayesian analysis (such as additional conditional probability, prior probabilities, posterior probabilities, and their applications). In this work, we propose to analyze the problem method that starts by analyzing the scenarios.•For that, we begin the analysis by paying attention to those events that we are certain have occurred.•We will separate them according to their possible origins and assign them a probability (conditional).•We will then use the probabilities to weight them, and then find a conditional probability of occurrence.
Bayesian networks (BNs) are a kind of graphical model that formally combines elements of graph and probability theory. BNs are a mathematically and statistically rigorous technique allowing their user to define a pictorial representation of assumed dependencies and influences among a set of variables deemed to be relevant for a particular inferential problem. The formalism allows one to process newly acquired evidence according to the rules of probability calculus. Applications of BNs have been reported in various forensic disciplines. However, there seems to be some reluctance to consider BNs as a more general framework for representing and evaluating sources of uncertainties associated with scientific evidence. Notably, BNs are widely thought of as an essentially numerical method, requiring "exact" numbers with a high "accuracy". The present paper aims to draw the reader's attention to the point that the availability of hard numerical data is not a necessary requirement for using BNs in forensic science. An abstraction of quantitative BNs, known as qualitative probabilistic networks (QPNs), and sensitivity analyses are presented and their potential applications discussed. As a main difference to their quantitative counterpart, QPNs contain qualitative probabilistic relationships instead of numerical relations. Sensitivity analyses consist of varying the probabilities assigned to one or more variables and evaluating the effect on one or more other variables of interest. Both QPNs and sensitivity analyses appear to be useful concepts that permit one to work in contexts with acute lack of numerical data and where reasoning consistent with the laws of probability should nevertheless be performed.
Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our approach extracts the probability of a trust factor being in a specific state directly from the devices (e.g. sensor quality). The strength of the relationship between related factors is defined by expert knowledge and incorporated into the model. We use propagation rules from requirements engineering to estimate how much each trust factor contributes to the related intermediate nodes in the network and ultimately compute the trust score. The trust score is a relative measure of trustworthiness when different devices are evaluated in the same test conditions and using the same Bayesian structure. To evaluate our approach, we developed Bayesian networks for the trust quantification of similar wearable devices from two manufacturers under identical test conditions and noise levels. The results demonstrated the learnability and generalizability of our approach.
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.
Because limited data exist about factors related to sugar-sweetened beverage (SSB) intake among younger children, we investigated factors associated with SSB intake among US children aged 1 to 5 years. We examined SSB intake (0, 1-3, or ≥4 times/week) by using data from the 2021 National Survey of Children's Health. We performed a multinomial logistic regression to calculate adjusted odds ratios (aORs) for select sociodemographic and household factors associated with moderate (1-3 times/week) and high (≥4 times/week) SSB intake. Overall, 36% of children consumed SSBs 1 to 3 times/week and 21% consumed 4 or more times/week. Both moderate and high SSB intake were associated with child's age, child's race and ethnicity, highest caregiver education level, household income, primary household language, and frequency of family meals. For example, children who lived in households with caregiver education level of high school graduate or less were significantly more likely to have moderate (aOR, 2.06) and high (aOR, 2.81) SSB intake than those who lived in households with caregiver education level of college degree or higher. High SSB intake was also associated with marginal household food sufficiency, nonmetropolitan statistical area status, and receipt of government food benefits. Several sociodemographic and household factors were significantly associated with SSB intake among children aged 1 to 5 years. Public health initiatives designed to address SSB intake among young children in various settings including pediatric health care, early care and education, and the child's home could consider key associated factors.
Because of the rapidly rising cultural diversity, the ability to recognize cultural diversity is extremely important to all healthcare professionals, especially to nurses. However, there is a paucity of information regarding the cultural sensitivity of nurses in Ethiopia. Hence, this study aimed to assess cultural sensitivity and associated factors among nurses working at Jimma Medical Center, Oromia Regional State, Southwest Ethiopia. Health-facility-based cross-sectional study was conducted among 244 nurses selected by simple random sampling from May 20th to June 20th, 2020. A structured, self-administered questionnaire was used to collect data. Data were analysed using Statistical Product and Service Solution Version 26.0. Bivariate binary logistic regression analyses were used to select variables for the final model. Multivariable binary logistic regression analysis was used to identify factors associated with cultural sensitivity. Statistical significance was declared at [Formula: see text]0.05, and adjusted odds ratio with respective 95% CI was used to report significant covariates. Out of the total sample, 236 nurses participated in this study, giving a response rate of 96.72%. Nurses who were culturally sensitive while delivering routine nursing services were found to be 40.3% (95% CI (34.3, 46.6)). Level of education ([AOR (95% CI)], [4.846 (1.188, 19.773)]), interpersonal communication ([AOR (95% CI)], [4.749 (1.334, 16.909)]), and intercultural communication ([AOR (95% CI)], [51.874 (13.768, 195.45)]) were positively and significantly associated with the cultural sensitivity of nurses. Cultural sensitivity is found to be low in the study area. Increasing level of education, effective interpersonal communication abilities, and intercultural communication abilities positively predict cultural sensitivity of nurses. It is helpful for nurses to improve their knowledge of transcultural nursing theories and cultural understanding.
Because of the relationship between independent living and activities of daily living, care teams spend significant time managing assisted living residents' toileting problems. Recently, the TrueLoo was developed as a connected toilet seat to automatically log and monitor toileting sessions. This study aimed to demonstrate the validity of the TrueLoo to (1) record and identify toileting sessions with regard to stool and urine events; (2) compare the results with the person-reported, standard-of-care methods; and (3) establish metrics of user acceptability and ease of use in a assisted living facility population. We used two phases: (1) initial development of the TrueLoo algorithms to accurately identify urine and stool events and (2) evaluation of the algorithms against person-reported, standard-of-care methods commonly used in assisted living facilities. Phase 2 analyzed data over a 3-day period from 52 devices. Participants' age ranged from 63 to 101 (mean 84, SD 9.35) years. Acceptability and ease-of-use data were also collected. Regarding the development of the TrueLoo algorithm for urine assessment, sensitivity and specificity of 96% and 85% were observed when evaluating a gold-standard labeled data set, respectively (F1-score=0.95). For stool, sensitivity and specificity of 90% and 79% were observed, respectively (F1-score=0.85). Regarding the TrueLoo algorithm in assisted living settings, classification performance statistics for urine assessment revealed sensitivity and specificity of 84% and 94%, respectively (F1-score=0.90), and for stool, 92% and 98%, respectively (F1-score=0.91). Throughout the study, 46 person-reported instances of urine were documented, compared with 630 recorded by the TrueLoo. For stool events, 116 person-reported events were reported, compared with 153 by the TrueLoo. This indicates that person-reported events were captured 7% (46/630) of the time for urine and 76% (116/153) of the time for stool. Overall, 45% (32/71) of participants said that the new toilet seat was better than their previous one, 84% (60/71) reported that using the TrueLoo was easy, and 99% (69/71) said that they believed the system could help aging adults. Over 98% (69/71) of participants reported that they would find alerts related to their health valuable and would be willing to share this information with their doctor. When asked about sharing information with caregivers, 66% (46/71) reported that they would prefer the TrueLoo to send information and alerts to their caregiver, as opposed to the participant having to personally communicate those details. The TrueLoo accurately recorded toileting sessions compared with standard-of-care methods, successfully establishing metrics of user acceptability and ease of use in assisted living populations. While additional validation studies are warranted, data presented in this paper support the use of the TrueLoo in assisted living settings as a model of event monitoring during toileting.
Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE) is one of the most successful algorithms and is frequently used to solve various industrial problems. Over the previous 2 decades, DE has been heavily modified to improve its capabilities. Several DE variations secured positions in IEEE CEC competitions, establishing their efficacy. However, to our knowledge, there has never been a comparison of performance across various CEC-winning DE versions, which could aid in determining which is the most successful. In this study, the performance of DE and its eight other IEEE CEC competition-winning variants are compared. First, the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions, and the performances have been compared. One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems, out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite, have been solved to compare the performances. Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm's ability statistically. Performance analysis exposes that none of the DE variants can solve all the problems efficiently. Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.
Because residents are frequently delegated the task of obtaining consent early in their training, the American Association of Medical Colleges describes "obtaining informed consent" as a core entrustable professional activity (EPA) for medical school graduates. However, prior studies demonstrated that residents frequently perform this task without receiving formal instruction or assessment of competency. This study sought to understand how attending physicians decide to delegate obtaining informed consent for surgical procedures to trainees. The authors conducted a survey of attending surgeons at a university-based health care system of 6 affiliated teaching hospitals (October-December 2020) to collect data about current entrustment practices and attendings' knowledge, experience, and attitudes surrounding the informed consent process. Summary statistics and bivariate analyses were applied. Eighty-five attending surgeons participated (response rate, 49.4%) from diverse specialties, practice types, and years in practice. Fifty-eight of 85 (68.2%) stated they "never" granted responsibility for the consent conversation to a trainee and 74/81 (91.4%) reported they typically repeated their own consent conversation whenever a trainee already obtained consent. The most common reasons they retained responsibility for consent were ethical duty (69/82, 84.1%) and the patient relationship (65/82, 79.3%), while less than half (40/82, 48.8%) described concerns about trainee competency. Reflecting on hypothetical clinical scenarios, increased resident competency did not correspond with increased entrustment (P = 0.27 - 0.62). Nearly all respondents (83/85, 97.7%) believed residents should receive formal training, however, only 41/85 (48.2%) felt additional training and assessment of residents might change their current entrustment practices. Attendings view informed consent as an ethical and professional obligation that typically cannot be entrusted to trainees. This practice is discordant with previous literature studying residents' perspectives. Furthermore, resident competency does not play a predominant role in this decision, calling into question whether informed consent can be considered an EPA.
Bedside cardiac assessment (BCA) is deficient across a spectrum of noncardiology trainees. Learners not taught BCA well may become instructors who do not teach well, creating a self-perpetuating problem. To improve BCA teaching and learning, we developed a high-quality, patient-centered curriculum for medicine clerkship students that could be flexibly implemented and accessible to other health professions learners. With a constructivist perspective, we aligned learning goals, activities, and assessments. The curriculum used a "listen before you auscultate" framework, capturing patient history as context for a six-step, systematic approach. In the flipped classroom, short videos and practice questions preceded two 1-hour class activities that integrated diagnostic reasoning, pathophysiology, physical diagnosis, and reflection. Activities included case discussions, jugular venous pressure evaluation, heart sound competitions, and simulated conversations with patients. Two hundred sixty-eight students at four US and international medical schools participated. We incorporated feedback, performed thematic analysis, and assessed learners' confidence and knowledge. Low posttest data capture limited quantitative results. Students reported increased confidence in BCA ability. Knowledge increased in both BCA and control groups. Thematic analysis suggested instructional design strategies were effective and peer encounters, skills practice, and encounters with educators were meaningful. The curriculum supported active learning of day-to-day clinical competencies and promoted professional identity formation alongside BCA ability. Feedback and increased confidence on the late-clerkship posttest suggested durable learning. We recommend approaches to confirm this and other elements of knowledge, skill acquisition, or behaviors and are surveying impacts on professional identity formation-related constructs.