Yoxas/statistical_literacy huggingface dataset:
Innovative educational approaches such as simulation-based nursing education using virtual reality (VR) technologies provide new opportunities for nursing education. However, there is a lack of information on side effects, especially health-related side effects, of head-mounted displays (HMDs) on the human body when using VR devices for nursing simulation. This study aims to validate the German version of the Virtual Reality Sickness Questionnaire (VRSQ) and to evaluate its associations with sex and age, as reflected in the VRSQG scores (total score, oculomotor, and disorientation) over time. A longitudinal-sectional study was conducted. In addition to the VRSQG (pre-, post-, and 20 min post-intervention), participants (all nursing students) completed data on personal characteristics. Participants completed a VR simulation of a blood draw. Confirmatory factor analysis (CFA) was used to evaluate whether the measured construct was consistent with the original. In addition to the validity, internal consistency was analyzed and generalized linear models (GLMs) were used for data analysis. A total of 38 nursing students (mean age 26.8 years; SD = 7.1, 79.0% female) participated. The mean time spent in the VR simulation was 21 min. All participants completed the entire simulation. The CFA indicates (CFI = 0.981, SRMR = 0.040) VRSQG structure is given. Internal consistency showed low values for the subdomain Oculomotor (Cronbach alpha 0.670). For Disorientation and the Total score values showed a sufficient internal consistency. GLMs showed significant between subject associations with age over time with VRSQG total score, oculomotor, and disorientation. Older nursing students start with higher VRSQG-Scores. Over time, an approximation occurs, so that all participants reach a similar level by the final measurement point. No associations were found between sex (male/female) and VRSQG scores. The VRSQG is a reliable and valid self-assessment for measuring cybersickness in VR based nursing simulations, with cybersickness symptoms positively associated with age. However, in depth-evaluation regarding age-associations with cybersickness should be done. As well as studies to explore additional associations and emphasizes the importance of establishing cut-off values to assess the clinical relevance of the scores.
Innovative methods to collect dietary data at multiple times across the year are needed to better understand seasonal or temporal changes in household diets and measure the impact of nutrition-sensitive agricultural programmes in low-income countries. The present study aims to validate a picture-based research tool for participants to self-record their household's dietary diversity each month in villages of Manyoni District, Tanzania. Pictorial record charts were developed to reflect local food resources. In 113 randomly selected households, the person responsible for food preparation was trained to mark all items consumed by any household member within the home, or prepared for consumption outside the home, for a single recording day. The next day, an interview-based household 24-h food recall (H24HR) was collected for the same period. Separate analyses tested agreement (a) between picture charts and H24HR and (b) between H24HR following chart completion and on an alternative day. Concordance between methods differed between food groups and items but was high to very high for all cereals, vegetables, pulses, legumes and nuts and almost all fruits. Recording of ten items (including non-cultivated fruits and ingredients of mixed dishes) differed significantly between H24HR assessments, all of which were reported by more households in interviews following chart completion. Results suggest potential for visual prompts and the contemporaneous nature of data collection to improve the accuracy of interview-based recall. With adequate investment in developing and implementing context-adapted tools, pictorial charts may also offer an effective standalone method for use at multiple time-points in agricultural programmes.
Innovative teaching strategies in nursing education are essential with increasing enrollment. Collaborative learning and leadership (CLL) activities encourage near-peer learning through mentorship between senior-level and novice students while supporting teaching ratios in lab and clinical. In this study, senior nursing students' perceptions and performance during CLL activities were explored. Final-semester senior students participated in CLL activities and were evaluated on their leadership and engagement. Grading rubric results were summarized using descriptive statistics. Thematic analysis of students' post-CLL reflections supported common themes. Students' average scores (97.53%) confirm students were prepared and engaged in CLL activities. Senior students enjoyed "building confidence" through these activities, with a consistent theme of "becoming a leader," noted in reflections. Near-peer learning activities assisted senior students in development of leadership and communication skills, preparing them for nursing practice. Recommendations include developing instructions for varied CLL activities and exploring faculty perspectives regarding this experience.
Inpatients need to recognize their fall risk accurately and objectively. Nurses need to assess how patients perceive their fall risk and identify the factors that influence patients' fall risk perception. This study aims to explore the congruency between nurses' fall risk assessment and patients' perception of fall risk and identify factors related to the non-congruency of fall risk. A descriptive and cross-sectional design was used. The study enrolled 386 patients who were admitted to an acute care hospital. Six nurses assessed the participants' fall risk. Congruency was classified using the Morse Fall Scale for nurses and the Fall Risk Perception Questionnaire for patients. The nurses' fall risk assessments and patients' fall risk perceptions were congruent in 57% of the participants. Underestimation of the patient's risk of falling was associated with gender (women), long hospitalization period, department (orthopedics), low fall efficacy, and history of falls before hospitalization. Overestimation of fall risk was associated with age group, gender (men), department, and a high health literacy score. In the multiple logistic regression, the factors related to the underestimation of fall risk were hospitalization period and department, and the factors related to the overestimation of fall risk were health literacy and department. Nurses should consider the patient's perception of fall risk and incorporate it into fall prevention interventions. Nurses need to evaluate whether patients perceive the risk of falling consistently. For patients who underestimate or overestimate their fall risk, it may be helpful to consider clinical and fall-related characteristics together when evaluating their perception of fall risk.
Inquiry-based learning instructional design methods support online health professions capability-based curricula. This article proposes which instructional design priorities should guide development of inclusive, accessible online curricula and learning experiences.
Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification. Specifically, as the number of connections falls below a critical threshold, flight performance drops considerably. We develop sparsification paradigms and explore their limits for control tasks. Monte Carlo simulations also quantify the statistical distribution of network weights during pruning given initial random weights of the DNNs. We demonstrate that on average, the network can be pruned to retain a small amount of original network weights and still perform comparably to its fully-connected counterpart. The relative number of remaining weights, however, is highly dependent on the initial architecture and size of the network. Overall, this work shows that sparsely connected DNNs are capable of predicting the forces required to follow flight trajectories. Additionally, sparsification has sharp performance limits.
Insect growth regulators (IGRs) have been developed as effective control measures against harmful insect pests to disrupt their normal development. This study is to propose a mathematical model to evaluate the cost-effectiveness of IGRs for pest management. The key features of the model include the temperature-dependent growth of insects and realistic impulsive IGRs releasing strategies. The impulsive releases are carefully modeled by counting the number of implements during an insect's temperature-dependent development duration, which introduces a surviving probability determined by a product of terms corresponding to each release. Dynamical behavior of the model is illustrated through dynamical system analysis and a threshold-type result is established in terms of the net reproduction number. Further numerical simulations are performed to quantitatively evaluate the effectiveness of IGRs to control populations of harmful insect pests. It is interesting to observe that the time-changing environment plays an important role in determining an optimal pest control scheme with appropriate release frequencies and time instants.
Insider knowledge is critical information about how to achieve success that is not available to the general public but is relatively well-known to individuals within the domain and to those who have access to those individuals. The goal of this study was to examine the perceived role of insider knowledge in a sample of highly accomplished American professionals in science, technology, engineering, and mathematics (STEM). We asked participants explicitly if insider knowledge had played a role in their talent development trajectory from K-12 education to their current creative work, with questions related to experiences at the undergraduate and graduate level and during their careers. The study was exploratory, so no formal hypotheses were put forward. Given the lack of research on the topic of insider knowledge as defined in this paper, we conducted semi-structured interviews with a select group of individuals who had successful careers in STEM fields, both to see if insider knowledge was something that they considered important and to see if they felt that insider knowledge had played a role in their educational and career trajectories. Our hope is that the results of this paper will inform future in-depth studies on the topic of insider knowledge.
Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical "Aha!" experience, the evidence is inconclusive. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers' subjective experience of the solution process. In our previous paper, we used matchstick arithmetic problems to demonstrate that it is possible to objectively trace problem-solving processes by combining eye movements with new analytical and statistical approaches. Specifically, we divided the problem-solving process into ten (relative) temporal phases to better capture possible small changes in problem representation. Here, we go a step further to demonstrate that classical statistical procedures, such as ANOVA, cannot capture sudden representational change processes, which are typical for insight problems. Only nonlinear statistical models, such as generalized additive (mixed) models (GAMs) and change points analysis, correctly identified the abrupt representational change. Additionally, we demonstrate that explicit hints reorient participants' focus in a qualitatively different manner, changing the dynamics of restructuring in insight problem solving. While insight problems may indeed require a sudden restructuring of the initial mental representation, more sophisticated analytical and statistical approaches are necessary to uncover their true nature.
Insomnia is one of the most common sleep disorders and is characterized by a subjective perception of difficulty falling asleep. Drivers with insomnia are vulnerable to distraction and exhibit higher levels of risk while driving. This study investigated the effect of two sources of in-vehicle distractions on the driving performance of drivers with insomnia and good sleepers by analyzing different driving behavior measures. Twenty-one drivers with insomnia and twenty-one healthy volunteers were recruited to complete simulated driving dual tasks. The primary task required the participants to perform: (a) a lane-keeping task, and (b) a lane-change task. The secondary task required the participants to deal with: (a) baseline (non-task), (b) internal distraction task, and (c) external distraction task. The internal distraction task required participants to complete quantitative reasoning tasks, while the external distraction task was a 0-back test. The relationship between distracted driving ability and cognitive function was also investigated. The results demonstrate that for lane-keeping tasks, drivers with insomnia had significantly higher standard deviations (SD) for speed, throttle position, acceleration, and lateral position than healthy drivers under internal distraction, but the driving performance did not differ significantly between groups under internal distraction or baseline. In the lane-change task, drivers with insomnia had higher SDs for steering wheel angle, steer angular velocity, lateral acceleration, and lateral speed than healthy drivers under external distraction. Moreover, external distraction impaired driving behavior in the healthy group, while internal distraction impaired driving ability in both groups. Healthy drivers with cognitive impairment displayed impaired lane-keeping abilities under internal distractions and impaired lane-changing abilities under external distractions. Driving performance in the insomnia group was not significantly associated with cognitive function. The results demonstrate that insomnia and distraction impair driving ability, and driver performance is affected differently by the distraction source (internal or external). The driving ability of healthy drivers with decreased cognition was impaired, but not that of insomniacs.The findings of this study provide new insights for preventing and estimating the potential influence of distracted driving behavior in individuals with insomnia.
Instrumented gait analysis (IGA) is an assessment and research tool with proven impacts on clinical decision-making for the management of ambulant children and young people with cerebral palsy (CYPwCP) but is underused and variably understood by relevant clinicians. Clinicians' difficulties in gaining expertise and confidence in using IGA are multifactorial and related to access for clinical decision-making, limited training opportunities and inability to translate this training into clinical practice. The primary aim of this study is to test the feasibility of an educational intervention to advance clinicians' application of gait analysis in CYPwCP, to inform a definitive trial. The secondary aim is to measure the effect that appropriate IGA training has on physiotherapists' knowledge, skills, confidence and behaviours. This will be a two-arm feasibility randomised controlled trial with an experimental and control group. The 6-week on-line intervention uses a multicomponent approach grounded in behavioural change techniques. A repeated measures design will be adopted, whereby participants will complete outcome measures at baseline, immediately after the intervention and at 4 months. The primary outcome measures (trial feasibility-related outcomes) are recruitment and engagement. The secondary outcome measures (trial research-related outcomes) are knowledge, skills, confidence and practice change. Outcome measures will be collected via online questionnaires and during observed skill assessments. Analysis of data will use descriptive statistics, two-way mixed ANOVA model and qualitative content analysis. This study will determine feasibility of the definitive randomised control trial of educational intervention delivered to advance clinicians' application of gait analysis in CYPwCP. This study offers the shift in emphasis from regarding IGA as a tool to a focus on clinicians' requirements for access, training and a well-defined role to optimise utilisation of IGA. The impact of this should be better engagement with IGA and clinical practice change. This study will contribute to a body of educational research into clinical education of healthcare professionals and IGA training offering insight into high levels of evaluation evidence including clinical behaviour change. Protocol has been registered with the Open Science Framework (osf.io/nweq6) in June 2023.
Insufficient physical activity (PA) in early childhood is linked to adverse health outcomes and a heightened risk of obesity. Successful PA programmes often require input from key stakeholders, such as parents and educators. However, research on stakeholders' perspectives regarding PA programmes for preschool children is limited, impeding effective programme design and implementation. This study aims to explore the perspectives of key stakeholders to gain insights into the challenges, facilitators, and motivators that influence the planning, execution, and sustainability of the "I'm an Active Hero (IAAH) intervention component," a preschool-based initiative designed to promote PA among young children. Semi-structured interviews were conducted in Saudi Arabia with individual preschool principals (n = 2), and focus group discussions were held, respectively, with preschool staff members (n = 4, all female) and parents (4 mothers, 5 fathers). A thematic analysis identified four main themes: (1) Barriers to parental involvement in preschool PA interventions, such as time constraints, lack of flexibility, limited space, and a shortage of trained staff; (2) Risks and benefits of children's programme participation; (3) Motivators including rewards, non-financial incentives, and concerns about childhood obesity and a sedentary lifestyle; (4) Facilitating factors for overcoming barriers, including staff training, time reallocation, staff coordination, space optimization, non-financial incentives, and sustaining partnerships. This study's findings are crucial for childcare professionals, preschools, education authorities, and policymakers, offering valuable insights for future research. However, further collaboration with key stakeholders is essential to enhance individual attitudes and preschool policies for effective intervention implementation.
Insufficient screening and management of malnutrition leads to increase morbidity and mortality. to evaluate the characteristics and clinical outcomes of malnourished patients referred to a specialized outpatient clinic for the management of malnutrition (primary); to compare the latter according to malnutrition severity; to determine the factors associated with severe malnutrition and mortality; to compare the consistency of the decided medical nutrition therapy with the European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines (secondary). This retrospective, observational study included malnourished adults referred for the first time to the specialized nutrition consultation of a teaching University Hospital during 50 months. Malnutrition was diagnosed according to the Global Leadership Initiative on Malnutrition criteria, including body composition assessment by bioimpedance analysis. Chi2 or Fisher, Student or Wilcoxon rank-sum tests; multivariable logistic regression to assess the factors associated with severe malnutrition and mortality. 108 malnourished adults were included: 74% had severe malnutrition. The main secondary diagnosis was digestive cancer (48.2%). During the follow-up (median = 70 days) after the first nutritional consultation, 11% of patients were admitted at hospital, 19% had infections and 23.1% died, without any difference according to malnutrition severity. Severely malnourished patients had lower body mass index, a smaller fat mass index (FMI) (4.6 ± 1.8 vs 6.0 ± 2.5 kg/m2; p = 0.01), and a higher level of total body water (64.7 ± 7.1 vs 60.6 ± 5.4%; p = 0.02), compared to moderately malnourished individuals. A low FMI (odds ratio = 0.72 [0.54-0.96]) was the only factor significantly associated with severe malnutrition. We did not find any factor associated with mortality. There was a moderate consistency (47.1%) between the decided medical nutrition therapy and the ESPEN guidelines of nutritional care. Adults referred for the first time to a specialized nutritional consultation present mostly with severe malnutrition and are at risk for significant complications, leading to a high mortality rate. In this population, a low FMI is associated with severe malnutrition. An earlier diagnosis and care of malnutrition and an earlier referral to clinical nutrition units would improve outcomes.
Insulin Dependent Diabetes Mellitus (IDDM) is a chronic disease characterized by the inability of the pancreas to produce sufficient amounts of insulin. Daily compensation of the deficiency requires 4-6 insulin injections to be taken daily, the aim of this insulin therapy being to maintain normoglycemia--i.e., a blood glucose level between 4-7 mmol/L. To determine the quantity and timing of these injections, various different approaches are used. Currently, mostly qualitative and semi-quantitative models and reasoning are used to design such a therapy. Here, an attempt is made to show how system identification and control may be used to estimate predictive quantitative models to be used in design of optimal insulin regimens. The system was divided into three subsystems, the insulin subsystem, the glucose subsystem and the insulin-glucose interaction. The insulin subsystem aims to describe the absorbtion of injected insulin from the subcutaneous depots and the glucose subsystem the absorbtion of glucose from the gut following a meal. These subsystems were modeled using compartment models and proposed models found in the literature. Several black-box models and grey-box models describing the insulin/glucose interaction were developed and analysed. These models were fitted to real data monitored by a IDDM patient. Many difficulties were encountered, typical of biomedical systems: Non-uniform and scarce sampling, time-varying dynamics and severe nonlinearities were some of the difficulties encountered during the modeling. None of the proposed models were able to describe the system accurately in all aspects during all conditions. However, all the linear models shared some dynamics. Based on the estimated models, short-term blood glucose predictors for up to two-hour-ahead blood glucose prediction were investigated.