Yoxas/statistical_literacy huggingface dataset:
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 and 7mmol/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 absorption of injected insulin from the subcutaneous depots and the glucose subsystem the absorption 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 analyzed. These models were fitted to real data monitored by an 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 designed. Furthermore, we explored the issues that arise when applying prediction theory and control to short-term blood glucose prediction.
Integrated care facilitates better outcomes for patients and their family, health workforce and health and social care systems. Accordingly, integrated care is a global and inherent area of work for health professionals. Despite this, formal learning programmes for workforce development in integrated care are still rare. In this paper, I have shared lessons about developing an innovative and sustainable integrated care learning programme for teachers, practitioners and learners in integrated care discipline. These lessons are drawn from my experience of leading an integrated care specialisation programme (that sits at Australian Qualification Framework Level 9) at a public university in Australia. An integrated care learning programme should be designed with appropriate philosophies (e.g., social constructivism and connected learning) and relevant multidisciplinary content that addresses deficiencies in practices of integrated care. Moreover, the programme should aim for sustainability, through financial feasibility and continuity with offering of creative and effective solutions in integrated care. Similarly, the programme should have a suit of curriculum development and teaching initiatives to promote genuine and collaborative learning. Throughout this learning journey, it would be critical for teachers to engage in 'walking-the-talk', that is, genuinely collaborate with patients, practitioners and students, for successful delivery of an innovative and sustainable learning programme.
Integrated Community Case Management (ICCM) of common childhood illness is one of the global initiatives to reduce mortality among under-five children by two-thirds. It is also implemented in Ethiopia to improve community access and coverage of health services. However, as per our best knowledge the implementation status of integrated community case management in the study area is not well evaluated. Therefore, this study aimed to evaluate the implementation status of the integrated community case management program in Gondar City, Northwest Ethiopia. A single case study design with mixed methods was employed to evaluate the process of integrated community case management for common childhood illness in Gondar town from March 17 to April 17, 2022. The availability, compliance, and acceptability dimensions of the program implementation were evaluated using 49 indicators. In this evaluation, 484 mothers or caregivers participated in exit interviews; 230 records were reviewed, 21 key informants were interviewed; and 42 observations were included. To identify the predictor variables associated with acceptability, we used a multivariable logistic regression analysis. Statistically significant variables were identified based on the adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value. The qualitative data was recorded, transcribed, and translated into English, and thematic analysis was carried out. The overall implementation of integrated community case management was 81.5%, of which availability (84.2%), compliance (83.1%), and acceptability (75.3%) contributed. Some drugs and medical equipment, like Cotrimoxazole, vitamin K, a timer, and a resuscitation bag, were stocked out. Health care providers complained that lack of refreshment training and continuous supportive supervision was the common challenges that led to a skill gap for effective program delivery. Educational status (primary AOR = 0.27, 95% CI:0.11-0.52), secondary AOR = 0.16, 95% CI:0.07-0.39), and college and above AOR = 0.08, 95% CI:0.07-0.39), prescribed drug availability (AOR = 2.17, 95% CI:1.14-4.10), travel time to the to the ICCM site (AOR = 3.8, 95% CI:1.99-7.35), and waiting time (AOR = 2.80, 95% CI:1.16-6.79) were factors associated with the acceptability of the program by caregivers. The overall implementation status of the integrated community case management program was judged as good. However, there were gaps observed in the assessment, classification, and treatment of diseases. Educational status, availability of the prescribed drugs, waiting time and travel time to integrated community case management sites were factors associated with the program acceptability. Continuous supportive supervision for health facilities, refreshment training for HEW's to maximize compliance, construction clean water sources for HPs, and conducting longitudinal studies for the future are the forwarded recommendation.
Integrated Testing Strategies (ITSs) are considered tools for guiding resource efficient decision-making on chemical hazard and risk management. Originating in the mid-nineties from research initiatives on minimizing animal use in toxicity testing, ITS development still lacks a methodologically consistent framework for incorporating all relevant information, for updating and reducing uncertainty across testing stages, and for handling conditionally dependent evidence. This paper presents a conceptual and methodological proposal for improving ITS development. We discuss methodological shortcomings of current ITS approaches, and we identify conceptual requirements for ITS development and optimization. First, ITS development should be based on probabilistic methods in order to quantify and update various uncertainties across testing stages. Second, reasoning should reflect a set of logic rules for consistently combining probabilities of related events. Third, inference should be hypothesis-driven and should reflect causal relationships in order to coherently guide decision-making across testing stages. To meet these requirements, we propose an information-theoretic approach to ITS development, the "ITS inference framework", which can be made operational by using Bayesian networks. As an illustration, we examine a simple two-test battery for assessing rodent carcinogenicity. Finally, we demonstrate how running the Bayesian network reveals a quantitative measure of Weight-of-Evidence.
Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges. A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests. Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI. The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.
Integrating diverse perspectives in psychological science can enhance innovation in research and allow research teams to better study diverse populations of individuals through an authentic lens. Despite recent efforts to better address issues of race and ethnicity in research samples, the field of psychology broadly-and the area of mathematics cognition specifically-has largely failed to support scientists from diverse racial and ethnic backgrounds. In this essay, we consider the unique contributions that scholars of color can make to psychological research in mathematics cognition. Next, we reveal common challenges faced by scholars of color and challenges to recruiting and maintaining scholars of color in our community with a focus on Black scholars. Finally, we propose actions for diversifying the "pipeline" of promising scholars.
Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. This study aimed to explore the possibility of using routine malaria data and satellite-derived climate data to forecast malaria cases in Togo. Generalised additive (mixed) models were developed to forecast the monthly number of malaria cases in 40 health districts and three target groups. Routinely collected malaria data from 2013 to 2016 and meteorological and vegetation data with a time lag of 1 or 2 months were used for model training, while the year 2017 was used for model testing. Two methods for selecting lagged meteorological and environmental variables were compared: a first method based on statistical approach ('SA') and a second method based on biological reasoning ('BR'). Both methods were applied to obtain a model per target group and health district and a mixed model per target group and health region with the health district as a random effect. The predictive skills of the four models were compared for each health district and target group. The most selected predictors in the models per district for the 'SA' method were the normalised difference vegetation index, minimum temperature and mean temperature. The 'SA' method provided the most accurate models for the training period, except for some health districts in children ≥5 years old and adults and in pregnant women. The most accurate models for the testing period varied by health district and target group, provided either by the 'SA' method or the 'BR' method. Despite the development of models with four different approaches, the number of malaria cases was inaccurately forecasted. These models cannot be used as such in malaria control activities in Togo. The use of finer spatial and temporal scales and non-environmental data could improve malaria prediction.
Integrating smartphone applications into screening and identifying autism spectrum disorder (ASD) represents a promising and innovative frontier within healthcare. This forward-looking paper examines the current landscape of ASD screening apps, shedding light on their potential advantages and addressing and navigating significant challenges. One of the most compelling aspects of these apps lies in their potential to democratize access to ASD screening, effectively breaking down geographical barriers. By using the widespread availability of smartphones, these apps make it possible for individuals, caregivers, and healthcare providers to engage in early ASD screening from virtually anywhere. This accessibility is especially crucial in underserved areas or regions with limited access to specialized healthcare services. Moreover, these apps offer a degree of objectivity that traditional screening methods may need help to match. By relying on data-driven algorithms and machine learning, they can provide a more impartial assessment of a child's behavior, minimizing the potential for subjective bias. This objectivity, combined with the ability to monitor and assess a child's development over time, empowers caregivers with valuable insights into their child's progress. However, as with any technological advancement in healthcare, integrating smartphone apps for ASD screening is not without its share of ethical and privacy considerations. Ensuring informed consent is obtained, especially when collecting data from children, is complex and critical. Striking the right balance between collecting necessary data and protecting an individual's privacy requires careful thought and transparent communication. Additionally, the "digital divide" represents a challenge that needs to be acknowledged and addressed. Not all individuals and families have equal access to smartphones or the technological literacy required to use these apps effectively. This disparity in access must be considered when developing and implementing app-based screening solutions to prevent exacerbating existing healthcare inequalities. Nevertheless, the future of ASD screening apps holds significant promise. Advancements in technology, including integrating advanced sensors, wearables, augmented reality, and machine learning, can further enhance the accuracy and depth of screening. Interdisciplinary collaboration between researchers, developers, clinicians, and educators is crucial to ensure that these apps are effective, culturally sensitive, and user-friendly. Furthermore, integrating these apps into broader healthcare systems, including electronic health records and telehealth platforms, can streamline the screening process and enable a more seamless transition from screening to diagnosis and intervention.
Integrating technology and active learning methods into Laboratory activities would be a transformative educational experience to familiarize physical therapy (PT) students with STEM backgrounds and STEM-based new technologies. However, PT students struggle with technology and feel comfortable memorizing under expositive lectures. Thus, we described the difficulties, uncertainties, and advances observed by faculties on students and the perceptions about learning, satisfaction, and grades of students after implementing laboratory activities in a PT undergraduate course, which integrated surface-electromyography (sEMG) and kinematic technology combined with active learning methods. Six cohorts of PT students (n = 482) of a second-year PT course were included. The course had expositive lectures and seven laboratory activities. Students interpreted the evidence and addressed different motor control problems related to daily life movements. The difficulties, uncertainties, and advances observed by faculties on students, as well as the students' perceptions about learning, satisfaction with the course activities, and grades of students, were described. The number of students indicating that the methodology was "always" or "almost always," promoting creative, analytical, or critical thinking was 70.5% [61.0-88.0%]. Satisfaction with the whole course was 97.0% [93.0-98.0%]. Laboratory grades were linearly associated to course grades with a regression coefficient of 0.53 and 0.43 R-squared (p < 0.001). Integrating sEMG and kinematics technology with active learning into laboratory activities enhances students' engagement and understanding of human movement. This approach holds promises to improve teaching-learning processes, which were observed consistently across the cohorts of students.
Integration of hospice and palliative care principles within pharmacy curricula is essential to fill the need of pharmacist training in this growing specialty. A formalized assessment tool to evaluate skill development does not exist for student pharmacists specific to palliative care. The objective of this study was to develop a valid and reliable, palliative care-focused, performance-based assessment tool for student pharmacists. Eight academic palliative care (PC) pharmacists were recruited for the workgroup to perform domain development, validation, tool creation, and reliability testing for this performance-based assessment tool. Hospice and palliative care clinical pharmacist entrustable professional activities (EPAs) served as the framework. Content validity testing utilized content validity index and scale universal agreement (S-CVI/UA) to determine level of agreement for activities included in the tool. Student volunteers completed a standardized patient case and workgroup members served as raters during the reliability testing phase. Interrater reliability was measured through calculation of Fleiss Kappa scores for each activity. Out of 14 EPAs, nine were deemed "essential" to include in the tool. Thirty-four supporting activities for the nine essential EPAs were drafted. Two rounds of content validity testing were necessary to achieve S-CVI/UA of 0.9593. Consensus was reached from workgroup members for activities deemed necessary to include in the tool after questionnaire distribution utilizing a Fleiss Kappa cutoff >0.6. This validated tool will afford colleges and schools of pharmacy with PC curricula an opportunity to assess student achievement of PC-specific skills and evaluate curricular effectiveness.
Integration of inquiry-based approaches into curriculum is transforming the way science is taught and studied in undergraduate classrooms. Incorporating quantitative reasoning and mathematical skills into authentic biology undergraduate research projects has been shown to benefit students in developing various skills necessary for future scientists and to attract students to science, technology, engineering, and mathematics disciplines. While large-scale data analysis became an essential part of modern biological research, students have few opportunities to engage in analysis of large biological data sets. RNA-seq analysis, a tool that allows precise measurement of the level of gene expression for all genes in a genome, revolutionized molecular biology and provides ample opportunities for engaging students in authentic research. We developed, implemented, and assessed a series of authentic research laboratory exercises incorporating a large data RNA-seq analysis into an introductory undergraduate classroom. Our laboratory series is focused on analyzing gene expression changes in response to abiotic stress in maize seedlings; however, it could be easily adapted to the analysis of any other biological system with available RNA-seq data. Objective and subjective assessment of student learning demonstrated gains in understanding important biological concepts and in skills related to the process of science.
Integration of precision medicine (PM) competencies across the Medical Technology and Pharmaceutical industry is critical to enable industry professionals to understand and develop the skills needed to navigate the opportunities arising from rapid scientific and technological innovation in PM. Our objective was to identify the key competency domains required by industry professionals to enable them to upskill themselves in PM-related aspects of their roles. A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to PM competencies that were consistent across multiple disciplines and competency frameworks. A survey was used to confirm the applicability of these domains to the cross-functional and multi-disciplinary work practices of industry professionals. Companies were requested to trial the domains to determine their relevance in practice and feedback was obtained. Four PM-relevant domains were identified from the literature review: medical science and technology; translational and clinical application; governance and regulation and professional practice. Survey results refined these domains, and case studies within companies confirmed the potential for this framework to be used as an adjunct to current role specific competency frameworks to provide a specific focus on needed PM capabilities. The framework was well accepted by local industry as a supplement to role specific competency frameworks to provide a structure on how to integrate new and evolving technologies into their current workforce development planning and build a continuous learning and cross-disciplinary mindset.
Integration of sex and gender into health research is best practice for designing and conducting equitable, rigorous scientific research. Many evidence-based resources exist to support researchers in this endeavour, but such resources often remain underutilized as they are difficult to find, are not publicly accessible, or are specific to a particular research phase, context, or population. The development and evaluation of a repository of resources was deemed important to create an accessible platform for promoting sex- and gender-integration in health research. A rapid review was conducted of critical resources for conducting sex and gender health research. These were integrated into a prototype website design (the Genderful Research World; GRW) that provided an interactive digital landscape for researchers to access these resources. A pilot study evaluated the GRW website for applicability, desirability, and usability with an international sample of 31 health researchers from various disciplines and career stages. Quantitative data from the pilot study was summarized with descriptive statistics. Qualitative data was summarized narratively and used to identify concrete elements for improvement in a second design iteration. Results of the pilot study revealed that the GRW was considered user friendly and desirable by health researchers and helped them access relevant information. Feedback suggested that providing these resources in a playful way may enhance the experience of the user, particularly given the high 'desirability' scores and that users emphasized the interactive layout as being key to their intention to integrate it into their teaching endeavors. Key feedback from the pilot study (e.g., addition of resources specific to research with transgender populations, revision of website layout) was integrated into the current version of the website: www.genderfulresearchworld.com . The present research suggests a utility for a repository of resources for integrating sex and gender considerations into research, and that providing a logical, intuitive means of cataloguing and navigating such resources is critical for usability. The results of this study may inform the development of other novel researcher-directed resource curation efforts to address health equity issues and encourage and support health researchers to integrate a sex and gender perspective in their work.