Yoxas/statistical_literacy huggingface dataset:
An equitable education system is essential for all students to acquire the knowledge and skills necessary to become productive members of society. Pre-service teachers in education play a vital role in fostering equitable educational practices. This study aimed to measure the association between the social media-based community of inquiry and pre-service teachers' intentions toward social justice and equity in education. It focused on pre-service teachers enrolled in the education departments of universities in Gilgit Baltistan (GB), Pakistan. Census sampling was used to include all students enrolled in teacher education departments across universities in GB. The research utilized a multi-wave survey design, beginning with a baseline survey to assess pre-service teachers' presence on social media. This information guided the design of a community of inquiry on social media centered on the theme of social justice and equity in education. After 4 months, a second survey was conducted to measure the association between the community of inquiry and pre-service teachers' intentions toward social justice and equity. For data analysis, the study employed the partial least squares-consistent structural equation modeling (PLSc-SEM) approach. The novelty of the study lies in integrating the community of inquiry framework with the theory of planned behavior. We found a significant and positive association between the social media-based community of inquiry and pre-service teachers' attitudes, subjective norms, and perceived behavioral control regarding their intentions to implement social justice and equity in education. These findings hold the potential for developing prospective teachers and educational leadership with a strong focus on equity. Future research could explore creating a community of inquiry for pre-service teachers to enhance their mindset and skills for inclusive education. This aligns with the broader objective of achieving Sustainable Development Goal 4 (SDG 4) on fostering a more inclusive and equitable educational environment.
An estimated 609,820 child-rearing adults in 2023 died from advanced cancer, affecting 153,675 dependent children. Although children are known to suffer significant distress when a parent is diagnosed with cancer, few studies have described parents' views of their adolescent's behavioral response to their advanced cancer or what the parent did to interpret or manage that response. To describe patient-reported concerns about their adolescent and how they responded to their adolescent's behavior. Single occasion interviews were administered to 6 adolescent-rearing parents with Stage IV cancer. Interviews were analyzed using inductive content analysis by trained coders. Trustworthiness of results was protected through peer debriefing, coding to consensus, and maintaining an audit trail. The core construct that explained study data was Being There without Taking Over, comprised of 4 domains: Struggling to Read My Child, Attempting to Talk with My Child about My Cancer, Trying to Maintain Optimism, and Understanding My Child. Parents were deeply concerned about the impact of their advanced cancer on their adolescent but were unable to distinguish between cancer-related distress and adolescent angst. They feared initiating cancer-related discussions and struggled with their own feelings of guilt and parental inadequacy but did not turn to professionals for help. Adolescent-rearing patients with advanced disease need to be triaged into services that offer a framework from which parents can interpret their child's behavior and learn ways to have adolescent-appropriate conversations about the cancer. Such services should also help parents gain skills to manage feelings of parental inadequacy and guilt. In the absence of services, parents struggle and do not know how to interpret and respond to their adolescent's cancer-related behavior.
An exclusive breastfeeding period is a time when the infant's feeding depends on only breast milk. Inadequate maternal nutrition during this period could lead to insufficient infant feeding, which can further lead to childhood undernutrition and developmental restrictions. Evidently, the burden of maternal undernutrition was higher in resource-limited countries, including Ethiopia. This study aimed to assess the proportion of undernutrition among exclusive breastfeeding mothers and its associated factors in Southwest Ethiopia. The study used a community-based cross-sectional design. The study was conducted among 442 nursing mothers from 10 to 30 June 2022. The participants were selected using multistage sampling techniques. An interviewer-administered structured questionnaire was used to collect information. Statistical software EpiData version 3.1 and SPSS version 20 were used for data entry and analysis, respectively. The factors associated with undernutrition were identified using a binary logistic regression analysis. In the bivariable analysis, a p-value of less than 0.25 was used to include the variable in the multivariable analysis, whereas p-value less than 0.05 was an odds ratio used to declare an independent association at a 95% confidence interval. The proportion of undernutrition among the participants was found to be 24.8% in the study area. Poor intake of extra meals (adjusted odds ratio = 2.104; 95% confidence interval: 1.208, 3.664), poor dietary diversity habits (adjusted odds ratio = 3.605; 95% confidence interval: 2.112, 6.153), a lack of nutrition information (adjusted odds ratio = 1.853; 95% confidence interval: 1.070, 3.212), and household food insecurity (adjusted odds ratio = 4.424; 95% confidence interval: 2.639, 7.417) were identified as factors enhancing undernutrition among exclusive breastfeeding mothers in the area. A quarter of exclusive breastfeeding mothers were undernourished in the area. Poor dietary diversity habits, poor intake of extra meals, a lack of nutrition information, and household food insecurity were found to be the factors determining undernutrition. Hence, it is important to reinforce nutrition intervention programs, including maternal nutrition education and counseling.
An illness script is a specific script format geared to represent patient-oriented clinical knowledge organized around enabling conditions, faults (i.e., pathophysiological process), and consequences. Generative artificial intelligence (AI) stands out as an educational aid in continuing medical education. The effortless creation of a typical illness script by generative AI could help the comprehension of key features of diseases and increase diagnostic accuracy. No systematic summary of specific examples of illness scripts has been reported since illness scripts are unique to each physician. This study investigated whether generative AI can generate illness scripts. We utilized ChatGPT-4, a generative AI, to create illness scripts for 184 diseases based on the diseases and conditions integral to the National Model Core Curriculum in Japan for undergraduate medical education (2022 revised edition) and primary care specialist training in Japan. Three physicians applied a three-tier grading scale: "A" denotes that the content of each disease's illness script proves sufficient for training medical students, "B" denotes that it is partially lacking but acceptable, and "C" denotes that it is deficient in multiple respects. By leveraging ChatGPT-4, we successfully generated each component of the illness script for 184 diseases without any omission. The illness scripts received "A," "B," and "C" ratings of 56.0% (103/184), 28.3% (52/184), and 15.8% (29/184), respectively. Useful illness scripts were seamlessly and instantaneously created using ChatGPT-4 by employing prompts appropriate for medical students. The technology-driven illness script is a valuable tool for introducing medical students to key features of diseases.
An important first step in talent development in science, technology, engineering, and mathematics (STEM) is getting individuals excited about STEM. Females, in particular, are underrepresented in many STEM fields. Since girls' interest in STEM declines in adolescence, interventions should begin in secondary education at the latest. One appropriate intervention is (online) mentoring. Although its short-term effectiveness has been demonstrated for proximal outcomes during secondary education (e.g., positive changes in elective intentions in STEM), studies of the long-term effectiveness of STEM mentoring provided during secondary education-especially for real-life choices of university STEM majors and professions-are lacking. In our study, we examine females' real-life decisions about university majors and entering professions made years after they had participated in an online mentoring program (CyberMentor) during secondary education. The program's proximal positive influence on girls' elective intentions in STEM and certainty about career plans during secondary education had previously been demonstrated in several studies with pre-post-test waitlist control group designs. Specifically, we compared the choices that former mentees (n = 410) made about university majors and entering professions several years after program participation with (1) females of their age cohort and (2) females of a group of girls comparably interested in STEM who had signed up for the program but then not participated (n = 71). Further, we examined the explanatory contribution to these later career-path-relevant, real-life choices based on (1) mentees' baseline conditions prior to entering the program (e.g., elective intentions in STEM), (2) successful 1-year program participation, and (3) multiyear program participation. Findings indicate positive long-term effects of the program in all areas investigated.
An important part of scientific training in undergraduate curriculum involves teaching students how to effectively communicate in science. Scientific writing and oral presentations are important parts of most science classes. The same cannot be said about a new and emerging aspect of many recent scientific articles: graphical abstracts. In recent years, many scientific journals have adopted graphical abstracts as a way to capture both the scientific audience and increase visibility on social media platforms. Graphical abstracts are becoming the norm for many journals; however, there is no equivalent training in undergraduate classes that teaches students the intricate skills of efficient graphical design. In this paper, we share our course design and discuss how students can be taught to design better experiments and excel in communicating their research findings through graphical abstracts.
An important part of surgical training in ophthalmology is understanding how to proficiently perform cataract surgery. Operating skill in cataract surgery is typically assessed by real-time or video-based expert review using a rating scale. This is time-consuming, subjective and labour-intensive. A typical trainee graduates with over 100 complete surgeries, each of which requires review by the surgical educators. Due to the consistently repetitive nature of this task, it lends itself well to machine learning-based evaluation. Recent studies utilize deep learning models trained on tool motion trajectories obtained using additional equipment or robotic systems. However, the process of tool recognition by extracting frames from the videos to perform phase recognition followed by skill assessment is exhaustive. This project proposes a deep learning model for skill evaluation using raw surgery videos that is cost-effective and end-to-end trainable. An advanced ensemble of convolutional neural network models is leveraged to model technical skills in cataract surgeries and is evaluated using a large dataset comprising almost 200 surgical trials. The highest accuracy of 0.8494 is observed on the phacoemulsification step data. Our model yielded an average accuracy of 0.8200 and an average AUC score of 0.8800 for all four phase datasets of cataract surgery proving its robustness against different data. The proposed ensemble model with 2D and 3D convolutional neural networks demonstrated a promising result without using tool motion trajectories to evaluate surgery expertise.
An important prerequisite for the success of the digitisation of the healthcare system are risk-literate users. Risk literacy means the ability to weigh potential benefits and harms of digital technologies and information, to use digital services critically, and to understand statistical evidence. How do people find reliable and comprehensible health information on the Internet? How can they better assess the quality of algorithmic decision systems? This narrative contribution describes two approaches that show how the competence to make informed decisions can be promoted.Evidence-based and reliable health information exists on the Internet but must be distinguished from a large amount of unreliable information. Various institutions in the German-speaking world have therefore provided guidance to help laypersons make informed decisions. The Harding Center for Risk Literacy in Potsdam, for example, has developed a decision tree ("fast-and-frugal tree"). When dealing with algorithms, natural frequency trees (NFTs) can help to assess the quality and fairness of an algorithmic decision system.Independent of reliable and comprehensible digital health services, further tools for laypersons to assess information and algorithms should be developed and provided. These tools can also be included in institutional training programmes for the promotion of digital literacy. This would be an important step towards the success of digitisation in prevention and health promotion.
An important publication titled The State of Doctor of Nursing Practice Education in 2022 reports on a national study that examined the current state of DNP graduates both in practice and in academia. The key findings, conclusions, and recommendations of the 2022 report provide an updated snapshot of current DNP education and practice along with recommendations for curricula and utilization of DNP graduates. This article focuses on the DNP Project as an integral component of the DNP curriculum in developing clinical leaders and scholars to improve healthcare outcomes and significantly impact healthcare transformation. Many publications have addressed the significant variability of the DNP Project among programs. This AACN report further underscores variability among DNP Projects regarding purpose, focus, format, development, evaluation, dissemination, and contribution to practice. The purpose of this article is to provide a perspective for nurse leaders and faculty about selected key findings, conclusions, and recommendations from this 2022 report that relate to DNP Projects as a means to improve the value and rigor of DNP Projects and decrease the variability among DNP programs.
An important yet challenging aspect of atomistic materials modeling is reconciling experimental and computational results. Conventional approaches involve generating numerous configurations through molecular dynamics or Monte Carlo structure optimization and selecting the one with the closest match to experiment. However, this inefficient process is not guaranteed to succeed. We introduce a general method to combine atomistic machine learning (ML) with experimental observables that produces atomistic structures compatible with experiment by design. We use this approach in combination with grand-canonical Monte Carlo within a modified Hamiltonian formalism, to generate configurations that agree with experimental data and are chemically sound (low in energy). We apply our approach to understand the atomistic structure of oxygenated amorphous carbon (a-COx), an intriguing carbon-based material, to answer the question of how much oxygen can be added to carbon before it fully decomposes into CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy (XPS) model trained from GW and density functional theory (DFT) data, in conjunction with an ML interatomic potential, we identify a-COx structures compliant with experimental XPS predictions that are also energetically favorable with respect to DFT. Employing a network analysis, we accurately deconvolve the XPS spectrum into motif contributions, both revealing the inaccuracies inherent to experimental XPS interpretation and granting us atomistic insight into the structure of a-COx. This method generalizes to multiple experimental observables and allows for the elucidation of the atomistic structure of materials directly from experimental data, thereby enabling experiment-driven materials modeling with a degree of realism previously out of reach.
An increasing body of research has examined the link between biodiversity of birds and human mental health, but most studies only use cross-sectional data. Few studies have used longitudinal or repeated cross-sectional data to investigate the mental health benefits of bird diversity. The aim of this study is to analyse the relationship between bird diversity and mental health at the national level using a unique repeated cross-sectional dataset. I used repeated cross-sectional health data from the German National Cohort health study, collected between March, 2014, and September, 2019, and annual bird citizen science data to investigate the effects of bird-diversity exposure on mental health. Mental health was measured using the summary score of the Patient Health Questionnaire depression module 9 (SumPHQ) and the Short Form Health Survey-12 Mental Health Component Scale. As a proxy for bird diversity, I created a unique indicator called reporting-rate richness and combined it with the health data. Reporting-rate richness measures the number of bird species within postcode areas across Germany in probabilities while accounting for variation in survey efforts. Alternative indicators of bird diversity, such as bird-species richness or abundance, were also calculated. Associations between bird diversity and mental health were estimated using linear regression with region and time fixed effects, adjusted for a range of sociodemographic and environmental confounders and spatial autocorrelation. Interaction terms between income levels and reporting-rate richness were also analysed to examine the moderating effect of socioeconomic status. I did the analyses for an unbalanced (n=176 362) and balanced (n=125 423) dataset, with the balanced dataset comprising only regions (postcode areas) in which health data were available for each year. The linear fixed-effects regression analysis indicated a significant negative association between reporting-rate richness and SumPHQ, as observed in both the unbalanced dataset (β -0·02, p=0·017) and the balanced dataset (β -0·03, p=0·0037). Similarly, regression results with both datasets showed a positive relationship between reporting-rate richness and Mental Health Component Scale (MCS; unbalanced β 0·02, p=0·0086; balanced β 0·03, p=0·0018). The moderator analyses revealed a significant influence of socioeconomic status on the relationship between reporting-rate richness and mental health. The robustness of these findings was confirmed through sensitivity analyses. The results suggest that a greater likelihood of having many different bird species in a person's area of residence might positively contribute to mental health, especially for people with lower socioeconomic status. These findings could have implications for biodiversity conservation and health policy decisions, as governments are facing challenges such as global biodiversity loss and growing public mental health problems. None.
An increasing shift towards non-communicable diseases and an existing high surgical burden of disease in low-middle-income countries (LMICs), such as Pakistan, has driven the need for implementing Enhanced Recovery After Surgery (ERAS), a safe and cost-effective surgical service aimed at improving patient recovery and reducing post-operative complications. Despite countless benefits, there are few ERAS programs throughout Pakistan and sparse literature on healthcare professionals' views regarding ERAS. Without a deep understanding of healthcare professionals' perspectives on ERAS, underlying barriers and facilitators to a long-term ERAS implementation cannot be addressed and improved upon. Therefore, the purpose of this study is to better understand the knowledge, implementation, and perception of ERAS from the perspective of healthcare professionals across Pakistan. Upon receiving ethical approval from the McGill University Health Center (MUHC), a previously validated questionnaire was modified and a 29-question survey was developed and disseminated to healthcare professionals practising in Pakistan. Quantitative data was analyzed using descriptive statistics and potential correlations that exist between the implementation of ERAS and the participants' gender, employment setting, and surgical specialty were investigated using the chi-squared analysis with a p-value of 0.05 as the cutoff. A total of 49 participants responded to this survey of whom 34 (69%) worked at a tertiary care teaching hospital whereas 15 (31%) worked at a private hospital. Surprisingly, 42 (85%) participants expressed being aware of the ERAS guidelines with only 30 (61%) either strongly agreeing or agreeing to successfully implementing ERAS into practice. The largest discrepancies in implementation were seen when discussing specific elements of the ERAS guidelines such as preoperative carbohydrate loading, practicing prolonged preoperative fasting, performing mechanical bowel preparation, performing active patient warming, and early postoperative removal of Foley's catheter. Surgeons employed at a private institution were more likely to discuss postoperative pain management and control, less likely to utilize prolonged fasting, more likely to perform regular body temperature monitoring, more likely to practice providing chewing gum to patients postoperatively, and more likely to perform early removal of the Foley's catheter. An understanding of ERAS, the implementation of various elements, and a positive attitude toward its benefits definitely seem to be prevalent among healthcare professionals in Pakistan. However, key barriers and enablers specific to the underlying healthcare environment seem to be hindering the long-term successful implementation of ERAS across Pakistan. It is crucial for future studies to explore these barriers in further detail and involve the perspective of these key stakeholders to help enhance long-term ERAS adoption.