Yoxas/statistical_literacy huggingface dataset:
The Grit scale (GS-12) is a widely used rating scale that assess passion and perseverance. The present study aimed to evaluate the reliability and validity of simple Chinese Version of Grit Scale (GS-SC) among Chinese adolescents. Seven hundred one primary school students were recruited as Sample 1. Item analysis and exploratory factor analysis (EFA) were conducted on Sample 1 to preliminarily examine the structure of the scale. Sample 2 consisted of 5,384 primary school students. Confirmatory factor analysis (CFA) and verification of reliability and validity were conducted on Sample 2 to establish a formal scale and further verify the psychometric properties by applying item response theory (IRT). EFA and CFA revealed a clear two-factor structure. The results demonstrated that the Simplified Chinese Version of Grit Scale had adequate internal consistency and re-test reliability. GS-CS also showed good criterion-validity with personality, self-control, effort regulation and academic achievement. Furthermore, all the items show a acceptable fit to the GRM and have good discrimination (ranging from 2.13 to 3.45) and moderate difficulty(ranging from-1.58 to 0.95). The reliability and validity of the GS-SC are good, indicating that the scale can be used as an effective tool for measuring the grit of primary school students in China.
The growing ability to provide online services has enabled the proliferation of exercise-based telehealth interventions; however, adoption in older adults may be impacted by low digital literacy and "technophobia." The aim of this study was to explore the experience of community and aged-care dwelling older adults following exercise-based telehealth services to provide insights that could guide future telehealth exercise delivery. Semi-structured interviews for qualitative analysis. Participants (age ≥60) who had completed at least one online exercise session from a registered health professional were recruited through a combination of purposeful and snowball sampling methods via their exercise facility or provider. A semi-structured interview guide was used by 2 interviewers to investigate participants' experiences and a "critical friends" approach used to identify common themes. Thirteen interviews with 21 participants were conducted from 2 different facilities. Analysis identified meaning units within 3 themes and subthemes. Technology subthemes related to digital confidence prior to telehealth and changes during interventions, as well as the usability of technology for telehealth. Clinical practice subthemes described the different motivations to exercise, perceived benefits of telehealth, important implications for practitioners, and perceptions of safety. The social connection theme related to the social benefits of telehealth. Older adults in our sample were technologically confident and capable of performing exercise sessions delivered via telehealth. They notice benefits from this form of exercise delivery however, prefer face-to-face exercise delivery.
The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible "Paediatric Intensive Care database" to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.
The growing demand for data scientists in both the global and Dutch labour markets has led to an increase in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skills of data scientists. This study addresses this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by 11 Dutch universities and an interuniversity combined program. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our analysis reveals that data processing, statistics, research, and ethics are the core competencies in Dutch data science and AI master programs. General universities tend to focus on research skills, while technical universities lean more towards IT and electronics skills. Broad-focussed data science and AI programs generally concentrate on data processing, information technology, electronics, and research, while subject-specific programs give priority to statistics and ethics. This research enhances the understanding of the diverse skills of Dutch data science graduates, providing valuable insights for employers, academic institutions, and prospective students.
The growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, but separately from, more traditional engineering courses, such as signal and systems analysis. We propose a new approach that systematically integrates signal processing and systems analysis with key techniques in machine learning. In the proposed course, the student obtains hands-on experience in applying algorithms that can be applied to practical problems of physiological signal conditioning, analysis and interpretation. This is achieved by exposing the student to a sequence of 4 applications-based modules that represent different biomedical engineering problems: human activity recognition from wearable devices, epileptic seizure detection, quantification of dynamic respiratory-cardiac coupling in humans under different conditions, and detection of sleep apnea episodes from heart rate variability data. Within each module, the student gains the experience of working with the data in question "from the ground up". We also introduce a general plan for assessment of student learning, and discuss the expected outcomes and limitations of this integrative approach to teaching.Clinical Relevance- The proposed course is targeted at biomedical engineering students at the senior undergraduate or first-year graduate level who are interested in learning how to analyze physiological signals. The course would also be suitable for clinician-scientists who have prior training in statistics with some exposure to engineering mathematics.
The growing number of breast cancer patients in Iran, following the lower referrals of women to screening centers after the outbreak of the COVID-19, suggests the need for designing virtual educational interventions to teach self-care methods to women. The aim of this study is to design a virtual training program for the prevention of breast cancer in women based on the steps of the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) educational design model. This developmental study will be conducted based on the steps of the ADDIE model. In the first step (analysis), a qualitative study, literature review, and a panel of experts will be conducted to analyze the situation (learners, content, platforms, and media for the electronic presentation of the program). In the design step, the learning objectives, educational strategies, and the way of program's implementation and evaluation will be specified. In the third step, not only are the content, storyboard, and educational program developed, but the pilot study is also conducted and formative assessment is performed. In the fourth step, the program will be provided to the audience and will be implemented as a preliminary program. In the final step, the final virtual education program for the prevention of breast cancer in women will be presented based on the results of the evaluation. Using a comprehensive and systematic educational design model can be a step toward making changes and encouraging innovations in breast cancer prevention education programs in women based on virtual education. Given the existing needs and conditions, this program can promote cancer preventive behaviors as much as possible, reduce the costs imposed on the family and healthcare systems, and lower the complications and mortality rate caused by the delayed diagnosis of the disease.
The growing presence of digital technologies in health care requires the health workforce to have proficiency in subjects such as informatics. This has implications in the education of nursing students, as their preparedness to use these technologies in clinical situations is something that course administrators need to consider. Thus, students' attitudes toward technology could be investigated to assess their needs regarding this proficiency. This study aims to investigate attitudes (enthusiasm and anxiety) toward technology among nursing students and to identify factors associated with those attitudes. Nursing students at 2 universities in Sweden and 1 university in Poland were invited to answer a questionnaire. Data about attitudes (anxiety and enthusiasm) toward technology, eHealth literacy, electronic device skills, and frequency of using electronic devices and sociodemographic data were collected. Descriptive statistics were used to characterize the data. The Spearman rank correlation coefficient and Mann-Whitney U test were used for statistical inferences. In total, 646 students answered the questionnaire-342 (52.9%) from the Swedish sites and 304 (47.1%) from the Polish site. It was observed that the students' technology enthusiasm (techEnthusiasm) was on the higher end of the Technophilia instrument (score range 1-5): 3.83 (SD 0.90), 3.62 (SD 0.94), and 4.04 (SD 0.78) for the whole sample, Swedish students, and Polish students, respectively. Technology anxiety (techAnxiety) was on the midrange of the Technophilia instrument: 2.48 (SD 0.96), 2.37 (SD 1), and 2.60 (SD 0.89) for the whole sample, Swedish students, and Polish students, respectively. Regarding techEnthusiasm among the nursing students, a negative correlation with age was found for the Swedish sample (P<.001; ρSwedish=-0.201) who were generally older than the Polish sample, and positive correlations with the eHealth Literacy Scale score (P<.001; ρall=0.265; ρSwedish=0.190; ρPolish=0.352) and with the perceived skill in using computer devices (P<.001; ρall=0.360; ρSwedish=0.341; ρPolish=0.309) were found for the Swedish, Polish, and total samples. Regarding techAnxiety among the nursing students, a positive correlation with age was found in the Swedish sample (P<.001; ρSwedish=0.184), and negative correlations with eHealth Literacy Scale score (P<.001; ρall=-0.196; ρSwedish=-0.262; ρPolish=-0.133) and with the perceived skill in using computer devices (P<.001; ρall=-0.209; ρSwedish=-0.347; ρPolish=-0.134) were found for the Swedish, Polish, and total samples and with the semester only for the Swedish sample (P<.001; ρSwedish=-0.124). Gender differences were found regarding techAnxiety in the Swedish sample, with women exhibiting a higher mean score than men (2.451, SD 1.014 and 1.987, SD 0.854, respectively). This study highlights nursing students' techEnthusiasm and techAnxiety, emphasizing correlations with various factors. With health care's increasing reliance on technology, integrating health technology-related topics into education is crucial for future professionals to address health care challenges effectively. RR2-10.2196/14643.
The growing recognition of the importance of simulation-based training has been a present focus of medical education curriculum planners. This study aims to design, develop, implement, and evaluate the educational outcomes of training modules for a list of essential skills that MBBS students in a tertiary care teaching hospital in South India need to learn as part of their competency-based MBBS curriculum. This sequential transformative mixed method study will be implemented in three phases: i) identification of essential skills for simulation-based training following the modified Delphi method, ii) development of skill training modules using ADDIE model of instructional design, implementation of the modules and collection of data on the educational outcomes and iii) a qualitative study involving in-depth interview and focus group discussion on understanding the potential for incorporating the modules into the present MBBS curriculum. Data on educational outcomes relevant to the study objectives will be collected using appropriate tools and analyzed using descriptive statistics and qualitative analytic methods. Phase one will tabulate the list of five essential skills and phase two educational outcomes will contain descriptive statistics on knowledge and skill acquisition, perception of simulation-based teaching and learning, and higher-order thinking skills. Phase three qualitative analysis will highlight the enabling and barrier aspects for incorporating this approach of simulation-based skill training within the current MBBS curriculum. The study outcomes will provide curriculum planners and educators insights into designing and implementing simulation-based skill training for undergraduate medical students. It will also help policymakers develop policies in medical education technologies to provide quality medical education and fulfill the objective of quality healthcare for all.
The growing significance of E-Pharmacy services in healthcare necessitates investigating the factors influencing users' behavioral intentions on these platforms. Comprehending these variables is imperative to maximize service provision, elevate customer satisfaction, and ultimately elevate healthcare accessibility and results. The main goal of this study is to explore the factors that shape consumers' inclination to use E-Pharmacy services, focusing on the framework provided by the Technology Acceptance Model. Additionally, we aimed to investigate how technological literacy plays a moderating role in this context, specifically within the South African setting. Using a sample of 480 South African clients, the Technology Acceptance Model (TAM) and partial least squares structural equation modeling (PLS-SEM) were used to investigate the factors influencing behavioral intention to use E-Pharmacy services. The findings underscore perceived usefulness, trust, performance expectancy, and social Influence as fundamental drivers influencing users' intentions to adopt E-Pharmacy systems. Additionally, the study shows that consumers' intention to use E-Pharmacy services is significantly and favorably affected by their desire to utilize such services. This suggests that individuals are more likely to act on positive intentions when they are interested in using E-Pharmacy platforms. Intriguingly, technological literacy has emerged as a moderating factor in the relationship between the intention to use and the actual behavior of utilizing E-Pharmacy platforms. This demonstrates the importance of consumers' technical knowledge and skills in bridging the intention-behavior gap and highlights the necessity of adapting treatments and instructional strategies to account for various levels of technological literacy. This study provides valuable insights into the complex relationships between factors influencing the acceptance of E-Pharmacy services in South Africa. This knowledge can have practical implications for lawmakers, developers of E-Pharmacy platforms, and healthcare professionals who aim to enhance user acceptance and utilization.
The growing trend of bilingual education between Chinese and English has contributed to a rise in the number of early bilingual children, who were exposed to L2 prior to formal language instruction of L1. The L2-L1 transfer effect in an L1-dominant environment has been well established. However, the threshold of L2 proficiency at which such transfer manifests remains unclear. This study investigated the behavioral and neural processes involved when manipulating phonemes in an auditory phonological task to uncover the transfer effect in young bilingual children. Sixty-two first graders from elementary schools in Taiwan were recruited in this study (29 Chinese monolinguals, 33 Chinese-English bilinguals). The brain activity was measured using fNIRS (functional near-infrared spectroscopy). Bilingual children showed right lateralization to process Chinese and left lateralization to process English, which supports more on the accommodation effect within the framework of the assimilation-accommodation hypothesis. Also, compared to monolinguals, bilingual children showed more bilateral frontal activation in Chinese, potentially reflecting a mixed influence from L2-L1 transfer effects and increased cognitive load of bilingual exposure. These results elucidate the developmental adjustments in the neural substrates associated with early bilingual exposure in phonological processing, offering valuable insights into the bilingual learning process.
The growing use of digital technologies (DTs) has a myriad of implications to socio-technical systems, which are not yet fully recognised. This paper investigates the contributions and drawbacks of DTs to resilient performance (RP), an aspect that so far has received less attention in comparison to others such as efficiency. To this end, a survey questionnaire was applied to 79 academics and practitioners linked to resilience engineering. Data analysis involved descriptive statistics and a thematic analysis of the open-text responses to the survey. Mixed impacts were identified, with 10 themes related to contributions and 16 to drawbacks. Regarding the contributions, the results highlighted the use of DTs for monitoring and anticipating system performance. Machine learning seems to be the most promising approach for this purpose. A key drawback is the need for developing new skills across the workforce so that they can make sense of the outputs of DTs and are aware of their strengths and weaknesses. The human role is expected to remain crucial for RP, which makes the current coordination difficulties with DTs even more important to address. A research agenda composed of five topics is proposed, encompassing description, prescription, and assessment. The agenda emphasizes the need for mapping the attributes or functionalities of DTs onto resilience concepts, models, and frameworks.
The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has become an important hub for technological innovation and economic development in China. With the growing demand for artificial intelligence (AI) and big data technology talents, it is essential to develop educational cooperation within the GBA to develop a talent pool that can meet the changing needs in the region. This paper focuses on the development of dynamic demand for AI talents and proposes a strategic planning framework for educational cooperation in the GBA. We use the research idea of common attributes and key chain clustering-factor association selection-analysis of the driving force and subordination among factors-the key characteristics of AI talents. Using collinear analysis of citations and grounded theory methods, an operational definition of the influencing factors of AI talent literacy characteristics is constructed. Using the Interpretative Structural Modeling(ISM) and MICMAC (Matrice d'Impacts Croises-Multipication Applique A Classement), analyze and identify the driving force and subordination of the influencing factors of key traits of talents, and present the combined effect of multi-level factors of key traits of talents. Combined with the educational differences and complementary advantages in the GBA, five strategies and seven implementation suggestions for the GBA's AI talent education cooperation plan are formulated to establish a collaborative ecosystem that promotes the growth and integration of AI in the GBA.
The gut microbiota is a key determinant of long-term health. Promoting maternal health literacy may enhance children well-being. Aim of the present study was to assess gut microbiota-related health literacy of Italian women and identify potential gaps in awareness. A cross-sectional survey study was conducted using an online questionnaire (17 questions) on determinants and long-term impact of infant gut microbiota. The survey targeted Italian pregnant women and mothers of children under 2 years old, and was distributed through various social media channels between September 28th and November 15th, 2022. A total score was calculated as the sum of positive answers. Data on demographics, pregnancy status, and pre-existing knowledge of the infant gut microbiota were also collected. Descriptive and inferential statistics were applied. The questionnaire was completed by 1076 women. Median total score was 9 [7-11]. The 81.7% of respondents declared prior knowledge of the gut microbiota. The internet was among the most commonly cited primary sources of information. Independent predictors of total score were having a university degree (B = 0.656, p = 0.002) and prior knowledge (B = 2.246, p < 0.001). Conversely, older age was associated with lower total scores (B = -0.092, p < 0.001). The least known determinants of infant gut microbiota were gestational BMI, prematurity, mode of delivery and NICU stay. Pregnant women failed to recognize the role of breastfeeding in the development of infant gut microbiota more frequently than non-pregnant women. The 97.5% of participants reported increased interest in the gut microbiota, with heightened interest associated with prior knowledge. Our study revealed a moderate level of knowledge about infant gut microbiota among respondents, emphasizing the positive impact of prior knowledge on understanding and interest. Targeted educational interventions are needed to address awareness gaps, especially concerning the influence of breastfeeding on infant gut microbiota. Healthcare providers have the potential to enhance women's knowledge and awareness of this topic.