Yoxas/statistical_literacy huggingface dataset:
The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.
The current approach to increasing diversity in medical education fails to consider local community demographics when determining medical school matriculation. We propose that medical schools better reflect their surrounding community, both because racially/ethnically concordant physicians have been shown to provide better care and to repair the historical and current racist impacts of these institutions that have criminalized, displaced, and excluded local Black and Brown communities. In this study, we used geospatial analysis to determine that medical school enrollments generally fail to reflect their surrounding community, represented as their core-based statistical area, within which the individual medical schools reside.
The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure-activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.
The current global pandemic has caused unprecedented strain on critical care resources, creating an urgency for global critical care education programs. Learning needs assessment is a core element of designing effective, targeted educational interventions. In theory, multimodal methods are preferred to assess both perceived and unperceived learning needs in diverse, interprofessional groups, but a robust design has rarely been reported. Little is known about the best approach to determine the learning needs of international critical care professionals. We conducted multimodal learning needs assessment in a pilot group of critical care professionals in China using combined quantitative and qualitative methods. The assessments consisted of three phases: 1) Twenty statements describing essential entrustable professional activities (EPAs) were generated by a panel of critical care education experts using a Delphi method. 2) Eleven Chinese critical care professionals participating in a planned education program were asked to rank-order the statements according to their perceived learning priority using Q methodology. By-person factor analysis was used to study the typology of the opinions, and post-ranking focus group interviews were employed to qualitatively explore participants' reasoning of their rankings. 3) To identify additional unperceived learning needs, daily practice habits were audited using information from medical and nursing records for 3 months. Factor analysis of the rank-ordered statements revealed three learning need patterns with consensual and divergent opinions. All participants expressed significant interest in further education on organ support and disease management, moderate interest in quality improvement topics, and relatively low interest in communication skills. Interest in learning procedure/resuscitation skills varied. The chart audit revealed suboptimal adherence to several evidence-based practices and under-perceived practice gaps in patient-centered communication, daily assessment of antimicrobial therapy discontinuation, spontaneous breathing trial, and device discontinuation. We described an effective mixed-methods assessment to determine the learning needs of an international, interprofessional critical care team. The Q survey and focus group interviews prioritized and categorized perceived learning needs. The chart audit identified additional practice gaps that were not identified by the learners. Multimodal methods can be employed in cross-cultural scenarios to customize and better target medical education curricula.
The Current Insights feature is designed to introduce life science educators and researchers to current articles of interest in other social science and education journals. In this installment, I highlight three recent studies from the fields of psychology and science, technology, engineering, and mathematics education that can inform life science education. The first characterizes how instructor beliefs about intelligence are communicated to students in the classroom. The second explores how instructor identity as a researcher may lead to different types of teaching identities. The third presents an alternative way to characterize students' success that is based in Latinx college student values.
The current interpretation of the observed late time cooling of transiently accreting neutron stars in low-mass x-ray binaries during quiescence requires the suppression of neutron superfluidity in their crust at variance with recent ab initio many-body calculations of dense matter. Focusing on the two emblematic sources KS 1731-260 and MXB 1659-29, we show that their thermal evolution can be naturally explained by considering the existence of a neutron superflow driven by the pinning of quantized vortices. Under such circumstances, we find that the neutron superfluid can be in a gapless state in which the specific heat is dramatically increased compared to that in the classical BCS state assumed so far, thus delaying the thermal relaxation of the crust. We perform neutron-star cooling simulations taking into account gapless superfluidity, and we obtain excellent fits to the data, thus reconciling astrophysical observations with microscopic theories. The imprint of gapless superfluidity on other observable phenomena is briefly discussed.
The current literature about pharmaceutical and personal care compounds (PPCPs) focuses on identifying their concentration and toxicological risk both in surface water and in wastewater. However, the influence of urban areas (population ageing, income level, hospitals and others) has not yet been analysed. Knowing how a population (and its facilities) affects PPCPs' presence in wastewater is important to identify the conditions that are responsible for their presence. In this work, the influence of water consumption, population ageing, income level, hospitals and nursing homes on the anti-inflammatory concentration have been analysed. To fill the gap between the quantitative data on PPCPs' concentration and the qualitative reasoning of the influence of urban areas on the anti-inflammatory concentration, the use of fuzzy-set qualitative comparative analysis (fsQCA) is proposed. The fsQCA results are presented as recipes that show the different causal combinations of conditions that explain the presence of anti-inflammatories in wastewater. Using fsQCA for urban wastewater management with the aim of explaining the presence of anti-inflammatories in wastewater treatment plants (WWTPs) is a novelty in the literature. The results obtained here show the influence of water consumption (WATCON), hospitals (HOSP) and population ageing (POPAG) as the main conditions for the anti-inflammatory concentration in Valèncian wastewater. Specifically, these conditions are present in all the recipes obtained with consistency of 99%. Through the results obtained, it would be possible to identify that HOSP are the main facilities that discharge anti-inflammatories into urban wastewater. Hence, the necessity of preventive measures to avoid the anti-inflammatory discharge into water bodies has been showed. Furthermore, under a methodological point of view, this work highlights the eligibility of fsQCA as a wastewater cycle management tool.
The current literature indicates that routine evaluation of preoperative anxiety, its determinants, and patient-specific concerns is universally advocated. This aligns with the increasingly acknowledged importance of prehabilitation - a comprehensive process preparing patients for surgery. A crucial component of prehabilitation is assessing patients' mental health. Recommendations for psychological evaluations in prehabilitation encompass, inter alia, determining the severity of anxiety. This work builds on a 2019 article, which presented scales for preoperative anxiety assessment: the State Trait Anxiety Inventory (STAI), the Hospital Anxiety and Depression Scale (HADS), the Amsterdam Preoperative Anxiety and Information Scale (APAIS), and the Visual Analogue Scale (VAS). This article extends the possibilities of preoperative anxiety assessment by introducing four additional methods: the Surgical Fear Questionnaire (SFQ), the Anxiety Specific to Surgery Questionnaire (ASSQ), the Surgical Anxiety Questionnaire (SAQ), and Anesthesia- and Surgery-dependent Preoperative Anxiety (ASPA). The authors provide comprehensive details on these instruments, including scoring, interpretation, availability, and usefulness both in scientific research and clinical practice. The authors also provide the data on the availability of Polish versions of the presented methods and preliminary data on the reliability of SFQ in patients awaiting cardiac surgery. This review seems relevant for professionals in multiple disciplines, including anesthesiology, surgery, clinical psychology, nursing, primary care and notably prehabilitation. It emphasizes the necessity of individualizing anxiety assessment and acknowledging patient subjectivity, which the presented methods facilitate through a thorough evaluation of specific patient concerns. The literature review also identifies concerns and future research avenues in this area. The importance of qualitative studies and those evaluating prehabilitation intervention is emphasized.
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset-PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
The current novel coronavirus (nCoV) pandemic, COVID-19, was first reported in December 2019 in Wuhan, China, and has spread globally, causing startling loss of life, stalling the global economy, and disrupting social life. One of the challenges to contain COVID-19 is convincing people to adopt personal hygiene, social distancing, and self-quarantine practices that are related to knowledge, attitudes, and practices (KAP) of the residents of respective countries. Bangladesh, a densely populated country with a fast-growing economy and moderate literacy rate, has shown many hiccups in its efforts to implement COVID-19 policies. Understanding KAP may help policy makers produce informed decisions. This study assessed KAP in relation to COVID-19 in Bangladesh. An online survey using a pre-tested questionnaire conducted in late March 2020 attained 1,837 responses across Bangladesh. Ultimately, 1,589 completed responses were included in a statistical analysis to calculate KAP scores and their interrelations with sociodemographic variables. The overall KAP was poor, with only 33% of the participants demonstrating good knowledge, whereas 52.4% and 44.8% of the subjects showed good attitudes and practices, respectively. Sociodemographic factors had strong bearings on the KAP scores. Significantly higher KAP scores were evident in females over males, among aged 45 years and older over younger participants, and among retired workers and homemakers over students and public service employees. This study indicated a panic fuelled by poor understanding of COVID-19 associated facts and the need for the government to ensure more granular and targeted awareness campaigns in a transparent and factual manner to foster public confidence and ensure more meaningful public participation in mitigation measures. This study provides a KAP baseline regarding COVID-19 among Bangladeshis.
The current paper explores the relationship between technological knowledge, beliefs about technology use in education, beliefs about the limits of technology use with respect to children with disabilities, self-efficacy beliefs, and their effects on technology use or teacher practices in the special education setting regarding mathematics education. Research was conducted via an online questionnaire. A total of 83 teachers working in the field of special education answered the questionnaire. The answers were analyzed via quantitative and qualitative analyses. In addition, correlational analyses were conducted. A prediction model that included all the aforementioned variables was tested. Our regression analysis evidenced the strong predictive value of technology-related knowledge with respect to technology integration practices but not toward the other models that included beliefs. Through mediation analysis, we found that technology-related knowledge mediates the relationship between self-efficacy and technology integration practices. The qualitative findings provided information on the interplay between knowledge, practice, and beliefs that are contextualized; on the specificity of technology-related knowledge. Implications are discussed with reference to factors important for the professional development of teachers with regard to technology integration.
The current paradigm of competency-based medical education and learner-centredness requires learners to take an active role in their training. However, deliberate and planned continual assessment and performance improvement is hindered by the fragmented nature of many medical training programs. Attempts to bridge this continuity gap between supervision and feedback through learner handover have been controversial. Learning plans are an alternate educational tool that helps trainees identify their learning needs and facilitate longitudinal assessment by providing supervisors with a roadmap of their goals. Informed by self-regulated learning theory, learning plans may be the answer to track trainees' progress along their learning trajectory. The purpose of this study is to summarise the literature regarding learning plan use specifically in undergraduate medical education and explore the student's role in all stages of learning plan development and implementation. Following Arksey and O'Malley's framework, a scoping review will be conducted to explore the use of learning plans in undergraduate medical education. Literature searches will be conducted using multiple databases by a librarian with expertise in scoping reviews. Through an iterative process, inclusion and exclusion criteria will be developed and a data extraction form refined. Data will be analysed using quantitative and qualitative content analyses. By summarising the literature on learning plan use in undergraduate medical education, this study aims to better understand how to support self-regulated learning in undergraduate medical education. The results from this project will inform future scholarly work in competency-based medical education at the undergraduate level and have implications for improving feedback and supporting learners at all levels of competence. Open Science Framework osf.io/wvzbx.
The current study aimed to conduct a systematic review and meta-analysis of self-management interventions for teaching daily living skills to autistic individuals. This study accessed the corresponding studies by doing a search in six databases. 14 articles and one dissertation met the inclusion criteria. The included studies were first analyzed descriptively and coded according to quality indicators using What Works Clearinghouse (WWC) standards. Second, the effect sizes of the included studies were calculated using two different effect size measures (i.e., Tau-U and performance-criteria-based effect size values [PCES]). Third, these analyses were also conducted for generalization and maintenance data. Of 15 studies included in this review, nine met the WWC standards with and without reservations. Tau-U analyses were conducted for 14 studies, whereas PCES values were calculated for only eight studies with mastery criteria. The findings indicated that the self-management interventions had a .93 CI95 (.80, 1) overall effect size for Tau-U with a very large effect. On the other hand, the overall effect size for the PCES values indicated a moderate effect with .99. The weighted effect sizes in generalization and maintenance phases were very large for Tau-U; however, moderate to high effects for PCES. Although self-management interventions showed diversity, one of the domains of daily living skills (i.e., community living skills) has not been studied in the field. Notably, among the studies in our review, the last ones are from 2019. Detailed findings from descriptive analyses and two different effect size calculations are discussed, and recommendations for future studies are given.
The current study aims to examine lecturer readiness for English Medium Instruction (EMI) in higher educational institutions and the contextual influences of gender, age, academic qualification, teaching experience, EMI course teaching involvement, and EMI training. A quantitative research design was employed, and a survey questionnaire was completed by 227 lecturers (out of 250 invited participants) from private universities in Klang Valley, Malaysia to gauge self-ratings of personal knowledge, skills, abilities, and attitudes in educating EMI courses. The collected data were subsequently analysed via the Statistical Package for Social Sciences (SPSS) version 27.0 software before revealing the findings from the inferential statistics of the t-test and one-way analysis of variance (ANOVA) on lecturers' gender, age, academic qualification, teaching experience, EMI course teaching involvement, and EMI training. Resultantly, the important role of lecturers' knowledge, understanding, skills, abilities, and attitudes was highlighted to further enhance intercultural communicative competence in managing the increasingly diversified student body in EMI classrooms.