Yoxas/statistical_literacy huggingface dataset:
The process of teaching mathematics represents a challenge for primary education, due to the different perspectives and disciplines involved. In addition, as an active and flexible process, it requires feedback on what the students actually achieved. An analysis of the different learning and development outcomes allows the teacher to understand the mathematical content and the method of teaching it in the classroom, with the objective of promoting the students' conceptual development. The objective of our study was to analyze the general skills for problem solving which students developed, by applying dynamic evaluation. A verification method was used to identify the students' abilities and difficulties. A protocol for evaluating the process of solving mathematical problems was organized. The assessment included four simple problems and four complex ones. The participants were 15 students in the third grade of primary school attending a private school located in Mexico City. The results showed that the students identified the types of mathematical operations (addition, subtraction, multiplication, and division) required to solve the problems as their objective. Therefore, their preparation of a solution plan, its execution, and its verification were based only on empirical thinking and quantitative information. We concluded that problem-solving is an intellectual activity that requires conceptual development to carry out a solution plan, execute it, and verify it, in addition to the main objective of answering the question posed by the problem. We propose that these characteristics be included in the organization of mathematics teaching in order to develop mathematical thinking.
The Professional Identity Essay (PIE) is a theory and evidence-based Medical Professional Identity Formation (MPIF) measure. We describe trajectories of PIE-measured MPIF over a 4-year US medical school curriculum. Students write PIEs at medical school orientation, clinical clerkships orientation, and post-advanced (near graduation) clerkship. A trained evaluator assigns an overall stage score to narrative responses to nine PIE prompts (inter-rater ICC 0.83, 95% CI [0.57 - 0.96], intra-rater ICC 0.85). Distribution of PIE stage scores across time points were analyzed in the aggregate and individual students were classified as Increase, Stable (no score change) or Decrease based on the trajectories of PIE stage scores over time. 202 students completed 592 PIEs from 2018-2023. There was a significant change in the proportion of PIEs in stages over time (X2 84.40, p < 0.001), 47% (n = 95) students were categorized in the Increase trajectory, 45.5% (n = 92) as Stable and 7.4% (n = 15) as Decrease. Older age and time-predicted stage scores change within trajectories (p < 0.05). Medical students' PIE stage scores increase over time with three distinctive trajectories. Further study is needed to explore the utility of this method for formative assessment, program evaluation, and MPIF research.
The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy. The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC). AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910. The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner. This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.
The prognostic relevance of fetal/early postnatal magnetic resonance (MR) imaging (MRI) isolated "minor" lesions in congenital cytomegalovirus (CMV) infection is still unclear, because of the heterogeneity of previously reported case series. The aim of this study was to report the imaging and long-term clinical follow-up data on a relatively large cohort of infected fetuses. Among 140 CMV-infected fetuses from a single-center 12-year-long fetal MRI database, cases that showed isolated "minor" lesions at MRI, mainly represented by polar temporal lesions, were selected. MRI features were described, and clinical follow-up information was collected through consultation of medical records and telephone interview to establish the auditory and neurological outcome of each patient. Thirty-six cases were included in the study. The frequency of "minor" lesions increased progressively with ongoing gestational age in cases who underwent serial MR examination; 31% of cases were symptomatic at birth for unilateral altered auditory brainstem response. At long-term clinical follow-up, performed in 35 patients at a mean age of 64.5 months (range: 25 to 138), 43% of patients were asymptomatic and 57% presented with mild/moderate disability including hearing loss (34%), unilateral in all cases but one (therefore classified as severe), and/or minor cognitive and behavioral disorders (49%). Descriptive analysis of the type and modality of occurrence of "minor" lesions suggests performing serial fetal/postnatal MR examinations not to miss later-onset lesions. Follow-up data from the present cohort, combined with maternal/fetal factors and serologic-laboratory parameters may contribute to improve prenatal and neonatal period counselling skills.
The Program for the Education and Enrichment of Relational Skills (PEERS®), designed to enhance social skills and relationships for individuals with autism spectrum disorder (ASD) and their caregivers, has primarily been implemented with older children and adolescents ages 11-19, leaving a gap in research on its effectiveness in young children. This scoping review assesses evidence of the effectiveness of the PEERS® program for children with ASD. A literature search was conducted, resulting in 97 articles. Following the implementation of inclusion and exclusion criteria, four articles of Level III and IV evidence based on CEBM guidelines were included in this review. All studies demonstrated positive findings regarding social skills development with one study revealing statistically significant results in increasing social skills and decreasing problem behaviors after participation in the PEERS® program. This scoping review found improvements in the PEERS® programs for social skills in young children (ages 4-7) with ASD. However, further research is warranted, emphasizing larger sample sizes, consideration of external factors, and implementation of randomization and blinding in future studies.
The Program of Education through Work for Health (PET-Health), with a focus on interprofessionality, is one of the actions of the Plan for the Strengthening of Interprofessional Education in Brazil. This research aimed to systematically analyze the characteristics of the proposals submitted to the public notice of the PET-Health Interprofessionality specifically in relation to the theoretical-conceptual and methodological alignment of interprofessional education (IPE). The study is a qualitative document content analysis. We analyzed one hundred and twenty projects submitted to the selection process from institutions participating in the PET-Health Interprofessionality. Content analysis followed three steps: pre-analysis, exploration of the material, and treatment and interpretation of results. Seven categories were identified: a) alignment with the theoretical-conceptual frameworks of IPE, b) curriculum changes, c) faculty development with a focus on IPE, d) articulation among objectives, actions, and results expected related to IPE, e) strategies for monitoring and evaluation, f) involvement of users/families and community, and g) development of collaborative competencies. We conclude that while some advancements have been made, there remains a need for more in-depth discussion in Brazil to ensure the development of competencies capable of assuring more integral, resolute, and safer healthcare services, with capacity to (re)signify user-centered care in the planning and delivery of healthcare.
The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model's effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling.
The Progress Test is an individual assessment applied to all students at the same time and on a regular basis. The test was structured in the medical undergraduate education of a conglomerate of schools to structure a programmatic assessment integrated into teaching. This paper presents the results of four serial applications of the progress test and the feedback method to students. This assessment comprises 120 items offered online by means of a personal password. Items are authored by faculty, peer-reviewed, and approved by a committee of experts. The items are classified by five major areas, by topics used by the National Board of Medical Examiners and by medical specialties related to a national Unified Health System. The correction uses the Item Response Theory with analysis by the "Rasch" model that considers the difficulty of the item. Student participation increased along the four editions of the tests, considering the number of enrollments. The median performances increased in the comparisons among the sequential years in all tests, except for test1 - the first test offered to schools. Between subsequent years of education, 2nd-1st; 4th-3rd and 5th-4th there was an increase in median scores from progress tests 2 through 4. The final year of undergraduate showed a limited increase compared to the 5th year. There is a consistent increase in the median, although with fluctuations between the observed intervals. The progress test promoted the establishment of regular feedback among students, teachers and coordinators and paved the road to engagement much needed to construct an institutional programmatic assessment.
The progressive aging of the population and the increasing complexity of health issues contribute to a growing number of older individuals seeking emergency care. This study aims to assess the state of the art of care provided to older people in the Emergency Departments of Lombardy, the most populous region in Italy, counting over 2 million people aged 65 years and older. An online cross-sectional survey was developed and disseminated among emergency medicine physicians and physicians affiliated to the Lombardy section of the Italian Society of Geriatrics and Gerontology (SIGG), during June and July 2023. The questionnaire covered hospital profiles, geriatric consultation practices, risk assessment tools, discharge processes and perspectives on geriatric emergency care. In this mixed method research, 219 structured interviews were collected. The majority of physicians were employed in hospitals, with 54.7% being geriatricians. Critical gaps in older patient's care were identified, including the absence of dedicated care pathways, insufficient awareness of screening tools, and a need for enhanced professional training. Tailored protocols and geriatric educational programs are crucial for improving the quality of emergency care provided to older individuals. These measures might also help relieve the burden on the Emergency Departments, thereby potentially enhancing overall efficiency and ensuring better outcomes.
The proliferation of global environmental problems has necessitated the need to re-examine the environmental impacts caused by human-nature relations. Yet individual attitudes and environmental literacy remain among the critical determinants of environmental sustainability. Consequently, environmental psychology has been one of the most effective tools for shaping people's actions in favor of the environment. As such, this article presents a dataset that describes an intervention carried out to explore the effect of inquiry-based learning on shaping environmental attitudes (EAs) and knowledge of pre-service biology teachers in selected teacher colleges (TCs) in Tanzania. Data were collected from four TCs located in four different regions of Tanzania (N = 333). Particularly, EAs were measured using the two-factor model of ecological attitudes (2-MEV) by assessing two uncorrelated domains of environmental attitudes: Preservation and Utilization of nature. In addition, a Competence Model for Environmental Education was used to measure the three dimensions (system knowledge, action-related knowledge, and effectiveness knowledge) of environmental knowledge among pre-service biology teachers. A modified design of the Solomon four-group was employed to guide the intervention in measuring the level of change in EAs and environmental knowledge before and after the study intervention. A paired-sample t-test was used to assess the effect of the intervention on TCs that had pre- and post-tests, given the variation in the number of subjects in each TC. In addition, the one-way analysis of variance (ANOVA) compared the mean scores of the combined study groups at the post-test. Besides, in the regression analysis, Hayes' PROCESS macro (version 4.1) was used to assess the moderation effect of the Social Desirability Responding (SDR) on the relationships between EAs and environmental knowledge. Likewise, age (in years) was entered as a covariate in the regression model of the Statistical Product and Service Solution (IBM SPSS version 25). The presented dataset can act as a basis for improving the status of environmental education delivery in teacher education in Tanzania and other areas with similar or related contexts. Thus, program-specific interventions could be designed for prospective biology teachers as potential change agents in shaping how people interact with the environment.
The proliferation of online disinformation and fake news, particularly in the context of breaking news events, demands the development of effective detection mechanisms. While textual content remains the predominant medium for disseminating misleading information, the contribution of other modalities is increasingly emerging within online outlets and social media platforms. However, multimodal datasets, which incorporate diverse modalities such as texts and images, are not very common yet, especially in low-resource languages. This study addresses this gap by releasing a dataset tailored for multimodal fake news detection in the Italian language. This dataset was originally employed in a shared task on the Italian language. The dataset is divided into two data subsets, each corresponding to a distinct sub-task. In sub-task 1, the goal is to assess the effectiveness of multimodal fake news detection systems. Sub-task 2 aims to delve into the interplay between text and images, specifically analyzing how these modalities mutually influence the interpretation of content when distinguishing between fake and real news. Both sub-tasks were managed as classification problems. The dataset consists of social media posts and news articles. After collecting it, it was labeled via crowdsourcing. Annotators were provided with external knowledge about the topic of the news to be labeled, enhancing their ability to discriminate between fake and real news. The data subsets for sub-task 1 and sub-task 2 consist of 913 and 1350 items, respectively, encompassing newspaper articles and tweets.
The proliferation of smart devices in educational settings has prompted a need to investigate their influence on learners' attitudes and language learning outcomes. Recent advancements in smart technology (ST) have ignited curiosity regarding their impact on academic eagerness (AE), (AS), and academic performance (AP) among elementary English language learners. Despite this, there remains a dearth of comprehensive discussion in this area. This study encompasses all primary language students from the academic year 2023 as its sample. A multistage sampling method was employed for sample selection. The study introduced ST as an intervention over eight 45-minute sessions spanning two months. Data collection instruments included AE assessments adapted from Fredericks et al., an AS questionnaire developed by the researchers, and an AP questionnaire designed by Pham and Taylor. Data analysis incorporated statistical tests such as the Kolmogorov-Smirnov test, Levene test, and univariate analysis of covariance. The findings yield valuable insights into the impact of ST on AE, AS, and AP, shedding light on its potential advantages and limitations in language learning. Notably, the experimental group (EG) outperformed the control group (CG). These results contribute to the growing body of knowledge concerning ST integration in education and its consequences on AP and learner attitudes. Ultimately, this research aims to provide evidence-based recommendations for enhancing language learning outcomes and experiences among elementary English as a Foreign Language (EFL) students in the digital education era.
The proliferation of social media in contemporary society presents a compelling impetus for scholars to explore the untapped capabilities of social networking platforms with the aim of enhancing educational practices. Despite the notable impact of social media on the acquisition of second or foreign languages, there is a scarcity of empirical research investigating the influence of Instagram feed-based activities on the development of vocabulary in language learning at under-intermediate level. In order to address this research gap, the present study aimed to investigate the impact of Instagram feed-based activities on the acquisition of vocabulary among individuals studying English as a foreign language (EFL). As a result, a cohort of 79 intermediate EFL learners was subjected to random assignment, leading to the formation of both experimental and control groups. The participants in the control group were provided with conventional online teaching using webinar platforms. The participants in the experimental group, on the other hand, were instructed via the use of webinar platforms and were required to participate in exercises that were based on Instagram feeds respectively. The results of the data analysis using one-way Multivariate Analysis of Variance (MANOVA) revealed that the learners in the experimental group (EG) demonstrated superior performance compared to their counterparts in the control group (CG). The findings underscored the notable impact of using Instagram feed-based activities in promoting the acquisition of vocabulary knowledge. Additionally, it was shown that EFL learners had favorable views towards the use of Instagram Feed-based Tasks for the purpose of learning vocabulary. Due to treatment, EG achieved higher state in informal digital learning of English and on-line engagement. Moreover, they felt less technostress after treatment. The findings of this research have potential consequences for language learners, instructors, curriculum designers, and policy makers. Specifically, they suggest that incorporating Instagram feed-based activities into language learning and teaching may provide valuable chances for further practice.