Yoxas/statistical_literacy huggingface dataset:
Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research. The goal of most qualitative studies is not to generalize but rather to provide a rich, contextualized understanding of some aspect of human experience through the intensive study of particular cases. Yet, in an environment where evidence for improving practice is held in high esteem, generalization in relation to knowledge claims merits careful attention by both qualitative and quantitative researchers. Issues relating to generalization are, however, often ignored or misrepresented by both groups of researchers. Three models of generalization, as proposed in a seminal article by Firestone, are discussed in this paper: classic sample-to-population (statistical) generalization, analytic generalization, and case-to-case transfer (transferability). Suggestions for enhancing the capacity for generalization in terms of all three models are offered. The suggestions cover such issues as planned replication, sampling strategies, systematic reviews, reflexivity and higher-order conceptualization, thick description, mixed methods research, and the RE-AIM framework within pragmatic trials.
Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.
Generating synthetic locomotory and neural data is a useful yet cumbersome step commonly required to study theoretical models of the brain's role in spatial navigation. This process can be time consuming and, without a common framework, makes it difficult to reproduce or compare studies which each generate test data in different ways. In response, we present RatInABox, an open-source Python toolkit designed to model realistic rodent locomotion and generate synthetic neural data from spatially modulated cell types. This software provides users with (i) the ability to construct one- or two-dimensional environments with configurable barriers and visual cues, (ii) a physically realistic random motion model fitted to experimental data, (iii) rapid online calculation of neural data for many of the known self-location or velocity selective cell types in the hippocampal formation (including place cells, grid cells, boundary vector cells, head direction cells) and (iv) a framework for constructing custom cell types, multi-layer network models and data- or policy-controlled motion trajectories. The motion and neural models are spatially and temporally continuous as well as topographically sensitive to boundary conditions and walls. We demonstrate that out-of-the-box parameter settings replicate many aspects of rodent foraging behaviour such as velocity statistics and the tendency of rodents to over-explore walls. Numerous tutorial scripts are provided, including examples where RatInABox is used for decoding position from neural data or to solve a navigational reinforcement learning task. We hope this tool will significantly streamline computational research into the brain's role in navigation.
Generative Adversarial Networks (GANs) can produce synthetic images free from personal data. They hold significant value in medical research, where data protection is increasingly regulated. Panoramic radiographs (PRs) are a well-suited modality due to their significant level of standardization while simultaneously displaying a high degree of personally identifiable data. We produced synthetic PRs (syPRs) out of real PRs (rePRs) using StyleGAN2-ADA by NVIDIA©. A survey was performed on 54 medical professionals and 33 dentistry students. They assessed 45 radiological images (20 rePRs, 20 syPRs, and 5 syPRcontrols) as real or synthetic and interpreted a single-image syPR according to the image quality (0-10) and 14 different items (agreement/disagreement). They also rated the importance for the profession (0-10). A follow-up was performed for test-retest reliability with >10 % of all participants. Overall, the sensitivity was 78.2 % and the specificity was 82.5 %. For professionals, the sensitivity was 79.9 % and the specificity was 82.3 %. For students, the sensitivity was 75.5 % and the specificity was 82.7 %. In the single syPR-interpretation image quality was rated at a median of 6 and 11 items were considered as agreement. The importance for the profession was rated at a median score of 7. The Test-retest reliability yielded a value of 0.23 (Cohen's kappa). The study marks a comprehensive testing to demonstrate that GANs can produce synthetic radiological images that even health professionals can sometimes not differentiate from real radiological images, thereby being genuinely considered authentic. This enables their utilization and/or modification free from personally identifiable information. Synthetic images can be used for university teaching and patient education without relying on patient-related data. They can also be utilized to upscale existing training datasets to improve the accuracy of AI-based diagnostic systems. The study thereby supports clinical teaching as well as diagnostic and therapeutic decision-making.
Generative AI is revolutionizing patient education in healthcare, particularly through chatbots that offer personalized, clear medical information. Reliability and accuracy are vital in AI-driven patient education. How effective are Large Language Models (LLM), such as ChatGPT and Google Bard, in delivering accurate and understandable patient education on lumbar disc herniation? Ten Frequently Asked Questions about lumbar disc herniation were selected from 133 questions and were submitted to three LLMs. Six experienced spine surgeons rated the responses on a scale from "excellent" to "unsatisfactory," and evaluated the answers for exhaustiveness, clarity, empathy, and length. Statistical analysis involved Fleiss Kappa, Chi-square, and Friedman tests. Out of the responses, 27.2% were excellent, 43.9% satisfactory with minimal clarification, 18.3% satisfactory with moderate clarification, and 10.6% unsatisfactory. There were no significant differences in overall ratings among the LLMs (p = 0.90); however, inter-rater reliability was not achieved, and large differences among raters were detected in the distribution of answer frequencies. Overall, ratings varied among the 10 answers (p = 0.043). The average ratings for exhaustiveness, clarity, empathy, and length were above 3.5/5. LLMs show potential in patient education for lumbar spine surgery, with generally positive feedback from evaluators. The new EU AI Act, enforcing strict regulation on AI systems, highlights the need for rigorous oversight in medical contexts. In the current study, the variability in evaluations and occasional inaccuracies underline the need for continuous improvement. Future research should involve more advanced models to enhance patient-physician communication.
Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.
Generative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI's impact on higher education has the potential to change students' learning experience as well as faculty's teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research. This study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation. The study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions. The data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians' professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians' expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises. This study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed.
Genes commonly express multiple RNA products (RNA isoforms), which differ in exonic content and can have different functions. Making sense of the plethora of known and novel RNA isoforms being identified by transcriptomic approaches requires a user-friendly way to visualize gene isoforms and how they differ in exonic content, expression levels and potential functions. Here we introduce IsoVis, a freely available webserver that accepts user-supplied transcriptomic data and visualizes the expressed isoforms in a clear, intuitive manner. IsoVis contains numerous features, including the ability to visualize all RNA isoforms of a gene and their expression levels; the annotation of known isoforms from external databases; mapping of protein domains and features to exons, allowing changes to protein sequence and function between isoforms to be established; and extensive species compatibility. Datasets visualised on IsoVis remain private to the user, allowing analysis of sensitive data. IsoVis visualisations can be downloaded to create publication-ready figures. The IsoVis webserver enables researchers to perform isoform analyses without requiring programming skills, is free to use, and available at https://isomix.org/isovis/.
Genetic counselors (GCs) are trained to help individuals navigate the medical and psychological implications of genetic test results, familial conditions, and ultrasound anomalies. Therefore, familiarity with reproductive options, including abortion, is vital. However, previous studies have found gaps in GCs' knowledge regarding abortion care and there are currently no recommendations regarding abortion curriculum. This study aimed to assess the state of abortion curriculum in genetic counseling graduate programs in the United States and to examine and compare the satisfaction levels of program representatives and recent graduates. Program representatives and recent graduates were invited to complete an anonymous survey evaluating the abortion curriculum, satisfaction with said curriculum, and perceived preparedness to counsel on abortion. Quantitative data from 46 program representatives and 123 recent graduates were analyzed using descriptive statistics and appropriate statistical analyses, including the Mann-Whitney U-test and the Kruskal-Wallis test. Large variability existed in the amount and types of abortion training. Results showed greater satisfaction and feelings of preparation to counsel on abortion in graduates whose program provided a dedicated abortion curriculum (p < 0.001, p = 0.005). In addition, graduates with abortion counseling experience felt less prepared to counsel on abortion than their programs believed them to be (p = 0.04). Graduates perceived procedural timing, facilitation of genetic testing, and resources/support desired by patients before, during, or after an abortion, to be the most important topics, although these were not included in all programs' curriculum. Program representatives and recent graduates alike noted that variability in clinical training is a barrier to abortion education. Our results demonstrate a need for curricular reform to reduce variability in training and ensure that all graduates receive the same foundational abortion education. Further research is needed to determine the scope of GCs in abortion care, as well as which topics and education formats are most helpful in graduate education.
Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.
Genetic literacy among primary healthcare providers is crucial for appropriate patient care with the advances in genetic and genomic medicine. Studies from high-income countries highlight the lack of knowledge in genetics and the need to develop curricula for continuing professional development of non-geneticists. Scarce data is available from resource-constrained countries in Middle East and North Africa. Lebanon is a small country in this region characterized by high rates of consanguinity and genetic disorders like several surrounding countries, such as Jordan, Syria, and Turkey. The primary aim of this study assessed the genetic literacy, self-perceived and actual knowledge as well as practices among primary care providers in Lebanon. The secondary aim identified their educational needs and proposed evidence-based continuing education programs. A cross-sectional survey-based study, using a self-administered questionnaire, was conducted targeting physicians from Family Medicine, Obstetrics and Gynecology, and Pediatrics. The questionnaire was divided into five sections: demographics, familiarity with genetic tests, self-reported and actual knowledge, genetic practices, and educational needs. Statistics were performed using SPSS v24. The Chi-square test was used for independent variables. Differences between mean scores were measured using paired sample t-tests for groups of two levels and one-way ANOVA for more than two. Multiple linear regression was used to study the variables associated with the knowledge score while controlling for other variables. The survey included 123 physicians. They were mostly familiar with karyotype as first-tier genetic test. Although 38% perceived their knowledge as good, only 6% scored as such in knowledge assessment. A better knowledge score was observed in academic institutions as well as in urban settings (p<0.05). One third never ordered any genetic testing, mostly due to poor knowledge. Almost all (98%) were ready to attend continuing professional development sessions in genetics. Our findings show the need to improve genetic literacy among healthcare frontliners, focusing on remote regions and nonacademic centers in Lebanon, a model for other resource-constrained country in the Middle East and North Africa region. This study advances recommendations for evidence-based genetic continuing education programs and highlighted the role of that the few genetic specialists can play in their successful implementation.
Genetic modification has been used to create dairy cattle without horns and with increased resistance to disease; applications that could be beneficial for animal welfare, farm profits, and worker safety. Our aim was to assess how different stated purposes were associated with public attitudes toward these two applications using a mixed methods approach. Using an online survey, U.S. participants were randomly assigned to one of ten treatments in a 2 (application: hornless or disease-resistant) x 5 (purposes: improved animal welfare, reduced costs, increased worker safety, all three purposes, or no purpose) factorial design. Each participant was asked to read a short description of the assigned treatment (e.g. hornlessness to improve calf welfare) and then respond to a series of questions designed to assess attitude toward the treatment using 7-point Likert scales (1 = most negative; 7 = most positive). Responses of 957 participants were averaged to creative an attitude construct score. Participants were also asked to explain their response to the treatment. Qualitative analysis of these text responses was used to identify themes associated with the participants' reasoning. Participant attitudes were more favorable to disease resistance than to hornlessness (mean ± SE attitude score: 4.5 ± 0.15 vs. 3.7 ± 0.14). In the 'disease-resistance' group participants had more positive attitudes toward genetic modification when the described purpose was animal welfare versus reduction of costs (contrast = 1.00; 95% CI = 0.12-1.88). Attitudes were less favorable to the 'hornless' application if no purpose was provided versus when the stated purpose was either to improve animal welfare (contrast = 0.95; 95% CI = 0.26-1.64) or when all purposes were provided (contrast = 0.88; 95% CI = 0.19-1.58). Similarly, attitudes were less positive when the stated purpose was to reduce costs versus either improving animal welfare (contrast = 0.86; 95% CI = 0.09-1.64) or when all purposes were provided (contrast = 0.79; 95% CI = 0.02-1.56). Quantitative and qualitative analysis indicated that both the specific application and perceived purpose (particularly when related to animal welfare) can affect public attitudes toward genetic modification.
Genetic modification of capsid proteins by peptide insertion has created the possibility of using adeno-associated viral (AAV) vectors for receptor specific gene transfer (AAV targeting). The most common site used for insertion in AAV serotype 2 capsids are amino acid positions 587 and 588 located at the second highest capsid protrusion. Reasoning that peptide insertions at the most exposed position augments target receptor interaction, we explored position 453 as a new insertion site. Position 453 was identified in silico. Capsid mutants carrying the model ligand RGD-4C in position 453 with and without R585A/R588A substitutions were compared with respective mutants carrying the ligand in position 587. The accessibility of the inserted ligand was determined by an enzyme-linked immunosorbent assay, whereas the transduction efficiency and specificity of receptor binding were assayed by gene transfer and competition experiments, respectively. Vector biodistribution was determined in mice by quantitative polymerase chain reaction analysis. Initially, RGD-4C, inserted at position 453, failed to efficiently bind its target receptor. R585 and R588, located at the neighboring peak and known to mediate primary receptor binding, were identified as interfering residues. R585A and R588A substitutions rendered position 453 mutants superior to those with the ligand in position 587 in target receptor binding and cell transduction efficiency. The in vivo biodistribution was independent of the insertion site, but directed by the inserted ligand when primary receptor binding was avoided. Position 453 emerged as a prominent site for the development of targeting mutants. Furthermore, we show for the first time that linearly distant residues can be critical for the efficiency of inserted peptide ligands.