Yoxas/statistical_literacy huggingface dataset:
Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process. In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students. Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom's taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner's stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner's stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined. AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners' stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.
Artificial Intelligence (AI), and specifically ChatGPT, has shown potential in healthcare, yet its performance in specialized medical examinations such as the Orthopaedic Surgery In-Training Examination and European Board Hand Surgery diploma has been inconsistent. This study aims to evaluate the capability of ChatGPT-4 to pass the American Hand Surgery Certifying Examination. ChatGPT-4 was tested on the 2019 American Society for Surgery of the Hand (ASSH) Self-Assessment Exam. All 200 questions available online (https://onlinecme.assh.org) were retrieved. All media-containing questions were flagged and carefully reviewed. Eight media-containing questions were excluded as they either relied purely on videos or could not be rationalized from the presented information. Descriptive statistics were used to summarize the performance (% correct) of ChatGPT-4. The ASSH report was used to compare ChatGPT-4's performance to that of the 322 physicians who completed the 2019 ASSH self-assessment. ChatGPT-4 answered 192 questions with an overall score of 61.98%. Performance on media-containing questions was 55.56%, while on non-media questions it was 65.83%, with no statistical difference in performance based on media inclusion. Despite scoring below the average physician's performance, ChatGPT-4 outperformed in the 'vascular' section with 81.82%. Its performance was lower in the 'bone and joint' (48.54%) and 'neuromuscular' (56.25%) sections. ChatGPT-4 achieved a good overall score of 61.98%. This AI language model demonstrates significant capability in processing and answering specialized medical examination questions, albeit with room for improvement in areas requiring complex clinical judgment and nuanced interpretation. ChatGPT-4's proficiency is influenced by the structure and language of the examination, with no replacement for the depth of trained medical specialists. This study underscores the supportive role of AI in medical education and clinical decision-making while highlighting the current limitations in nuanced fields such as hand surgery.
Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.
Artificial intelligence (AI), particularly ChatGPT, is becoming more and more prevalent in the healthcare field for tasks such as disease diagnosis and medical record analysis. The objective of this study is to evaluate the proficiency and accuracy of ChatGPT in different domains of clinical pharmacy cases and queries. The study NAPLEX® Review Questions, 4th edition, pertaining to 10 different chronic conditions compared ChatGPT's responses to pharmacotherapy cases and questions obtained from McGraw Hill's, alongside the answers provided by the book's authors. The proportion of correct responses was collected and analyzed using the Statistical Package for the Social Sciences (SPSS) version 29. When tested in English, ChatGPT had substantially higher mean scores than when tested in Turkish. The average accurate score for English and Turkish was 0.41 ± 0.49 and 0.32 ± 0.46, respectively, p = 0.18. Responses to queries beginning with "Which of the following is correct?" are considerably more precise than those beginning with "Mark all the incorrect answers?" 0.66 ± 0.47 as opposed to 0.16 ± 0.36; p = 0.01 in English language and 0.50 ± 0.50 as opposed to 0.14 ± 0.34; p < 0.05in Turkish language. ChatGPT displayed a moderate level of accuracy while responding to English inquiries, but it displayed a slight level of accuracy when responding to Turkish inquiries, contingent upon the question format. Improving the accuracy of ChatGPT in languages other than English requires the incorporation of several components. The integration of the English version of ChatGPT into clinical practice has the potential to improve the effectiveness, precision, and standard of patient care provision by supplementing personal expertise and professional judgment. However, it is crucial to utilize technology as an adjunct and not a replacement for human decision-making and critical thinking.
Artificial Intelligence has proven to be effective in radiomics. The main problem in using Artificial Intelligence is that researchers and practitioners are not able to know how the predictions are generated. This is currently an open issue because results' explainability is advantageous in understanding the reasoning behind the model, both for patients than for implementing a feedback mechanism for medical specialists using decision support systems. Addressing transparency issues related to the Artificial Intelligence field, the innovative technique of Formal methods use a mathematical logic reasoning to produce an automatic, quick and reliable diagnosis. In this paper we analyze results given by the adoption of Formal methods for the diagnosis of the Coronavirus disease: specifically, we want to analyse and understand, in a more medical way, the meaning of some radiomic features to connect them with clinical or radiological evidences. In particular, the usage of Formal methods allows the authors to do statistical analysis on the feature value distributions, to do pattern recognition on disease models, to generalize the model of a disease and to reach high performances of results and interpretation of them. A further step for explainability can be accounted by the localization and selection of the most important slices in a multi-slice approach. In conclusion, we confirmed the clinical significance of some First order features as Skewness and Kurtosis. On the other hand, we suggest to decline the use of the Minimum feature because of its intrinsic connection with the Computational Tomography exam of the lung.
Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.
Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation R2 coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.
Artificially intelligent robots as teachers (AI teachers) have attracted extensive attention due to their potential to relieve the challenge of global teacher shortage and realize universal elementary education by 2030. Despite mass production of service robots and discussions about their educational applications, the study of full-fledged AI teachers and children's attitudes towards them is quite preliminary. Here, we report a new AI teacher and an integrated model to assess how pupils accept and use it. Participants included students from Chinese elementary schools via convenience sampling. Questionnaires (n = 665), descriptive statistics and structural equation modeling based on software SPSS Statistics 23.0 and Amos 26.0 were carried out in data collection and analysis. This study first developed an AI teacher by coding a lesson design, course contents and Power Point with script language. Based on the popular Technology Acceptance Model and Task-Technology Fit Theory, this study identified key determinants of the acceptance, including robot use anxiety (RUA), perceived usefulness (PU), perceived ease of use (PEOU) and robot instructional task difficulty (RITD). Moreover, this study found that pupils' attitudes towards the AI teacher, which could be predicted by PU, PEOU and RITD, were generally positive. It is also found that the relationship between RITD and acceptance was mediated by RUA, PEOU and PU. This study holds significance for stakeholders to develop independent AI teachers for students.
Arts and Humanities (A/H) training is a powerful strategy to help medical students develop key competencies which align with the CanMEDS roles that Canadian physicians are expected to embody. Students with backgrounds in A/H may enter medical school with the skills and dispositions that A/H training provides. This paper explores the varied experiences of medical students with prior A/H backgrounds, with an emphasis on how they navigate relationships with their student cohorts and participate in undergraduate medical training environments. Descriptive qualitative research methodology was used to conduct and analyze semi-structured interviews exploring the perspectives of Canadian medical students with either a A/H degree or training in A/H (n = 13). Domains such as identity, integration of interests, and challenges in maintaining A/H interests during medical training were explored. Participants described their A/H identity as intertwined with their identity as medical trainees and described their sense of interconnection between the disciplines. Challenges included imposter syndrome and difficulties in relating with peers from science backgrounds. Participants described returning to their A/H interests as a tool for wellness amidst medical training. Medical students with a background in A/H training describe this background as offering both affordances and challenges for their sense of identity, belonging, and wellness. These students offer an untapped resource: they come with dispositions of value to medicine, and they perceive a positive, hidden A/H curriculum that supports their maintenance of these dispositions during training. Understanding more about these hidden treasures could help foster the development of well-rounded and humanistic physicians in the entire medical class.
As a chronic disease, diabetes needs special self-care behaviors until the end of life. Personality traits are considered to be effective psychological factors in controlling diabetes and self-care in patients with diabetes. The present study was conducted to determine the relationship between personality profile and self-care among people with type 2 diabetes. In this descriptive-correlational study conducted in 2021, 160 patients with type 2 diabetes referred to the diabetes clinic of Imam Khomeini Educational and Medical Center in Ardabil were selected by convenience sampling method. The data collection tools included the Diabetes Self-Care Activities questionnaire (SDSCA) and the short form of the Millon Multi-Axis Clinical Test (MCMI-3), which were completed through interviews with patients. Data were analyzed by SPSS software using descriptive statistics (mean, SD, and frequency) and inferential statistics (Pearson correlation coefficient and linear regression). Based on the results, apart from the obsessive personality disorder, which had a positive relationship with self-care behaviors, a significant negative correlation was observed between schizoid, avoidant, depressed, dependent, antisocial, self-harming, borderline, and paranoid personality disorders with self-care behaviors (p < 0.01). The results showed that there is a significant negative relationship between personality profile and self-care status of patients with type 2 diabetes. In other words, a person's personality profile can predict self-care behaviors. Accordingly, personality traits can be considered as one of the influencing factors on self-care in the educational programs of diabetic patients. Holding educational classes to empower patients using psychological interventions and teaching effective solutions can be an effective step toward increasing the level of mental-physical health and self-care of patients with type 2 diabetes.
As a descriptive-inferential study, this research aimed at revealing the relationship between music training and academic development with the Chinese middle school students' academic performance of mathematics and physics skills. The participants of this study consisted of the students from two different middle schools located at two cities in Shandong province, China. From each school 250 students were selected, and the statistics was used to analyze both the academic performance of the students and the data obtained from the scale designed by the authors. The research results show that the non-music students outperformed music students on both mathematics and physics development. In addition, music training did not contribute to the academic achievement independently but rather integrated with several factors like parents' education and out-of-school engagement. The findings suggest the positive influence on non-musical cognitive learning, and it has potential implications for the Chinese middle school education.
As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.
As a response to the criticisms evidence-based practice currently faces, groups of health care researchers and guideline makers have started to call for the appraisal and inclusion of different kinds of knowledge in guideline production (other than randomized controlled trials [RCTs]) to better link with the informal knowledge used in clinical practice. In an ethnographic study, Gabbay and Le May showed that clinicians in everyday practice situations do not explicitly or consciously use guidelines. Instead, they use mindlines: collectively shared, mostly tacit knowledge that is shaped by many sources, including accumulated personal experiences, education (formal and informal), guidance, and the narratives about patients that are shared among colleagues. In this study on informal knowledge, we consider virtual networks of clinicians as representative of the mindlines in the wider medical community, as holders of knowledge, as well as catalysts of knowing. The aim of this study was to explore how informal knowledge and its creation in communities of clinicians can be characterized as opposed to the more structured knowledge produced in guideline development. This study included a qualitative study of postings on three large virtual networks for physicians in the United Kingdom, the Netherlands, and Norway, taking the topic of statins as a case study and covering more than 1400 posts. Data were analyzed thematically with reference to theories of collaborative knowledge construction and communities of practice. The dataset showed very few postings referring to, or seeking to adhere to, explicit guidance and recommendations. Participants presented many instances of individual case narratives that highlighted quantitative test results and clinical examination findings. There was an emphasis on outliers and the material, regulatory, and practical constraints on knowledge use by clinicians. Participants conveyed not-so-explicit knowledge as tacit and practical knowledge and used a prevailing style of pragmatic reasoning focusing on what was likely to work in a particular case. Throughout the discussions, a collective conceptualization of statins was generated and reinforced in many contexts through stories, jokes, and imagery. Informal knowledge and knowing in clinical communities entail an inherently collective dynamic practice that includes explicit and nonexplicit components. It can be characterized as knowledge-in-context in practice, with a strong focus on casuistry. Validity of knowledge appears not to be based on criteria of consensus, coherence, or correspondence but on a more polyphonic understanding of truth. We contend that our findings give enough ground for further research on how exploring mindlines of clinicians online could help improve guideline development processes.
As a specific group with high health inequality, it is crucial to improve the health status and health inequalities of rural-to-urban migrant workers. This study aimed to evaluate the health inequality of migrant and urban workers in China and decompose it. A cross-sectional study was carried out, using a standardized questionnaire to obtain basic information, self-rated health to evaluate health status, concentration index to measure health inequalities, and WDW decomposition to analyze the causes of health inequalities. The concentration index of health for migrants was 0.021 and 0.009 for urban workers. The main factors contributing to health inequality among rural-to-urban migrant workers included income, exercise, and age. In contrast, the main factors of health inequality among urban workers included income, the number of chronic diseases, social support, and education. There were health inequalities in both rural-to-urban migrant and urban workers. The government and relevant authorities should formulate timely policies and take targeted measures to reduce income disparities among workers, thereby improving health inequality.