Yoxas/statistical_literacy huggingface dataset:
Clinical environments can be so stressful to medical students as to be detrimental to their learning and well-being. Our intervention, Clinical Debrief, integrates learning through clinical experience with the development of positive coping strategies. Students shared cases and experiences during weekly small group classroom discussions, facilitated by general practitioners (from outside their current hospital placement), throughout two consecutive 12-week blocks of their first clinical year. Alongside enquiry-based and clinical reasoning learning, we gave students a safe space to reflect on their affect. Our aim was to critically examine students' views in Clinical Debrief. Anonymised quantitative and qualitative evaluation data were collected over 3 years using online questionnaires on completion of each 12-week block. The data relating to psychological supervision were analysed independently and in parallel, using thematic analysis for qualitative data. We aim to help students develop positive coping mechanisms, promoting empathy,self-awarenessand wellbeing RESULTS: A total of 1857 evaluations were extracted (response rate 67%). The median (interquartile range) overall rating for Clinical Debrief sessions was 9 (8-10), where 10 indicates 'excellent' and 1 indicates 'significant improvement needed'. The rating for the supervisory aspects of the sessions and free-text comments were positive. Students appreciated safe environments, the session structure, facilitator role modelling, transitional support and processing of emotional experiences. Mandatory integrated longitudinal supervision, using trained clinician facilitators, was positively received by students in transition to clinical placements. Normalising the emotional impact of medical work destigmatises distress. Linking clinical reasoning with affective state awareness to contextualise case management, following Mezirow's transformative learning theory, brings added benefit to learning and well-being. Student demand for the expansion of Clinical Debrief is evidence of success.
Clinical events suggestive of nutrition care found in electronic health records (EHRs) are rarely explored for their associations with hypertension outcomes. Longitudinal analysis using structured EHR data from primary care visits at a health system in the US from December 2017-December 2020 of adult patients with hypertension (n = 4,237) tested for associations between last visit blood pressure (BP) control (≤ 140 Systolic BP and ≤ 90 Diastolic BP) and ≥ 1 nutrition care clinical event operationalized as (overweight or obesity (BMI > 25 or 30, respectively) diagnoses, preventive care visits, or provision of patient education materials (PEM)). Descriptive statistics and longitudinal targeted maximum likelihood estimation (LTMLE) models were conducted to explore average treatment effects (ATE) of timing and dose response from these clinical events on blood pressure control overall and by race. The median age was 62 years, 29% were male, 52% were Black, 25% were from rural areas and 50% had controlled BP at baseline. Annual documentation of overweight/obesity diagnoses ranged 3.0-7.8%, preventive care visits ranged 6.2-15.7%, and PEM with dietary and hypertension content were distributed to 8.5-28.8% patients. LTMLE models stratified by race showed differences in timing, dose, and type of nutrition care. Black patients who had nutrition care in Year 3 only compared to none had lower odds for BP control (ATE -0.23, 95% CI: -0.38,-0.08, p = 0.003), preventive visits in the last 2 years high higher odds for BP control (ATE 0.31, 95% CI: 0.07,0.54, p = 0.01), and early or late PEMs had lower odds for BP control (ATE -0.08, 95% CI: -0.15,-0.01, p = 0.03 and ATE -0.23, 95% CI: -0.41,-0.05, p = 0.01, respectively). In this study, clinical events suggestive of nutrition care are significantly associated with BP control, but are infrequent and effects differ by type, timing, and patient race. Preventive visits appear to have the most effect; additional research should include examining clinical notes for evidence of nutrition care among different populations, which may uncover areas for improving nutrition care for patients with chronic disease.
Clinical expertise, patient preference, and the best evidence are the three elements of evidence-based medicine. Based on high-level and high-quality evidence, qualitative and quantitative analysis of the prescribing decisions of physicians is beneficial to improving clinical efficacy. A mature methodological system is available for the retrieval, analysis, summary, evaluation, and recommendation of the evidence, but there are still few studies on physicians' prescribing decisions. How to analyze the trend of physicians' prescribing decisions based on the priority ranking in addition and subtraction of prescriptions? Analytic hierarchy process(AHP) is a method for decision making, which arranges the elements of the decision problem into overall goal, criteria, and operational sub-criteria, and uses the matrix eigenvector method to solve the problem. This study aims to analyze the priority of physicians' prescribing decisions for diabetes mellitus with deficiency of both Qi and Yin based on AHP. To be specific, a database of diabetes mellitus cases with deficiency of both Qi and Yin was established and AHP was used to yield the priority ranking of Chinese patent medicine prescriptions in specific clinical scenarios. In the selected cases of diabetes mellitus with deficiency of both Qi and Yin, Xiaoke Pills was the best prescription for the treatment of type 2 diabetes mellitus(deficiency of both Qi and Yin)(normalized=0.388), followed by Liuwei Dihuang Pills(normalized=0.269), Qishen Capsules(normalized=0.230), and Shengmai Injection(normalized=0.113). According to the analysis the available data, for type 2 diabetes mellitus(deficiency of both Qi and Yin), Xiaoke Pills was the most effective prescription in specific scenarios. When the physicians' prescribing decisions are consistent with the evidence, quantitative analysis of physicians' cognition will boost the evidence-based medical decision-making. However, the research results are also affected by the quality of literature, evidence level and priority, which are thus have some limitations. It is recommended that further small data research based on individual cases be carried out to lay a evidence-based basis for the clinical decision-making of type 2 diabetes mellitus.
Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.
Clinical immersion experiences provide engineering students with opportunities to identify unmet user needs and to interact with clinical professionals. These experiences have become common features of undergraduate biomedical engineering curricula, with many published examples in the literature. There are, however, few or no published studies that describe rigorous qualitative analysis of biomedical engineering student reflections from immersion programs. Fifteen reflection prompts that align with program learning goals were developed and structured based on the DEAL model for critical reflection. Undergraduate participants in a summer immersion program responded to these prompts throughout five weeks of clinical rotations. Data from two summer cohorts of participants (n = 20) were collected, and thematic analysis was performed to characterize student responses. Students reported learning about key healthcare topics, such as medical insurance, access to healthcare (and lack thereof), stakeholder perspectives, and key medical terminology and knowledge. Most reflections also noted that students could apply newly gained medical knowledge to biomedical engineering design. Further, clinical immersion provided students with a realistic view of the biomedical engineering profession and potential areas for future professional growth, with many reflections identifying the ability to communicate with a variety of professionals as key to student training. Some students reflected on conversations with patients, noting that these interactions reinvigorated their passion for the biomedical engineering field. Finally, 63% of student reflections identified instances in which patients of low socioeconomic status were disadvantaged in health care settings. Clinical immersion programs can help close the gap between academic learning and the practical experience demands of the field, as design skills and product development experience are becoming increasingly necessary for biomedical engineers. Our work initiates efforts toward more rigorous analysis of students' reactions and experiences, particularly around socioeconomic and demographic factors, which may provide guidance for continuous improvement and development of clinical experiences for biomedical engineers.
Clinical informatics is a cornerstone in the delivery of safe and quality critical care in Australia and New Zealand. Recent advances in the field of clinical informatics, including new technologies that digitise healthcare data, improved methods of capturing and storing these data, as well as innovative analytic methods using machine learning and artificial intelligence, present exciting new opportunities to leverage data for improving the delivery of critical care and patient outcomes. However, ICU training in Australian and New Zealand does not adequately address capability gaps in this area, potentially leaving future intensivists without the necessary skills to provide leadership in the application of informatics within ICUs. This highlights the need to examine how competency in clinical informatics can be incorporated into ICU training, potentially through a range of activities such as curriculum redesign, the formal project, and workshops or datathons. Further work to identify relevant informatics competencies and methods to develop and assess these competencies within ICU training is needed.
Clinical judgment is an essential component of safe nursing practice that pre-licensure nursing students should develop by graduation from accredited nursing programs. For novice nurses, the consequences of underdeveloped clinical judgment skills that do not meet the demands of clinical practice are serious. This theory-practice gap correlates with increased numbers of errors occurring during care delivery, resulting in poorer patient outcomes. From a student perspective, this problem correlates with lower first-time pass rates on the NCLEX licensing exam. For nurse educators, there are uncertainties about how to resolve this complex and costly problem, but faculty development is one evidence-based solution to explore. The purpose of this article is to describe a three-pronged quality improvement project consisting of: 1) a faculty development session to teach faculty to implement the National Council State Boards of Nursing's Clinical Judgment Model (CJM) in their courses; 2) the use of the Clinical Judgment Tool; and 3) the implementation of a faculty champion to sustain and maintain ongoing faculty momentum to foster clinical judgment. This article focuses on how faculty can develop their own CJM faculty development session and use a CJM across the course curriculum.
Clinical learning experiences are known to exacerbate nursing student anxiety, causing them to present to clinical placement in distressed, meaning anxious, states. Students already living with anxiety are also more likely to suffer setbacks at this time. Supervising Registered Nurses (RNs), in the clinical settings, struggle to support this student cohort. A professional development activity was designed to introduce RNs to higher education and nursing students' known mental health concerns (such as anxiety) and to arm them with strategies for working with distressed students. A research approach that enabled researchers to collaborate with participants was employed to design the intervention. A study evaluated the impact of the educational intervention with 45 Australian RN supervisors. Two tailed T-tests were chosen to explore the statistical difference between pre- and post-test mean results across the survey items. A 95% confidence interval was used. Statistical significance was set at <0.05. The evaluation indicated the activity could be useful for improving supervising RNs' mental health literacy, thus enhancing their understanding of how to work with distressed students. Participants recommended the activity be offered to any staff supporting student clinical learning. Collaboration between nursing researchers and nursing clinical staff produced a meaningful professional development activity and motivated the participants to increase their mental health literacy and understanding of strategies to support distressed students. Future projects should adopt similar approaches that would support both RNs' ability to support students' during clinical learning and students in distress would also benefit.
Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.
Clinical mentors are experienced practitioners who play an important role in encouraging the professional development of students in clinical areas. The responsibility of clinical mentorship in nursing is often difficult to maintain. However, there is a dire need for clinical mentorship in maternity units, especially in South African hospitals were high maternal mortality rates remain alarmingly high. This study aimed to describe the perceptions of registered midwives regarding the clinical mentorship of midwifery students. The study occurred in a semi-rural state regional hospital in the eThekwini district, KwaZulu-Natal. A qualitative exploratory and descriptive design was conducted using in-depth individual interviews with midwives in maternity units. A purposive and convenient sampling method recruited 17 registered midwives from 3 maternity care areas within a single setting. Interviews were audio-recorded and all data were analysed using qualitative content analysis. Five categories emanated from this study namely, sharing knowledge and skills; encouraging role model behaviour; promoting self-worth; Is a challenging task; and requiring additional support. Clinical mentorship has a reciprocal effect on teaching and learning in maternity care areas and encouraged registered midwives to lead as role-models. The process demands competence, professionalism, and leading by example. Despite the confidence, satisfaction and interest in clinical mentorship, registered midwives often find the process challenged by patient care priorities. Therefore, registered midwives require additional support to mentor students in clinical practice. This article shows that clinical mentorship places various challenges on registered midwives and formal mentorship training could be beneficial.
Clinical metaproteomics has the potential to offer insights into the host-microbiome interactions underlying diseases. However, the field faces challenges in characterizing microbial proteins found in clinical samples, usually present at low abundance relative to the host proteins. As a solution, we have developed an integrated workflow coupling mass spectrometry-based analysis with customized bioinformatic identification, quantification, and prioritization of microbial proteins, enabling targeted assay development to investigate host-microbe dynamics in disease. The bioinformatics tools are implemented in the Galaxy ecosystem, offering the development and dissemination of complex bioinformatic workflows. The modular workflow integrates MetaNovo (to generate a reduced protein database), SearchGUI/PeptideShaker and MaxQuant [to generate peptide-spectral matches (PSMs) and quantification], PepQuery2 (to verify the quality of PSMs), Unipept (for taxonomic and functional annotation), and MSstatsTMT (for statistical analysis). We have utilized this workflow in diverse clinical samples, from the characterization of nasopharyngeal swab samples to bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness via analysis of residual fluid from cervical swabs. The complete workflow, including training data and documentation, is available via the Galaxy Training Network, empowering non-expert researchers to utilize these powerful tools in their clinical studies. Clinical metaproteomics has immense potential to offer functional insights into the microbiome and its contributions to human disease. However, there are numerous challenges in the metaproteomic analysis of clinical samples, including handling of very large protein sequence databases for sensitive and accurate peptide and protein identification from mass spectrometry data, as well as taxonomic and functional annotation of quantified peptides and proteins to enable interpretation of results. To address these challenges, we have developed a novel clinical metaproteomics workflow that provides customized bioinformatic identification, verification, quantification, and taxonomic and functional annotation. This bioinformatic workflow is implemented in the Galaxy ecosystem and has been used to characterize diverse clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its effectiveness and availability for use by the research community via analysis of residual fluid from cervical swabs.
Clinical neuropsychology relies on the use of appropriate test norms. Normative studies frequently stratify based on age, education, sex, and race. None to date has reported norms based on literacy, despite the substantial evidence that literacy impacts cognitive functioning. Some researchers have suggested that literacy is a more accurate reflection of academic achievement and quality of education than years of education, particularly for African Americans. The current study provides literacy-based normative data for multiple neuropsychological measures based on a sample of predominantly low socioeconomic status African Americans. These normative data should improve the diagnostic accuracy of performances by African-American clients with similar demographic backgrounds.
Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.