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The Perceptions of a Nurse-Led Telehealth Discharge Program on Continuity of Care for Pediatric Rheumatology Patients
Get PDFWhen pediatric rheumatology patients are discharged from a hospital, they are given a complex discharge plan which involves responsibilities in self-managing their care at home. This creates an overwhelming time for patients and families who may have questions or concerns during this time. In order to address this care gap and provide continuity of care, this quality improvement project implemented a nurse-led telehealth discharge visit intervention to 15 pediatric rheumatology patients during the transition from hospital discharge to outpatient care. This quality improvement (QI) project also aims to describe the patients’ perceptions of the nurse-led telehealth discharge intervention on continuity of care through the use of the Patient Continuity of Care Questionnaire (PCCQ) #1 – Hospital discharge and PCCQ #2 - Post nurse led telehealth visit survey at a large pediatric teaching hospital in Northern California.
A cross sectional, descriptive analysis of the quantitative data from the 14 participants who completed the PCCQ #1 – Hospital discharge survey and 13 participants who completed the PCCQ #2 – Post nurse led telehealth visit survey were performed. Content analysis of the qualitative data from the open-ended questions of both PCCQ #1 and PCCQ #2 were also performed. The results of this QI project showed that utilizing a nurse-led telehealth discharge visit to pediatric rheumatology patients within 30 days post hospital discharge had shown high patient satisfaction and provided continuity of care between the critical transition from hospital discharge to outpatient care. Emotional support, psychosocial support, knowledge support, care coordination support and patient safety such as avoidance of medication errors were provided to pediatric rheumatology patients through the utilization of the nurse-led telehealth discharge visit
Quantitative analysis of Machine Learning model performance and the need to consider explainability
Get PDFThis presentation, titled Quantitative analysis of Machine Learning model performance and the need to consider explainability, delves into various metrics used for evaluating machine learning models. It thoroughly examines fundamental classification metrics like accuracy, precision, recall, and F-score, while also discussing more advanced measures such as the Kappa Statistic and Matthews Correlation Coefficient (MCC), particularly highlighting their relevance in scenarios with imbalanced datasets. The presentation underscores the importance of model accuracy in real-world applications and briefly introduces regression metrics like R-squared and F-statistic. Additionally, it addresses challenges related to data imbalance and fairness in ML models, stressing the critical need for explainability alongside performance.
More details: https://events.vtools.ieee.org/m/442073
Video Recording: https://ieeetv.ieee.org/channels/computer-society/quantitative-analysis-of-machine-learning-model-performance-and-the-need-to-consider-explainabilityhttps://scholarworks.sjsu.edu/oer/1013/thumbnail.jp
Liu, Mengxiong
Get PDFUniversity of Michigan, Ann Arbor, MI, School of Informational & Library Studies, Ph.D., 1990
University of Denver, Denver, CO, Graduate School of Librarianship & Information Management, M.L.S., 1983
International Studies University, Shanghai, China, English Department, B.A., 1968https://scholarworks.sjsu.edu/erfa_bios/1273/thumbnail.jp
Defect Detection and Closed-loop Feedback Using Machine Learning for Fused Filament Fabrication
No full textThe objective of this study was to develop a closed-loop system for a commercial fused filament fabrication printer based on visual machine learning inspection of common defects. Convolutional neural network was used to identify levels of common defects: stringing, over/under-extrusion, and weak infill. Transfer learning was used to adapt a pre-trained model to fit this problem, as it involves incrementally fine-tuning the model parameters to new data. The observation model achieved an accuracy of 92.86% on validation data set and 90.0% on the testing data set. By modifying the input G-code, the custom program could adjust the feed-rate, nozzle temperature, material extrusion amount, and fan speed to correct for identified extrusion defects
Factor Structure and Psychometric Properties of the Muscle Dysmorphic Disorder Inventory (Mddi) Among Transgender Women
No full textMuscle dysmorphia (MD) is characterized by extreme preoccupation with insufficient muscularity that prompts pathological behaviors and distress/impairment. The Muscle Dysmorphic Disorder Inventory (MDDI) — a widely used measure of MD symptoms — has yet to be validated among transgender women, despite emerging evidence suggesting risk for muscularity-oriented concerns in this population. We examined the MDDI factor structure as well as the reliability and validity of its subscales in a sample of 181 transgender women ages 19–73 years who participated in a national longitudinal cohort study of U.S. sexual and gender minority adults. Confirmatory factor analysis was used to examine model fit for the original three-factor structure of the MDDI (drive for size, appearance intolerance, functional impairment). A re-specified three-factor model allowing covariance of residuals for two conceptually related items demonstrated good overall fit (χ2/df = 1.33, CFI =.94, TLI =.93, RMSEA =.06 [95 % CI =.01,.09], SRMR =.07). Moreover, results supported the internal consistency and convergent and discriminant validity of the MDDI subscales in transgender women. Findings inform the use of the MDDI among transgender women and provide a foundation to support future research on the MDDI and MD symptoms among gender minority populations
Cosmogenic radionuclides in meteorites from the Otway Massif blue ice area, Antarctica: An unusual, well-preserved H5 chondrite strewn field
Get PDFThe US Antarctic Search for Meteorites (ANSMET) discovered a dense cluster of 88 ordinary chondrites with a total mass of more than 100 kg on a blue ice area (BIA) of 1.6 × 0.3 km2 near the Otway Massif, Grosvenor Mountains, Antarctica. The larger masses (weighing up to 29 kg) were found at one end of an oval-shaped pattern and the smaller masses (50–200 g) at the other end. We measured concentrations of the cosmogenic radionuclides 10Be (half-life—1.36 × 106 year) and 36Cl (3.01 × 105 year) in the metal fraction of 17 H chondrites, including 14 fragments of this cluster, to verify the hypothesis that this meteorite cluster on the Otway Massif BIA represents a meteorite strewn field produced by the atmospheric breakup of a single meteoroid. The 10Be and 36Cl concentrations confirm that 10 out of 14 H chondrites from different locations within this small area are paired fragments of the same meteorite fall, while the four other H chondrites represent two additional—smaller—falls. The radionuclides suggest a pre-atmospheric mass of 200–400 kg for the large pairing group, suggesting that 25%–50% of the meteoroid survived atmospheric entry. Based on the distribution of the paired H chondrites and evidence of their common cosmic-ray exposure history in space, we conclude that most of the 88 meteorites within this small area represent a meteorite strewn field. The small size of the strewn field suggests that the meteoroid entered at a steep angle (\u3e60°), while the low amount of fusion crust on most meteorite surfaces most likely indicates atmospheric break up at low altitude, while additional fragmentation of a large surviving fragment may have occurred during impact on the ice. This well-documented strewn field provides a good opportunity to apply model simulations of the atmospheric fragmentation of this object as a function of entry angle, velocity, and meteoroid strength. Cosmogenic 14C analyses in two members of the Otway Massif pairing group yield a terrestrial age of 15.5 ± 1.5 kyr, which represents the time elapsed since this meteorite fell on Earth. The excellent preservation of an Antarctic meteorite strewn field suggests that the Otway Massif BIA represents a relatively stagnant blue ice field
MULTI-PLATFORM CYBERBULLYING DETECTION USING NLP AND MACHINE LEARNING
No full textThe issue of cyberbullying is growing due to the online anonymity and due to online platforms having less repercussions. This research proposes for proactive measures to detect and prevent such behavior before it reaches the victim. By using data from various social media platforms and employing machine learning techniques, this research proposes an innovative system aimed at identifying and thwarting cyberbullying incidents preemptively. While existing methods have primarily focused on prediction and detection of cyberbullying incidents, there remains a significant gap in research regarding prevention strategies. This project aims to address this gap by leveraging machine learning, natural language processing (NLP), and software development techniques to proactively prevent cyberbullying. This project uses an approach that involves the implementation of blocking and warning mechanisms to intervene before harmful content reaches the intended victim, fostering a safer online environment. In our research, we have also conducted an extensive comparison of five different feature engineering methods, along with nine machine learning algorithms. These algorithms encompass three ensemble methods, four statistical methods, and two deep learning algorithms, each with two variations. Additionally, we integrate data from multiple online platforms such as Twitter, Wikipedia comments, Kaggle and YouTube, to capture varying user behaviors effectively. Recognizing that behaviors may differ across platforms, our research employs a comprehensive approach to gather insights from diverse sources. Throughout this process, the achieved accuracy across the different algorithms ranges from 87.2% to 95.5%. In this report, we will also discuss other metrics that are relevant to text classification, apart from accuracy
Gamified Learning: Applying Game Design Thinking in Education
Get PDFGamification is an Education Strategy It has gained popularity in recent years. Gamification is a means to foster student engagement and improve their learning journeys. It\u27s about applying game design elements in non-game contexts. This is primarily used in educational settings. Gamification of learning makes it more engaging and fun by introducing rewards, challenges, and competition. By adding rewards, challenges, and competition, gamification makes learning more interesting and interactive (2020).In this essay, I will be discussing the concept of gamification. Its advantages and disadvantages will be explored. It will also give examples, including Duolingo and Quizlet, to illustrate how it can have a positive influence on education. And lastly, this essay will dive into future paths for gamification in learning environments
Decadal Evolution of Ice-Ocean Interactions at a Large East Greenland Glacier Resolved at Fjord Scale With Downscaled Ocean Models and Observations
Get PDFIn recent decades, the Greenland ice sheet has been losing mass through glacier retreat and ice flow acceleration. This mass loss is linked with variations in submarine melt, yet existing ocean models are either coarse global simulations focused on decadal-scale variability or fine-scale simulations for process-based investigations. Here, we unite these scales with a framework to downscale from a global state estimate (15 km) into a regional model (3 km) that resolves circulation on the continental shelf. We further downscale into a fjord-scale model (500 m) that resolves circulation inside fjords and quantifies melt. We demonstrate this approach in Scoresby Sund, East Greenland, and find that interannual variations in submarine melt at Daugaard-Jensen glacier induced by ocean temperature variability are consistent with the decadal changes in glacier ice dynamics. This study provides a framework by which coarse-resolution models can be refined to quantify glacier submarine melt for future ice sheet projections
Cross-cultural adaptation of the Voice-related Experiences of Nonbinary Individuals - VENI to Brazilian Portuguese
Get PDFPurpose: This study aimed to translate and cross-culturally adapt the “Voice-related Experiences of Nonbinary Individuals” (VENI) to Brazilian Portuguese (BP). Methods: Cross-cultural adaptation was performed based on the combined guidelines of the World Health Organization’s (WHO) Translation Recommendations and the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). The process included five stages: a) Translation of the instrument into BP by a translator specialized in the construct and a non-specialist, both native BP speakers and fluent in English; b) Synthesis of the two translations by consensus; c) Back-translation by a translator specialized in the construct and a non-specialist, both native English speakers and fluent in BP; d) Analysis by a committee of five speech-language pathologists voice specialist and the creation of the final version; e) Pre-testing with 21 individuals from the target population, conducted virtually. Results: During the translation stage, there were disagreements regarding the title, instructions, response key, and 15 items. In the back-translation stage, there were discrepancies in the format of 12 items and the content of four items. The expert committee’s analysis led to changes in the title, instructions, one option in the response key, and eight items to meet the equivalence criteria. In the pre-test, a significantly higher proportion of usual responses to the instrument was observed when compared to the non-applicable option; this is frequently observed in instrument adaptations. Conclusion: The cross-cultural adaptation of VENI into Brazilian Portuguese was successful, resulting in the “Experiências relacionadas à Voz de Pessoas Não Binárias - VENI-Br” version