Sue & Bill Gross School of Nursing

As a practicing clinical nurse, a phenomenon I experienced at times was the sudden acute sense that something was going wrong with a person in care at the sub-critical unit in the hospital where I worked. In fact, many hospital nurses have their story of somethings not right in relation to a person they were caring for/with, in that the day started with them on a coherent path to healing and then suddenly the nurse feels something is going very wrong, and yet there is nothing observable that would justify such a feeling. This feeling would be called intuition by many nurses, a concept most notably theorized in nursing by Patricia Benner in her extensive program of scholarship. Benner defines intuition as understanding without rationale. Benner opposes embodied intuition and rational abstract reasoning and creates criteria for the use of each by nursing depending on whether the clinical situation is familiar or novel. The philosophical idea is that the new must be reasoned with a different mode of thought than the familiar. Charles Sanders Peirce was a philosopher of reasoning. He defined logic as the theory of reasoning, which by the end of his career he was declaring was only another name for semiotic. Peirce argued that all reasoning/semiosis is done through signs, or more accurately sign-activity. Semiotic is the philosophical schema providing the concepts and methods by which semiosis - reasoning - happens. Importantly, semiotic does not oppose different modes of thought, and conceptualizes reasoning as a process that functions in familiar as well as novel situations. In this paper I describe Peirces philosophy of semiotic. I then provide an example relevant to nursing by conducting a semiosis of the nursing scenario above to show how nursing works chance, or novelty, in a way that doesnt need to resort to rational abstract reasoning and yet is different than Benners notion of intuition.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

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School nurses are pivotal to the safety of school-aged children, particularly those who receive medications in the school setting. The purpose of this study was to explore factors associated with medication administration errors in North Carolina school districts between 2012/2013 and 2017/2018. A longitudinal study using repeated measures analysis of school health services data collected in the North Carolina Annual School Health Services and Programs Survey was conducted. Over time, the number of medication errors (p = .001) and number of medication corrective action plans (p < .0001) trended upwards. There was also an increase in medication errors when the number of schools in a district was higher (p < .0001). Conversely, there was a decrease in corrective action plans when school nurses were directly employed by the school district (p = .0471). We implore school disticts to consider the important role of school nurses to keep kids safe, healthy, and ready to learn.

ABSTRACT: Background: In Fall 2020, at the height of the COVID-19 pandemic, the importance of avoiding a simultaneous influenza and COVID-19 “twindemic” led to the implementation of socially distanced, drive-through mobile vaccination clinics. Mobile clinics have been valuable in providing primary and preventative care to underserved populations and expanding healthcare access to individuals marginalized by geographic, social, and structural barriers. Although there are ~2,000 mobile clinics throughout the United States and 120 mobile clinics providing services in California, few studies to date have evaluated neighborhood-level factors to determine whether social drivers of health (SDOH) influence the use of mobile drive-through clinics versus static clinics for immunizations. Methods: We conducted a retrospective cohort study of a total of 25,246 patients, 3,151 of whom received immunizations in 3 mobile clinics and 22,095 of whom received immunizations in 3 static clinics in Orange County from 8/1/2020 to 12/31/2020. Data were collected from patient charts on demographic characteristics. Age was stratified 0-21 years, 22-64 years, and 65 years and older. SDOH was measured using state-ranked Area Deprivation Index (ADI), a composite measure of 17 variables across income, education, employment, and housing domains by neighborhood/block group. ADI ranking was categorized into quintiles with higher ADI indices corresponding to greater levels of disadvantage. Chi-squared analysis was paired with logistic regression to examine potential associations. Significance was set at p < 0.05. Statistical analysis was conducted using SPSS (version 28) The study was approved by our institution’s IRB. Results: A similar percentage of patients who identified as White attended the mobile and static clinics (60.3% and 66.8%, respectively), while a lesser percentage of patients who were <60 years of age (60.3% vs 74.4%); identified as Hispanic (19.4% vs 58.8%); spoke Spanish (5.6% vs 33.7%); and were on public insurance (36.7% vs 74.9%) attended the mobile (vs static) clinics. Less likely to obtain vaccines through mobile clinics were those who identified as Black (OR 0.51; 95% CI 0.35, 0.75) relative to Caucasian patients, had public insurance (0.25; 0.23, 0.28) versus commercial insurance, and whose primary language was Spanish (0.29; 0.24, 0.35). Those greater than 65 years of age (7.04; 5.94, 8.34) compared to those under 21 years, and those identified as non-Hispanic (1.67; 1.44, 1.90) versus Hispanic were more likely to obtain their vaccines through the mobile clinic. Additionally, those who lived in the most disadvantaged neighborhoods were the least likely to obtain vaccines at the mobile clinic (0.67; 0.48, 0.93). Conclusions: The study demonstrates that patients who lived in more disadvantaged neighborhoods were less likely to seek vaccinations at mobile clinics. Additional work is needed to identify why the mobile influenza clinics were highly skewed towards those who lived in more advantaged areas.

Hepatitis C virus (HCV), the most common blood-borne infection, disproportionately affects people experiencing homelessness (PEH); however, HCV interventions tailored for PEH are scarce. This study utilized a community-based participatory approach to assess perceptions of HCV treatment experiences among HCV-positive PEH, and homeless service providers (HSP) to develop and tailor the "I am HCV Free" intervention which integrates primary, secondary, and tertiary care to attain and maintain HCV cure. Four focus groups were conducted with PEH (N = 30, M_age = 51.76, standard deviation 11.49, range 22-69) and HSPs (n = 10) in Central City East (Skid Row) in Los Angeles, California. An iterative, thematic approach was used to ensure the trustworthiness of the data. Barriers and facilitators emerged from the data which have the potential to impact initiating HCV treatment and completion across the HCV care continuum. Understanding and addressing barriers and strengthening facilitators to HCV treatment will aid in HCV treatment completion and cure for PEH.

Media exposure to graphic images of violence has proliferated in contemporary society, particularly with the advent of social media. Extensive exposure to media coverage immediately after the 9/11 attacks and the Boston Marathon bombings (BMB) was associated with more early traumatic stress symptoms; in fact, several hours of BMB-related daily media exposure was a stronger correlate of distress than being directly exposed to the bombings themselves. Researchers have replicated these findings across different traumatic events, extending this work to document that exposure to graphic images is independently and significantly associated with stress symptoms and poorer functioning. The media exposure-distress association also appears to be cyclical over time, with increased exposure predicting greater distress and greater distress predicting more media exposure following subsequent tragedies. The war in Israel and Gaza, which began on October 7, 2023, provides a current, real-time context to further explore these issues as journalists often share graphic images of death and destruction, making media-based graphic images once again ubiquitous and potentially challenging public well-being. For individuals sharing an identity with the victims or otherwise feeling emotionally connected to the Middle East, it may be difficult to avoid viewing these images. Through a review of research on the association between exposure to graphic images and public health, we discuss differing views on the societal implications of viewing such images and advocate for media literacy campaigns to educate the public to identify mis/disinformation and understand the risks of viewing and sharing graphic images with others.

OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the datasets imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

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This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and physical activity (step, active calories, type of activity). Ecological momentary assessments were collected from participants on daily and weekly bases containing their Positive and Negative Affect Schedule (PANAS) questionnaire and their emotional responses to COVID-19 and their health. Subjective data was also collected through monthly surveys containing standard mood and mental health surveys such as Beck Depression Inventory II (BDI-II), Brief Symptom Inventory (BSI), GAD-7, Inclusion of Other in the Self Scale (IOS-Partner), Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM), Experiences in Close Relationships Scale Short Form (ECR-S), UCLA Three-Item Loneliness Scale (ULS), Multidimensional Scale of Perceived Social Support (MSPSS), Investment Model Scale (IMS), Conflict Management Scale (CMS), etc in addition to their response to important events and COVID-19. This dataset can be used to study emotions, mood, physical activity, and lifestyle of young adults through longitudinal subjective and objective measures. This dataset also contains valuable data regarding adjustment of lifestyle and emotions during the events of 2020 and 2021 including COVID-19 discovery and lockdown, Black Life Matter movement, 2020 US presidential elections, etc. On average, participants engaged in the EMA collection study at a rate of 86% (SD=10, MIN=65, MAX=99). Smartwatch usage saw an average participation rate of 51% (SD=20, MIN=16, MAX=88), while engagement with the Oura ring averaged at 85% (SD=12, MIN=60, MAX=99).