搜索结果：Policy, politics & nursing practice

Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.

Nursing notes, an important part of Electronic Health Records (EHRs), track a patient's health during a care episode. Summarizing key information in nursing notes can help clinicians quickly understand patients' conditions. However, existing summarization methods in the clinical setting, especially abstractive methods, have overlooked nursing notes and require reference summaries for training. We introduce QGSumm, a novel query-guided self-supervised domain adaptation approach for abstractive nursing note summarization. The method uses patient-related clinical queries for guidance, and hence does not need reference summaries for training. Through automatic experiments and manual evaluation by an expert clinician, we study our approach and other state-of-the-art Large Language Models (LLMs) for nursing note summarization. Our experiments show: 1) GPT-4 is competitive in maintaining information in the original nursing notes, 2) QGSumm can generate high-quality summaries with a good balance between recall of the original content and hallucination rate lower than other top methods. Ultimately, our work offers a new perspective on conditional text summarization, tailored to clinical app

While LLMs have demonstrated medical knowledge and conversational ability, their deployment in clinical practice raises new risks: patients may place greater trust in LLM-generated responses than in nurses' professional judgments, potentially intensifying nurse-patient conflicts. Such risks highlight the urgent need of evaluating whether LLMs align with the core nursing values upheld by human nurses. This work introduces the first benchmark for nursing value alignment, consisting of five core value dimensions distilled from international nursing codes: Altruism, Human Dignity, Integrity, Justice, and Professionalism. We define two-level tasks on the benchmark, considering the two characteristics of emerging nurse-patient conflicts. The Easy-Level dataset consists of 2,200 value-aligned and value-violating instances, which are collected through a five-month longitudinal field study across three hospitals of varying tiers; The Hard-Level dataset is comprised of 2,200 dialogue-based instances that embed contextual cues and subtle misleading signals, which increase adversarial complexity and better reflect the subjectivity and bias of narrators in the context of emerging nurse-patient

Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections

This position paper situates AR beauty filters within the broader debate on Body Politics in HCI. We argue that these filters are not neutral tools but technologies of governance that reinforce racialized, gendered, and ableist beauty standards. Through naming conventions, algorithmic bias, and platform governance, they impose aesthetic norms while concealing their influence. To address these challenges, we advocate for transparency-driven interventions and a critical rethinking of algorithmic aesthetics and digital embodiment.

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring. However, the development of automatic recognition systems in this field is currently hindered by the scarcity of appropriately labeled datasets. The existing video datasets pose several limitations: 1) these datasets are small-scale in size to support comprehensive investigations of nursing activity; 2) they primarily focus on single procedures, lacking expert-level annotations for various nursing procedures and action steps; and 3) they lack temporally localized annotations, which prevents the effective localization of targeted actions within longer video sequences. To mitigate these limitations, we propose NurViD, a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours, making it approximately four times longer than the existing largest nursing activity datasets. Notably, it encompa

Given the significant influence of lawmakers' political ideologies on legislative decision-making, analyzing their impact on transportation-related policymaking is of critical importance. This study introduces a novel framework that integrates a large language model (LLM) with explainable artificial intelligence (XAI) to analyze transportation-related legislative proposals. Legislative bill data from South Korea's 21st National Assembly were used to identify key factors shaping transportation policymaking. These include political affiliations and sponsor characteristics. The LLM was employed to classify transportation-related bill proposals through a stepwise filtering process based on keywords, sentences, and contextual relevance. XAI techniques were then applied to examine the relationships between political party affiliation and associated attributes. The results revealed that the number and proportion of conservative and progressive sponsors, along with district size and electoral population, were critical determinants shaping legislative outcomes. These findings suggest that both parties contributed to bipartisan legislation through different forms of engagement, such as initi

An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. Network-based approaches, such as quantitative ethnography (QE), can bridge this methodological gap. By leveraging the strengths of both methods, QE provides profound insights into need-finding interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used QE to analyze the data, and compared their daily activities in real life with training experiences. A two-sample t-test with a large effect size (Cohen's d=1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and human factors. This is illustrated by presenting an example of using QE to analyze expert interviews with nursing assistants as caregivers to inform subsequent design processes.

We extend Langdon Winner's idea that artifacts have politics into the realm of mathematics. To do so, we first provide a list of examples showing the existence of mathematical artifacts that have politics. In the second step, we provide an argument that shows that all mathematical artifacts have politics. We conclude by showing the implications for embedding ethics into mathematical curricula. We show how acknowledging that mathematical artifacts have politics can help mathematicians design better exercises for their mathematics students.

Nursing homes are critical facilities for caring frail older adults with round-the-clock formal care and personal assistance. To ensure quality care for nursing home residents, adequate staffing level is of great importance. Current nursing home staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to minimize labor costs subject to heterogeneous service demand of nursing home residents under various scenarios of census. We propose a data-driven analysis framework to model heterogeneous service demand of nursing home residents and further identify appropriate staffing strategies by combing survival model and computer simulation techniques as well as domain knowledge. Specifically, in the analysis, we develop an agent-based simulation tool consisting of four main modules, namely individual length of stay predictor, individual daily staff time generator, facility level staffing strategy evaluator, and graphical user interface. We use real nursing home data to validate the proposed model, and demonstrate that the identifi

Considerable research effort has been devoted to the study of Policy in the domain of Information Security Management (ISM). However, our review of ISM literature identified four key deficiencies that reduce the utility of the guidance to organisations implementing policy management practices. This paper provides a comprehensive overview of the management practices of information security policy and develops a practice-based model that addresses the four aforementioned deficiencies. The model provides comprehensive guidance to practitioners on the activities security managers must undertake for security policy development and allows practitioners to benchmark their current practice with the models suggested best practice. The model contributes to theory by mapping existing information security policy research in terms of the defined management practices.

Background: Telephone nursing is the first line of contact for many care-seekers and aims at optimizing the performance of the healthcare system by supporting and guiding patients to the correct level of care and reduce the amount of unscheduled visits. Good statistical models that describe the effects of telephone nursing are important in order to study its impact on healthcare resources and evaluate changes in telephone nursing procedures. Objective: To develop a valid model that captures the complex relationships between the nurse's recommendations, the patients' intended actions and the patients' health seeking behavior. Using the model to estimate the effects of telephone nursing on patient behavior, healthcare utilization, and infer potential cost savings. Methods: Bayesian ordinal regression modeling of data from randomly selected patients that received telephone nursing. Inference is based on Markov Chain Monte Carlo methods, model selection using the Watanabe-Akaike Information Criteria, and model validation using posterior predictive checks on standard discrepancy measures. Results and Conclusions: We present a robust Bayesian ordinal regression model that predicts 76% of

The flexibility level allowed in nursing care delivery and uncertainty in infusion durations are very important factors to be considered during the chemotherapy schedule generation task. The nursing care delivery scheme employed in an outpatient chemotherapy clinic (OCC) determines the strictness of the patient-to-nurse assignment policies, while the estimation of infusion durations affects the trade-off between patient waiting time and nurse overtime. We study the problem of daily scheduling of patients, assignment of patients to nurses and chairs under uncertainty in infusion durations for an OCC that functions according to any of the three commonly used nursing care delivery models representing fully flexible, partially flexible, and inflexible care models, respectively. We develop a two-stage stochastic mixed-integer programming model that is valid for the three care delivery models to minimize expected weighted cost of patient waiting time and nurse overtime. We propose multiple variants of a scenario grouping-based decomposition algorithm to solve the model using data of a major university oncology hospital. The variants of the algorithm differ from each other according to th

Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments exhibit non-stationary dynamics or are subject to changing performance goals, requiring updates to the learned policy. This leads to a fundamental challenge: how to update an RL policy while preserving its safety properties on previously encountered tasks? The majority of current approaches either do not provide formal guarantees or verify policy safety only a posteriori. We propose a novel a priori approach to safe policy updates in continual RL by introducing the Rashomon set: a region in policy parameter space certified to meet safety constraints within the demonstration data distribution. We then show that one can provide formal, provable guarantees for arbitrary RL algorithms used to update a policy by projecting their updates onto the Rashomon set. Empirically, we validate this approach across grid-world navigation environments (Frozen Lake and Poisoned Apple) where we guarantee an a priori provably deterministic safety on the source task during downstream adaptation. In contrast, we observe that regularisation-based baselines e

The development of modern nursing and consequently nursing research in Ex- Yugoslavia is about a century old. To profile the development, volume, and content of nursing research we completed a performance and spatial bibliometric analysis combined with synthetic content analysis to identify the most productive countries and institutions, most prolific source titles, country cooperation, publication production trends, the content of research and hot topics. The corpus was harvested from the Web of Science All databases and contained 1380 papers. Slovenia was the most productive country, followed by Croatia and Serbia. The synthetic content analysis demonstrated that nursing research in ex-Yugoslavian countries is growing both in scope and number of publications, notwithstanding the fact that research content differs between countries and it seems that each country is focused on their local health problems. A substantial part of the research is published in national journals in national languages however, it is noteworthy to note that some ex-Yugoslavian authors have succeeded in publishing their research in top nursing journals. The study also revealed substantial international coop

Data is the foundation of any scientific, industrial or commercial process. Its journey typically flows from collection to transport, storage, management and processing. While best practices and regulations guide data management and protection, recent events have underscored its vulnerability. Academic research and commercial data handling have been marred by scandals, revealing the brittleness of data management. Data, despite its importance, is susceptible to undue disclosures, leaks, losses, manipulation, or fabrication. These incidents often occur without visibility or accountability, necessitating a systematic structure for safe, honest, and auditable data management. In this paper, we introduce the concept of Honest Computing as the practice and approach that emphasizes transparency, integrity, and ethical behaviour within the realm of computing and technology. It ensures that computer systems and software operate honestly and reliably without hidden agendas, biases, or unethical practices. It enables privacy and confidentiality of data and code by design and by default. We also introduce a reference framework to achieve demonstrable data lineage and provenance, contrasting i

In an aging population, elderly patient safety is a primary concern at hospitals and nursing homes, which demands for increased nurse care. By performing nurse activity recognition, we can not only make sure that all patients get an equal desired care, but it can also free nurses from manual documentation of activities they perform, leading to a fair and safe place of care for the elderly. In this work, we present a multimodal transformer-based network, which extracts features from skeletal joints and acceleration data, and fuses them to perform nurse activity recognition. Our method achieves state-of-the-art performance of 81.8% accuracy on the benchmark dataset available for nurse activity recognition from the Nurse Care Activity Recognition Challenge. We perform ablation studies to show that our fusion model is better than single modality transformer variants (using only acceleration or skeleton joints data). Our solution also outperforms state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is available at \url{https://github.com/Momilijaz96/MMT_for_NCRC}.

The innovations emerging at the frontier of artificial intelligence (AI) are poised to create historic opportunities for humanity but also raise complex policy challenges. Continued progress in frontier AI carries the potential for profound advances in scientific discovery, economic productivity, and broader social well-being. As the epicenter of global AI innovation, California has a unique opportunity to continue supporting developments in frontier AI while addressing substantial risks that could have far reaching consequences for the state and beyond. This report leverages broad evidence, including empirical research, historical analysis, and modeling and simulations, to provide a framework for policymaking on the frontier of AI development. Building on this multidisciplinary approach, this report derives policy principles that can inform how California approaches the use, assessment, and governance of frontier AI: principles rooted in an ethos of trust but verify. This approach takes into account the importance of innovation while establishing appropriate strategies to reduce material risks.