Hasil untuk "Nursing"

J. Hinkle, K. Cheever

G. R. Glenn

K. Schumacher, A. Meleis

Joseph E. Gaugler, S. Duval, K. Anderson et al.

BackgroundWhile existing reviews have identified significant predictors of nursing home admission, this meta-analysis attempted to provide more integrated empirical findings to identify predictors. The present study aimed to generate pooled empirical associations for sociodemographic, functional, cognitive, service use, and informal support indicators that predict nursing home admission among older adults in the U.S.MethodsStudies published in English were retrieved by searching the MEDLINE, PSYCINFO, CINAHL, and Digital Dissertations databases using the keywords: "nursing home placement," "nursing home entry," "nursing home admission," and "predictors/institutionalization." Any reports including these key words were retrieved. Bibliographies of retrieved articles were also searched. Selected studies included sampling frames that were nationally- or regionally-representative of the U.S. older population.ResultsOf 736 relevant reports identified, 77 reports across 12 data sources were included that used longitudinal designs and community-based samples. Information on number of nursing home admissions, length of follow-up, sample characteristics, analysis type, statistical adjustment, and potential risk factors were extracted with standardized protocols. Random effects models were used to separately pool the logistic and Cox regression model results from the individual data sources. Among the strongest predictors of nursing home admission were 3 or more activities of daily living dependencies (summary odds ratio [OR] = 3.25; 95% confidence interval [CI], 2.56–4.09), cognitive impairment (OR = 2.54; CI, 1.44–4.51), and prior nursing home use (OR = 3.47; CI, 1.89–6.37).ConclusionThe pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of NH admission (e.g., 3 or more ADL dependencies, cognitive impairment, prior NH use). These results could be utilized as weights in the construction and validation of prognostic tools to estimate risk for NH entry over a multi-year period.

D. Mezibov

M. Hockenberry, David B. Wilson

J. Aucoin

H. S. Spence Laschinger, M. Leiter

I. Cameron, G. Murray, L. Gillespie et al.

Carol A. Wong, G. Cummings, L. Ducharme

AIM Our aim was to describe the findings of a systematic review of studies that examine the relationship between nursing leadership practices and patient outcomes. BACKGROUND As healthcare faces an economic downturn, stressful work environments, upcoming retirements of leaders and projected workforce shortages, implementing strategies to ensure effective leadership and optimal patient outcomes are paramount. However, a gap still exists in what is known about the association between nursing leadership and patient outcomes. METHODS Published English-only research articles that examined leadership practices of nurses in formal leadership positions and patient outcomes were selected from eight online bibliographic databases. Quality assessments, data extraction and analysis were completed on all included studies. RESULTS A total of 20 studies satisfied our inclusion criteria and were retained. Current evidence suggests relationships between positive relational leadership styles and higher patient satisfaction and lower patient mortality, medication errors, restraint use and hospital-acquired infections. CONCLUSIONS The findings document evidence of a positive relationship between relational leadership and a variety of patient outcomes, although future testing of leadership models that examine the mechanisms of influence on outcomes is warranted. IMPLICATIONS FOR NURSING MANAGEMENT Efforts by organisations and individuals to develop transformational and relational leadership reinforces organisational strategies to improve patient outcomes.

B. Kalisch, G. Landstrom, A. Hinshaw

M. Pulido-Martos, J. Augusto-Landa, Esther Lopez-Zafra

Xuejin Luo, Shiquan Sun, Runshi Zhang et al.

During surgery, scrub nurses are required to frequently deliver surgical instruments to surgeons, which can lead to physical fatigue and decreased focus. Robotic scrub nurses provide a promising solution that can replace repetitive tasks and enhance efficiency. Existing research on robotic scrub nurses relies on predefined paths for instrument delivery, which limits their generalizability and poses safety risks in dynamic environments. To address these challenges, we present a collision-free dual-arm surgical assistive robot capable of performing instrument delivery. A vision-language model is utilized to automatically generate the robot's grasping and delivery trajectories in a zero-shot manner based on surgeons' instructions. A real-time obstacle minimum distance perception method is proposed and integrated into a unified quadratic programming framework. This framework ensures reactive obstacle avoidance and self-collision prevention during the dual-arm robot's autonomous movement in dynamic environments. Extensive experimental validations demonstrate that the proposed robotic system achieves an 83.33% success rate in surgical instrument delivery while maintaining smooth, collision-free movement throughout all trials. The project page and source code are available at https://give-me-scissors.github.io/.

Jeanne Malécot, Hamed Rahimi, Jeanne Cattoni et al.

Existing human-robot interaction systems often lack mechanisms for sustained personalization and dynamic adaptation in multi-user environments, limiting their effectiveness in real-world deployments. We present HARMONI, a multimodal personalization framework that leverages large language models to enable socially assistive robots to manage long-term multi-user interactions. The framework integrates four key modules: (i) a perception module that identifies active speakers and extracts multimodal input; (ii) a world modeling module that maintains representations of the environment and short-term conversational context; (iii) a user modeling module that updates long-term speaker-specific profiles; and (iv) a generation module that produces contextually grounded and ethically informed responses. Through extensive evaluation and ablation studies on four datasets, as well as a real-world scenario-driven user-study in a nursing home environment, we demonstrate that HARMONI supports robust speaker identification, online memory updating, and ethically aligned personalization, outperforming baseline LLM-driven approaches in user modeling accuracy, personalization quality, and user satisfaction.

Dipankar Srirag, Quoc Dung Nguyen, Aditya Joshi et al.

Research in emergency triage is restricted to structured electronic health records (EHR) due to regulatory constraints on nurse-patient interactions. We introduce TriageSim, a simulation framework for generating persona-conditioned triage conversations from structured records. TriageSim enables multi-turn nurse-patient interactions with explicit control over disfluency and decision behaviour, producing a corpus of ~800 synthetic transcripts and corresponding audio. We use a combination of automated analysis for linguistic, behavioural and acoustic fidelity alongside manual evaluation for medical fidelity using a random subset of 50 conversations. The utility of the generated corpus is examined via conversational triage classification. We observe modest agreement for acuity levels across three modalities: generated synthetic text, ASR transcripts, and direct audio inputs. The code, persona schemata and triage policy prompts for TriageSim will be available upon acceptance.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Paloma Rabaey, Adrick Tench, Stefan Heytens et al.

Electronic health records (EHRs) form an invaluable resource for training clinical decision support systems. To leverage the potential of such systems in high-risk applications, we need large, structured tabular datasets on which we can build transparent feature-based models. While part of the EHR already contains structured information (e.g. diagnosis codes, medications, and lab results), much of the information is contained within unstructured text (e.g. discharge summaries and nursing notes). In this work, we propose a method for multi-modal patient-level information extraction that leverages both the tabular features available in the patient's EHR (using an expert-informed Bayesian network) as well as clinical notes describing the patient's symptoms (using neural text classifiers). We propose the use of virtual evidence augmented with a consistency node to provide an interpretable, probabilistic fusion of the models' predictions. The consistency node improves the calibration of the final predictions compared to virtual evidence alone, allowing the Bayesian network to better adjust the neural classifier's output to handle missing information and resolve contradictions between the tabular and text data. We show the potential of our method on the SimSUM dataset, a simulated benchmark linking tabular EHRs with clinical notes through expert knowledge.

Judith J. Lok, Mireille E. Schnitzer

Wang et al. (2025) use statistics to argue that sex at birth is not a biological coin toss, by noticing that repeated patterns such as Male Male Male and Female Female Female occur in the Nurses Health Study more often than patterns like Male Female Male, Male Female Female, Female Male Female, or Female Male Male. This letter shows that this over-representation is likely due to a statistical artifact, arising from parent preferences for mixed-sex children. As noticed in Angrist and Evans (1998) and supported by the data in Wang et al. (2025), parents are more likely to have a third child if their first two children are of the same sex. We show mathematically and statistically that mixed-sex preferences lead to the over-representation of patterns like Male Male Male and Female Female Female. In fact, the patterns seen in the Nurses Health Study are perfectly consistent with sex at birth being a random coin toss.

Anthony Sisti, Ellen McCreedy, Roee Gutman

In pragmatic cluster randomized controlled trials (PCRCTs), the unit of randomization may be the healthcare provider. In these studies, noncompliance can occur at both the patient and cluster levels. Some studies measure cluster-level implementation using multiple continuous metrics while documenting individual binary compliance. The complier average causal effect estimates the intervention effects among individuals that comply with the assigned intervention. However, it does not account for compliance metrics at the cluster level. When compliance with the intervention is influenced by both providers and individuals, it can be scientifically beneficial to describe the effects of the intervention between all levels of compliance. We propose a Bayesian method for PCRCTs with one-sided binary noncompliance at the individual level and one-sided partial compliance at the cluster level. Our Bayesian model classifies clusters into latent compliance strata based on pretreatment characteristics, partial compliance status, and individual outcomes. Because compliance is only observed in the treatment arm, the method imputes unobserved compliance for control clusters and the individuals within them. This approach estimates finite and super-population estimands within strata defined by both cluster- and individual-level compliance. We apply this method to the METRIcAL trial, a multi-part, pragmatic cluster randomized trial evaluating the effects of a personalized music intervention on agitation in nursing home residents with dementia.

Ruijie Wang, Jie Lu, Bo Pei et al.

Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-enhanced Immersive Multidisciplinary Simulations), a virtual simulation that integrates a large language model (Gemini-2.5-Flash), a Unity-based virtual environment engine, and a character creation pipeline to support synchronized, multimodal interactions between the user and the virtual patient. AIMS was designed to enhance collaborative clinical reasoning and health promotion competencies among students from pharmacy, medicine, nursing, and social work. A formal usability testing session was conducted in which participants assumed professional roles on a healthcare team and engaged in a mix of scripted and unscripted conversations. Participants explored the patient's symptoms, social context, and care needs. Usability issues were identified (e.g., audio routing, response latency) and used to guide subsequent refinements. Findings suggest that AIMS supports realistic, profession-specific, and contextually appropriate conversations. We discuss technical innovations of AIMS and conclude with future directions.