Hasil untuk "Nursing"

I. Hulatt

Holli A. DeVon, Michelle Block, Patricia Moyle-Wright et al.

Danuta M. Wojnar, K. Swanson

M. Sandelowski

E. Goffman

Sergi Foix, Jaume Oriol, Carme Torras et al.

Separating thin, flexible layers that must be individually grasped is a common but challenging manipulation primitive for most off-the-shelf grippers. A prominent example arises in clinical settings: the opening of sterile flat pouches for the preparation of the operating room, where the first step is to separate and grasp the flaps. We present a novel gripper design and opening strategy that enables reliable flap separation and robust seal opening. This capability addresses a high-volume repetitive hospital procedure in which nurses manually open up to 240 bags per shift, a physically demanding task linked to musculoskeletal injuries. Our design combines an active dented-roller fingertip with compliant fingers that exploit environmental constraints to robustly grasp thin flexible flaps. Experiments demonstrate that the proposed gripper reliably grasps and separates sealed bag flaps and other thin-layered materials from the hospital, the most sensitive variable affecting performance being the normal force applied. When two copies of the gripper grasp both flaps, the system withstands the forces needed to open the seals robustly. To our knowledge, this is one of the first demonstrations of robotic assistance to automate this repetitive, low-value, but critical hospital task.

Elihuruma Eliufoo Stephano, Tian Yusheng, Li Yamin

Abstract Background Mild cognitive impairment (MCI) and dementia are still global public health challenges, with an increased prevalence in sub-Saharan Africa (SSA) due to an increase in aging population. Despite different studies estimating the prevalence, there is still a lack of current and updated reviews that have provided the estimated proportions in SSA. Therefore, this protocol will help in developing the review that will help in understanding the epidemiological trends of MCI and dementia in this region, which is crucial for informing and adding knowledge, setting interventions, and planning healthcare. Methods This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Meta-analysis of Observational Studies in Epidemiology (MOOSE). We will search studies from an electronic database and manually search from listed references published after 2010 reporting on the prevalence of MCI and dementia among older adults. Two independent reviewers will extract data, and the quality will be assessed using a standardized checklist. Statistical analysis will be performed using Stata version 18 to present descriptive findings, forest plots, subgroup analysis, and meta-regression. Conclusion This study’s findings will provide updated estimates of MCI and dementia prevalence, filling a gap in the current literature essential for developing context-based interventions and health policies tailored to the needs of older adults. Prospero Registration: CRD42024542596

Chen Chen, Jianing Yin, Jiannong Cao et al.

Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involving bedside interviews and manual documentation, are time-consuming and labor-intensive. Although existing digital solutions, such as web questionnaires and intelligent automated calls, can alleviate the workload of nurses to a certain extent, they either deliver an inflexible scripted interaction or face private information leakage issues. To address these limitations, this paper introduces FollowUpBot, an LLM-powered edge-deployed robot for postoperative care and monitoring. It allows dynamic planning of optimal routes and uses edge-deployed LLMs to conduct adaptive and face-to-face conversations with patients through multiple interaction modes, ensuring data privacy. Moreover, FollowUpBot is capable of automatically generating structured postoperative follow-up reports for healthcare institutions by analyzing patient interactions during follow-up. Experimental results demonstrate that our robot achieves high coverage and satisfaction in follow-up interactions, as well as high report generation accuracy across diverse field types. The demonstration video is available at https://www.youtube.com/watch?v=_uFgDO7NoK0.

Arpana Sinhal, Anay Sinhal, Amit Sinhal

Healthcare professionals, particularly nurses, face elevated occupational stress, a concern amplified during the COVID-19 pandemic. While wearable sensors offer promising avenues for real-time stress monitoring, existing studies often lack comprehensive datasets and robust analytical frameworks. This study addresses these gaps by introducing a multimodal dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature. A systematic literature review identified limitations in prior stress-detection methodologies, particularly in handling class imbalance and optimizing model generalizability. To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE), ensuring balanced representation of stress states. Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking Classifier to leverage their collective predictive strengths. By using a publicly accessible dataset and a reproducible analytical pipeline, this work advances the development of deployable stress-monitoring systems, offering practical implications for safeguarding healthcare workers' mental health. Future research directions include expanding demographic diversity and exploring edge-computing implementations for low latency stress alerts.

Lucas S. Kupssinskü, Marco N. Bochernitsan, Jordan Kopper et al.

We propose DeCoDi, a debiasing procedure for text-to-image diffusion-based models that changes the inference procedure, does not significantly change image quality, has negligible compute overhead, and can be applied in any diffusion-based image generation model. DeCoDi changes the diffusion process to avoid latent dimension regions of biased concepts. While most deep learning debiasing methods require complex or compute-intensive interventions, our method is designed to change only the inference procedure. Therefore, it is more accessible to a wide range of practitioners. We show the effectiveness of the method by debiasing for gender, ethnicity, and age for the concepts of nurse, firefighter, and CEO. Two distinct human evaluators manually inspect 1,200 generated images. Their evaluation results provide evidence that our method is effective in mitigating biases based on gender, ethnicity, and age. We also show that an automatic bias evaluation performed by the GPT4o is not significantly statistically distinct from a human evaluation. Our evaluation shows promising results, with reliable levels of agreement between evaluators and more coverage of protected attributes. Our method has the potential to significantly improve the diversity of images it generates by diffusion-based text-to-image generative models.

Rodrigo Lankaites Pinheiro, Dario Landa-Silva, Jason Atkin

Understanding the relationships between objectives in a multiobjective optimisation problem is important for developing tailored and efficient solving techniques. In particular, when tackling combinatorial optimisation problems with many objectives, that arise in real-world logistic scenarios, better support for the decision maker can be achieved through better understanding of the often complex fitness landscape. This paper makes a contribution in this direction by presenting a technique that allows a visualisation and analysis of the local and global relationships between objectives in optimisation problems with many objectives. The proposed technique uses four steps: First, the global pairwise relationships are analysed using the Kendall correlation method; then, the ranges of the values found on the given Pareto front are estimated and assessed; next, these ranges are used to plot a map using Gray code, similar to Karnaugh maps, that has the ability to highlight the trade-offs between multiple objectives; and finally, local relationships are identified using scatter plots. Experiments are presented for three combinatorial optimisation problems: multiobjective multidimensional knapsack problem, multiobjective nurse scheduling problem, and multiobjective vehicle routing problem with time windows . Results show that the proposed technique helps in the gaining of insights into the problem difficulty arising from the relationships between objectives.

Xin Chen, Jiameng Lyu, Shilin Yuan et al.

We study nonstationary newsvendor problems under nonparametric demand models and general distributional measures of nonstationarity, addressing the practical challenges of unknown degree of nonstationarity and demand censoring. We propose a novel distributional-detection-and-restart framework for learning in nonstationary environments, and instantiate it through two efficient algorithms for the uncensored and censored demand settings. The algorithms are fully adaptive, requiring no prior knowledge of the degree and type of nonstationarity, and offer a flexible yet powerful approach to handling both abrupt and gradual changes in nonstationary environments. We establish a comprehensive optimality theory for our algorithms by deriving matching regret upper and lower bounds under both general and refined structural conditions with nontrivial proof techniques that are of independent interest. Numerical experiments using real-world datasets, including nurse staffing data for emergency departments and COVID-19 test demand data, showcase the algorithms' superior and robust empirical performance. While motivated by the newsvendor problem, the distributional-detection-and-restart framework applies broadly to a wide class of nonstationary stochastic optimization problems. Managerially, our framework provides a practical, easy-to-deploy, and theoretically grounded solution for decision-making under nonstationarity.

Vishalie Shah, Julia Hatamyar, Taufik Hidayat et al.

This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia's conditional cash transfer scheme on maternal health care utilisation. Using randomised programme assignment as an instrument for enrollment in the scheme, we estimate conditional local average treatment effects for four key outcomes: good assisted delivery, delivery in a health care facility, pre-natal visits, and post-natal visits. We find significant treatment effect heterogeneity by supply-side characteristics, even though supply-side readiness was taken into account during programme development. Mothers in areas with more doctors, nurses, and delivery assistants were more likely to benefit from the programme, in terms of increased rates of good assisted delivery outcome. We also find large differences in benefits according to indicators of household poverty and survey wave, reflecting the possible impact of changes in programme design in its later years. The impact on post-natal visits in 2013 displayed the largest heterogeneity among all outcomes, with some women less likely to attend post-natal check ups after receiving the cash transfer in the long term.

Shaina Raza, Maximus Powers, Partha Pratim Saha et al.

Text-to-Image (TTI) models are powerful creative tools but risk amplifying harmful social biases. We frame representational societal bias assessment as an image curation and evaluation task and introduce a pilot benchmark of occupational portrayals spanning five socially salient roles (CEO, Nurse, Software Engineer, Teacher, Athlete). Using five state-of-the-art models: closed-source (DALLE 3, Gemini Imagen 4.0) and open-source (FLUX.1-dev, Stable Diffusion XL Turbo, Grok-2 Image), we compare neutral baseline prompts against fairness-aware controlled prompts designed to encourage demographic diversity. All outputs are annotated for gender (male, female) and race (Asian, Black, White), enabling structured distributional analysis. Results show that prompting can substantially shift demographic representations, but with highly model-specific effects: some systems diversify effectively, others overcorrect into unrealistic uniformity, and some show little responsiveness. These findings highlight both the promise and the limitations of prompting as a fairness intervention, underscoring the need for complementary model-level strategies. We release all code and data for transparency and reproducibility https://github.com/maximus-powers/img-gen-bias-analysis.

Utku Ozbulak, Seyed Amir Mousavi, Francesca Tozzi et al.

Real-time video segmentation is a promising opportunity for AI-assisted surgery, offering intraoperative guidance by identifying tools and anatomical structures. Despite growing interest in surgical video segmentation, annotation protocols vary widely across datasets -- some provide dense, frame-by-frame labels, while others rely on sparse annotations sampled at low frame rates such as 1 FPS. In this study, we investigate how such inconsistencies in annotation density and frame rate sampling influence the evaluation of zero-shot segmentation models, using SAM2 as a case study for cholecystectomy procedures. Surprisingly, we find that under conventional sparse evaluation settings, lower frame rates can appear to outperform higher ones due to a smoothing effect that conceals temporal inconsistencies. However, when assessed under real-time streaming conditions, higher frame rates yield superior segmentation stability, particularly for dynamic objects like surgical graspers. To understand how these differences align with human perception, we conducted a survey among surgeons, nurses, and machine learning engineers and found that participants consistently preferred high-FPS segmentation overlays, reinforcing the importance of evaluating every frame in real-time applications rather than relying on sparse sampling strategies. Our findings highlight the risk of evaluation bias that is introduced by inconsistent dataset protocols and bring attention to the need for temporally fair benchmarking in surgical video AI.

Geon A Kim, Mi-Jin Lee, Eun Pyo Kim et al.

BYVET JOINT HEAL^TM (BJH) contains mucopolysaccharide protein, chondroitin sulfate, type II collagen, and omega-3 fatty acids, which protect and prevent osteoarthritis (OA)-associated tissue damage and degradation in dogs and cats. This study aimed to generate a novel dietary supplement and evaluate its prevention and therapeutic efficacy in an OA Sprague Dawley rat model induced using monosodium iodoacetate (MIA). Negative control, MIA-induced OA control (MIA), OA rats treated with BJH three weeks after (M+BJH3) and those treated two weeks before and three weeks after OA induction (BJH2+M+BJH3) groups were assigned. M+BJH3 and BJH2+M+BJH3 had similar mean body weight increases until 29 days. BJH2+M+BJH3 showed a significantly higher body weight than M+BJH3 and MIA on the final day. Interleukin-1β in BJH2+M+BJH3 was significantly lower than that in MIA. Tumor necrosis factor-α, aggrecan, matrix metalloproteinases13, and cyclooxygenase-2 levels in M+BJH3 and BJH2+M+BJH3 significantly differed compared to those in MIA. BJH administration before OA induction significantly decreased OA severity and functional recovery. Consuming a BJH supplement showed modifying and chondroprotective effects and significantly reduced cartilage degeneration and inflammation with no side effects. Hence, our findings demonstrate the potential of using BJH as a safe therapeutic and preventive supplement for OA and associated cartilage abnormalities. Also, 30 dogs diagnosed with OA by a veterinarian participated in the clinical trial, and BJH was provided for 8 weeks. Blood tests (CBC, serum chemistry) and joint assessment were performed before and after the feeding, and the effects of a BJH supplement were compared. BJH supplement was easy to administer, and no side effects were reported. Feeding BJH supplementation alone to dogs with arthritis had an overall positive effect on arthritis scores for 8 weeks without any other treatment, including non-steroidal drugs.

Lin Ge, Ce Yang, David Zucker et al.

Environmental exposures are often defined using buffer zones around geocoded home addresses, but these static boundaries can miss dynamic daily activity patterns, leading to biased results. This paper presents a novel measurement error correction method for spatially defined environmental exposures within a survival analysis framework using the Cox proportional hazards model. The method corrects high-dimensional surrogate exposures from geocoded residential data at multiple buffer radii by applying principal component analysis for dimension reduction and leveraging external GPS-tracked validation datasets containing true exposure measurements. It also derives the asymptotic properties and variances of the proposed estimators. Extensive simulations are conducted to evaluate the performance of the proposed estimators, demonstrating its ability to improve accuracy in estimated exposure effects. An illustrative application assesses the impact of greenness exposure on depression incidence in the Nurses' Health Study (NHS). The results demonstrate that correcting for measurement error significantly enhances the accuracy of exposure estimates. This method offers a critical advancement for accurately assessing the health impacts of environmental exposures, outperforming traditional static buffer approaches.

Gang Xu, Xin Zhou, Molin Wang et al.

One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual's nearest monitor. This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in estimating the health effects of air pollution and its constituents, especially when evaluating the causal effects of correlated multi-pollutant constituents measured with correlated error. This paper addresses estimation and inference for the causal effect of one constituent in the presence of other PM2.5 constituents, accounting for measurement error and correlations. We used a linear regression calibration model, fitted with generalized estimating equations in an external validation study, and extended a double/debiased machine learning (DML) approach to correct for measurement error and estimate the effect of interest in the main study. We demonstrated that the DML estimator with regression calibration is consistent and derived its asymptotic variance. Simulations showed that the proposed estimator reduced bias and attained nominal coverage probability across most simulation settings. We applied this method to assess the causal effects of PM2.5 constituents on cognitive function in the Nurses' Health Study and identified two PM2.5 constituents, Br and Mn, that showed a negative causal effect on cognitive function after measurement error correction.

ZHANG Tingting (张婷婷), HUANG Yanping (黄砚萍)

Diabetic gastroparesis is a common complication in diabetic patients. It is manifested as recurrent nausea, vomiting, early saturation and abdominal fullness. The main pathophysiology is a condition that slows down the emptying of the stomach. In recent years, the external treatment of Traditional Chinese Medicine in the treatment of diabetic gastroparesis has been widely used in clinical practice, which can improve the clinical symptoms of patients. By searching the relevant literature, this paper briefly listed external treatment of Traditional Chinese Medicine, and looked forward to the future development direction of new nursing technology. (糖尿病胃轻瘫是糖尿病常见并发症之一, 表现为反复发作恶心、呕吐、早饱和上腹饱胀, 主要病理生理是胃排空延缓。近年来中医外治法治疗糖尿病胃轻瘫在临床实践中得到广泛应用, 可有效改善患者临床症状。本文通过检索相关文献, 简要列举糖尿病胃轻瘫中医外治疗法, 旨在为今后糖尿病并发症护理研究提供参考。)

Zainab Ahmadi, Joar Björk, Hans Gilljam et al.

Background: Home oxygen therapy (HOT) improves survival in patients with hypoxaemic chronic respiratory disease. Most patients evaluated for HOT are former or active smokers. Oxygen accelerates combustion and smoking may increase the risk of burn injuries and fire hazards; therefore, it is considered a contraindication for HOT in many countries. However, there is variability in the practices and policies regarding this matter. This multidisciplinary Swedish taskforce aimed to review the potential benefits and risks of smoking in relation to HOT, including medical, practical, legal and ethical considerations. Methods: The taskforce of the Swedish Respiratory Society comprises 15 members across respiratory medicine, nursing, medical law and ethics. HOT effectiveness and adverse risks related to smoking, as well as practical, legal and ethical considerations, were reviewed, resulting in five general questions and four PICO (population–intervention–comparator–outcome) questions. The strength of each recommendation was rated according to the GRADE (grading of recommendation assessment, development and evaluation) methodology. Results: General questions about the practical, legal and ethical aspects of HOT were discussed and summarised in the document. The PICO questions resulted in recommendations about assessment, management and follow-up of smoking when considering HOT, if HOT should be offered to people that meet the eligibility criteria but who continue to smoke, if a specific length of time of smoking cessation should be considered before assessing eligibility for HOT, and identification of areas for further research. Conclusions: Multiple factors need to be considered in the benefit/risk evaluation of HOT in active smokers. A systematic approach is suggested to guide healthcare professionals in evaluating HOT in relation to smoking.