Hasil untuk "Nursing"

J. Hayden, Richard A. Smiley, M. Alexander et al.

C. Tanner

P. Jeffries

E. Lake

Cynthia Delgado, D. Upton, K. Ranse et al.

Moslem Rashidi, Luke B. Connelly, Gianluca Fiorentini

We study how a first heart-failure hospitalization, an adverse health shock, changes patients' care, and whether a nurse-led chronic-care program sustains those post-shock investments. Using linked population-wide administrative records from Italy's Romagna Local Health Authority (2017-2023), we anchor event time at each patient's first CHF admission and exploit staggered timing to estimate dynamic effects. The shock triggers a sharp post-discharge surge: beta-blocker adherence, cardiology follow-up, and echocardiography rise immediately, while emergency-room use spikes just before admission and then stabilizes. We then estimate the incremental impact of enrollment in the Nurse-led Program for Chronic Patients (NPCP) using the interaction-weighted event-study estimator for staggered adoption. Under conventional difference-in-differences inference, NPCP strengthens long-run preventive engagement, with little detectable change in emergency-room use. HonestDiD sensitivity analysis indicates these gains are economically meaningful but not statistically definitive under modest departures from parallel trends.

Md Mhamud Hussen Sifat, Md Maruf, Md Rokunuzzaman

The utilization of robotic technology has gained traction in healthcare facilities due to progress in the field that enables time and cost savings, minimizes waste, and improves patient care. Digital healthcare technologies that leverage automation, such as robotics and artificial intelligence, have the potential to enhance the sustainability and profitability of healthcare systems in the long run. However, the recent COVID-19 pandemic has amplified the need for cyber-physical robots to automate check-ups and medication administration. A robot nurse is controlled by the Internet of Things (IoT) and can serve as an automated medical assistant while also allowing supervisory control based on custom commands. This system helps reduce infection risk and improves outcomes in pandemic settings. This research presents a test case with a nurse robot that can assess a patient's health status and take action accordingly. We also evaluate the system's performance in medication administration, health-status monitoring, and life-cycle considerations.

Li Sun, Hai-Yan Gu, Guan-Hua Xu et al.

ObjectiveThe aim of this study is to develop and validate a prediction model for fall risk factors in hospitalized older adults with osteoporosis.MethodsA total of 615 older adults with osteoporosis hospitalized at a tertiary (grade 3A) hospital in Nantong City, Jiangsu Province, China, between September 2022 and August 2023 were selected for the study using convenience sampling. Fall risk factors were identified using univariate and logistic regression analyses, and a predictive risk model was constructed and visualized through a nomogram. Model performance was evaluated using the area under the receiver operator characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and clinical decision curve analysis, assessing the discrimination ability, calibration, and clinical utility of the model.ResultsBased on logistic regression analysis, we identified several significant fall risk factors for older adults with osteoporosis: gender of the study participant, bone mineral density, serum calcium levels, history of falls, fear of falling, use of walking aids, and impaired balance. The AUC was 0.798 (95% CI: 0.763–0.830), with a sensitivity of 80.6%, a specificity of 67.9%, a maximum Youden index of 0.485, and a critical threshold of 121.97 points. The Hosmer-Lemeshow test yielded a χ2 value of 8.147 and p = 0.419, indicating good model calibration. Internal validation showed a C-index of 0.799 (95% CI: 0.768–0.801), indicating the model’s high discrimination ability. Calibration curves showed good agreement between predicted and observed values, confirming good calibration. The clinical decision curve analysis further supported the model’s clinical utility.ConclusionThe prediction model constructed and verified in this study was to predict fall risk for hospitalized older adults with osteoporosis, providing a valuable tool for clinicians to implement targeted interventions for patients with high fall risks.

Lin Hu, Haixia Feng, Jing Han et al.

Abstract Background Frailty is a syndrome as with aging in the population of type 2 diabetes mellitus (T2DM) and exercise has become an essential non-pharmacological tool especially in the pre-frail stage. Notably, the form of supervised home-based exercise program has been strongly recommended in recent years. This study aimed to verify the potential effects of the supervised home-based elastic band exercise in pre-frail older T2DM patients in China. Methods A total of 100 participants were included and randomly divided into intervention group (IG) (n = 50) and control group (CG) (n = 50). The CG received a routine care, while the IG received an extra home-based elastic band training under online and offline supervisions sustaining 12-weeks. The glycosylated hemoglobin (HbA1c), blood lipids, body composition, physical function, scales of Diabetes specificity quality of life scale (DSQL), Pittsburgh sleep quality index (PSQI) and short form geriatric depression scale (GDS-15) of the participants were evaluated before and after intervention. Results The average age of the participants were 66.01 ± 4.76 with 55% male and average BMI 24.75 ± 3.51 kg/m2. The clinical characteristics of the two groups were comparable. After 12 weeks’ training, muscle mass of the limbs (P < 0.05), physical function indicators including grip strength, chair stands (both P < 0.05), walking time (P < 0.01), HbA1c (P < 0.05), frailty score (P < 0.05), subjective sleep quality (P < 0.05), total DSQL scores (P < 0.01) and the depressive status (P < 0.01) improved significantly in IG when compared with CG. Conclusion Supervised home-based elastic band exercise could improve limb muscle mass, physical fitness, glucose and lipid control and quality of life in pre-frail older T2DM patients. Trial registration number ChiCTR2300070726; Registration date: 21/04/2023.

Elinor Randi Schoenfeld, Tracy Trimboli, Kaylyn Schwartz et al.

By 2050, most adults aged 65 and older in the United States will want to age independently at home, a goal that will strain healthcare resources. Adults aged 50 and older (N = 112) were recruited for study participation between 2018 and 2022. They completed surveys and participated in discussion sessions to explore their needs and opinions regarding smart home sensors. Survey results indicated that older adults’ comfort with smart home sensors increased with their perceived need for monitoring when home alone (OR = 1.46; <i>p</i> = 0.012) or sick/recovering from an illness (OR = 2.21; <i>p</i> < 0.001). When sick compared to when healthy, individuals were 2.65 times more likely to prefer installing multiple sensors in the living room, 1.75 times more likely in the kitchen, 3.66 times more likely in the bedroom, and 3.41 times more likely in the bathroom (<i>p</i> < 0.05). Regarding data sharing, participants were most willing to share information with healthcare providers and family members on a regular basis (80 and 81%, respectively) and 71% on a regular basis or when sick/recovering. Comfort with data sharing with professional caregivers (OR = 1.67; <i>p</i> = 0.0017) and monitoring companies (OR = 1.34; <i>p</i> = 0.030) significantly increased when sick/recovering. Discussion sessions highlighted overwhelming concerns about personal security/privacy, loss of independence, and ethical issues in data collection. Participants emphasized the need for new systems to be flexible, cost-effective, user-friendly, and respectful of user autonomy, accommodating diverse life stages, comfort levels, home environments, income levels, and support structures. Insights are now informing sensor data collection in our model home. Study findings underscore the importance of involving potential users in technology development to create effective and acceptable solutions for aging in place.

Ke-rui Zhang, Yi Yang, Ya-qin Li et al.

Abstract Background Anterior cervical corpectomy and fusion (ACCF) with Traditional Titanium Mesh Cages (TTMCs) can lead to complications such as cage subsidence, dysphagia, and implant-related issues. These complications suggest that the biomechanical stability of ACCF with TTMC may be insufficient. This study aims to evaluate whether a New Assembled Titanium Mesh Cage (NTMC) can improve the biomechanical performance after ACCF. Methods ACCF procedures using both TTMC and NTMC models were constructed and compared. The range of motion (ROM) of the surgical segments and stress peaks in various regions including the endplate, bone-screw interface, facet joints, and adjacent intervertebral discs were analyzed. Results The use of NTMC significantly reduced the postoperative ROM of the surgical segments by 80.7%-82.0% compared to ACCF with TTMC. Additionally, stress peaks at the endplate, bone-screw interface, and facet contact force (FCF) were higher in ACCF with TTMC compared to NTMC. TTMC also induced higher stress peaks in the C3/4 and C6/7 intervertebral discs (ranging from 0.2009–6.961 MPa and 0.2477–4.735 MPa, respectively), followed by the NTMC (ranging from 0.1322–3.820 MPa and 0.2227–4.104 MPa, respectively). Conclusions The utilization of NTMC, which includes enlarged spacers and emulates endplate geometries, effectively reduces the risks of cage subsidence and instrument-related complications in ACCF. Furthermore, ACCF with NTMC also decreases the risks of dysphagia, facet joint degeneration, and adjacent disc degeneration during the follow-up period by altering the fixing method while maintaining construct stability.

Ying Xie, Dongmei Yang, Ting Jiang et al.

BackgroundChronic subdural hematoma (CSDH) has high postoperative recurrence rates. This study investigates the effects of hyperbaric oxygen therapy (HBOT) combined with Medical-Psychosocial-Nursing Functional Support (MPNFS) on functional recovery and recurrence prevention in CSDH patients, and establishes a recurrence prediction model.MethodsA total of 184 CSDH patients undergoing burr hole drainage were randomized into a control group and an observation group (HBOT + MPNFS). Neurological (NIHSS), motor (Fugl-Meyer Assessment), and quality-of-life (SF-36) outcomes were assessed preoperatively and at 1-month postoperatively. Complications and 6-month recurrence rates were recorded. Univariate/multivariate logistic regression identified recurrence risk factors, with ROC analysis evaluating predictive accuracy.ResultsThe observation group showed superior 1-month outcomes: lower NIHSS scores (t = 4.94, p &lt; 0.001), higher FMA and SF-36 scores (p &lt; 0.01). Complication and recurrence rates were significantly reduced (p &lt; 0.05). Independent recurrence predictors included brain atrophy (OR = 2.877), poor brain reexpansion (OR = 3.165), preoperative hematoma width ≥ 20 mm (OR = 2.782), and absence of combined intervention (OR = 2.842). The multifactorial model achieved an AUC of 0.7862, indicating robust predictive efficacy.ConclusionHyperbaric oxygen therapy combined with MPNFS enhances neurological/motor recovery, improves quality of life, and reduces complications/recurrence in postoperative CSDH patients.

P. Davidson, Sarah L Szanton

The COVID-19 pandemic is providing us with many painful lessons particularly the vulnerability of individuals living with chronic conditions and the need for preparedness, coordination, and monitoring. Long-term care facilities, including nursing homes, skilled nursing facilities, and assisted living facilities, provide care for some of the most vulnerable populations in society, including older people and those with chronic medical conditions. In the United Kingdom, there are about 17,000 people living in nursing and residential care homes and 200,000 Australians live or stay in residential aged care on any given day.

L. Labrague, D. McEnroe-Petitte, Ann M. Bowling et al.

BACKGROUND As a complementary teaching pedagogy, high-fidelity simulation remains as an effective form of simulation modality. Empirical evidence has additionally shown high-fidelity simulation (HFS) to be an effective contributor to students' learning outcomes. PURPOSE This paper critically appraised existing scientific articles that covered the influence of utilizing HFS on the effects of nursing students' anxiety and self-confidence during undergraduate nursing education. METHODS This was a systematic review of scientific articles conducted from 2007 to 2017 on the topic of the influence of using HFS on students' self-confidence and anxiety. The literature of six electronic databases (Proquest, SCOPUS, MEDLINE, PubMed Central, CINAHL, and PsychINFO) was reviewed. RESULTS Following the literature search, 35 articles were selected. This review provides updated evidence on the efficacy of HFS in reducing anxiety and enhancing self-confidence among nursing students when performing nursing duties or managing patients. Moreover, this review highlights the need for more research that examines the impact of HFS on students' anxiety. CONCLUSION As this form of simulation is found to be effective in the enhancement of nursing student self-confidence and the reduction of their anxiety when caring for patients and/or employing nursing skills, the inclusion of simulation-based activities in all clinical nursing courses is vital.

Elizabeth M White, L. Aiken, D. Sloane et al.

The objective of this cross-sectional study was to examine the relationships between work environment, care quality, registered nurse (RN) burnout, and job dissatisfaction in nursing homes. We linked 2015 RN4CAST-US nurse survey data with LTCfocus and Nursing Home Compare. The sample included 245 Medicare and Medicaid-certified nursing homes in four states, and 674 of their RN employees. Nursing homes with good vs. poor work environments, had 1.8% fewer residents with pressure ulcers (p = .02) and 16 fewer hospitalizations per 100 residents per year (p = .05). They also had lower antipsychotic use, but the difference was not statistically significant. RNs were one-tenth as likely to report job dissatisfaction (p < .001) and one-eighth as likely to exhibit burnout (p < .001) when employed in good vs. poor work environments. These results suggest that the work environment is an important area to target for interventions to improve care quality and nurse retention in nursing homes.

Batool Poorchangizi, F. Borhani, A. Abbaszadeh et al.

BackgroundProfessional values of nursing students may be changed considerably by curricula. This highlights the importance of the integration of professional values into nursing students’ curricula. The present study aimed to investigate the importance of professional values from nursing students’ perspective.MethodsThis cross-sectional study was conducted at the Kerman University of Medical Sciences, Iran. Data were gathered by using a two-section questionnaire consisting of demographic data and Nursing Professional Values Scale-Revised (NPVS-R). By using the stratified random sampling method, 100 nursing students were included in the study.ResultsResults showed that the mean score of the students’ professional values was at high level of importance (101.79 ± 12.42). The most important values identified by the students were “maintaining confidentiality of patients” and “safeguarding patients’ right to privacy”. The values with less importance to the students were “participating in public policy decisions affecting distribution of resources” and “participating in peer review”. The professional value score had a statistically significant relationship with the students’ grade point average (P < 0.05).ConclusionsIn light of the low importance of some values for nursing students, additional strategies may be necessary to comprehensively institutionalize professional values in nursing students.

Xu Liu, Jing Zheng, Ke Liu et al.

BACKGROUND Promotion of patient safety is among the most important goals and challenges of healthcare systems worldwide in countries including China. Donabedian's Structure-Process-Outcome model implies that patient safety is affected by hospital nursing organizational factors and nursing care process. However, studies are imperative for a clear understanding about the mechanisms by which patient safety is affected to guide practice. OBJECTIVE The objective of this study was to explore the impact of hospital nursing work environment, workload, nursing care left undone, and nurse burnout on patient safety. DESIGN This was a cross-sectional study conducted in 23 hospitals in Guangdong province, China in 2014. Data from nurses (n = 1542) responsible for direct care on 111 randomly sampled medical and surgical units were analyzed. METHODS Work environment was measured by the Practice Environment Scale of Nursing Work Index. Workload was measured by day shift unit patient-nurse ratio and non-professional tasks conducted by nurses. Nursing care left undone was measured by 12 items addressing necessary nursing activities. Nurse burnout was measured by the emotional exhaustion subscale of the Maslach Burnout Inventory-Human Services Survey. Patient safety was measured by three items indicating nurses' perception of overall patient safety and nine items addressing patient adverse events. Structural equation modeling was used to examine a hypothesized model that supposed work environment and workload have both direct and indirect effects on patient safety through nursing care left undone and nurse burnout. RESULTS The findings generally supported the hypothesized model. Better work environment was associated with better patient safety both directly and indirectly. Lower workload primarily indirectly related to better patient safety. Nursing care left undone and nurse burnout were mediators negatively associated with patient safety. CONCLUSIONS Improving work environment, increasing nurse staffing levels, and providing sufficient support for nurses to spend more time on direct patient care would be beneficial to patient safety improvement.

Yumeng Li, William Beluch, Margret Keuper et al.

Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content. They tend to synthesize quasi-static videos, ignoring the necessary visual change-over-time implied in the text prompt. At the same time, scaling these models to enable longer, more dynamic video synthesis often remains computationally intractable. To address this challenge, we introduce the concept of Generative Temporal Nursing (GTN), where we aim to alter the generative process on the fly during inference to improve control over the temporal dynamics and enable generation of longer videos. We propose a method for GTN, dubbed VSTAR, which consists of two key ingredients: 1) Video Synopsis Prompting (VSP) - automatic generation of a video synopsis based on the original single prompt leveraging LLMs, which gives accurate textual guidance to different visual states of longer videos, and 2) Temporal Attention Regularization (TAR) - a regularization technique to refine the temporal attention units of the pre-trained T2V diffusion models, which enables control over the video dynamics. We experimentally showcase the superiority of the proposed approach in generating longer, visually appealing videos over existing open-sourced T2V models. We additionally analyze the temporal attention maps realized with and without VSTAR, demonstrating the importance of applying our method to mitigate neglect of the desired visual change over time.

Isaac YL Lee, Thanh Nguyen-Duc, Ryo Ueno et al.

Excessive caregiver workload in hospital nurses has been implicated in poorer patient care and increased worker burnout. Measurement of this workload in the Intensive Care Unit (ICU) is often done using the Nursing Activities Score (NAS), but this is usually recorded manually and sporadically. Previous work has made use of Ambient Intelligence (AmI) by using computer vision to passively derive caregiver-patient interaction times to monitor staff workload. In this letter, we propose using a Multiscale Vision Transformer (MViT) to passively predict the NAS from low-resolution thermal videos recorded in an ICU. 458 videos were obtained from an ICU in Melbourne, Australia and used to train a MViTv2 model using an indirect prediction and a direct prediction method. The indirect method predicted 1 of 8 potentially identifiable NAS activities from the video before inferring the NAS. The direct method predicted the NAS score immediately from the video. The indirect method yielded an average 5-fold accuracy of 57.21%, an area under the receiver operating characteristic curve (ROC AUC) of 0.865, a F1 score of 0.570 and a mean squared error (MSE) of 28.16. The direct method yielded a MSE of 18.16. We also showed that the MViTv2 outperforms similar models such as R(2+1)D and ResNet50-LSTM under identical settings. This study shows the feasibility of using a MViTv2 to passively predict the NAS in an ICU and monitor staff workload automatically. Our results above also show an increased accuracy in predicting NAS directly versus predicting NAS indirectly. We hope that our study can provide a direction for future work and further improve the accuracy of passive NAS monitoring.

Jubin Thomas

When managing an organization, planners often encounter numerous challenging scenarios. In such instances, relying solely on intuition or managerial experience may not suffice, necessitating a quantitative approach. This demand is further accentuated in the era of big data, where the sheer scale and complexity of constraints pose significant challenges. Therefore, the aim of this study is to provide a foundational framework for addressing personnel scheduling, a critical issue in organizational management. Specifically, we focus on optimizing shift assignments for staff, a task fraught with complexities due to factors such as contractual obligations and mandated rest periods. Moreover, the current landscape is characterized by frequent employee shortages across various industries, with many organizations lacking efficient and dependable management tools to address them. Therefore, our attention is particularly drawn to the nurse rostering problem, a personnel scheduling challenge prevalent in healthcare settings. These issues are characterized by a multitude of variables, given that a single healthcare facility may employ hundreds of nurses, alongside stringent constraints such as the need for adequate staffing levels and rest periods postnight shifts. Furthermore, the ongoing COVID19 pandemic has exacerbated staffing challenges in healthcare institutions, underlining the importance of accurately assessing staffing needs and optimizing shift allocations for effective operation amidst crisis situations.