Nursing Administration Quarterly最新文献

The Nursing Value Model (NVM) is a data framework model developed to measure the value of nursing care at the patient level. The NVM was constructed by multiple datasets extracted and assembled from various sources, such as the hospital electronic health records (EHR) and administrative data. Yet, very few studies have examined this model. As such, this study aimed to introduce how to construct NVM using available health care data, and discuss the feasibility of doing so by describing the insights and pitfalls during the development of the dataset. Data from 5 sources were used to build the dataset used to explore the NVM to estimate patient-level nursing cost estimation. Five aspects of data acquisition and synthesis are described: (a) each dataset acquisition, (b) the data wrangling process, (c) dataset construction, (d) data integrability, and (e) the strengths and weaknesses of each dataset. Six datasets from four different data sources were collected and merged, constructing the final dataset used for the NVM. Unique codes for nurses and patients were not always uniform, making the data complex and difficult to merge. To compute nursing value for the future, data systems need to be designed to collect, organize, and synthesize data easily.

In the evolving landscape of health care, digital health literacy has emerged as a core competency for nurses. This study explored the impact of a case-based learning intervention on digital health literacy, confidence, and satisfaction among undergraduate nursing students. A quasi-experimental pretest-posttest design was implemented with 54 students enrolled in a Bachelor of Science in Nursing program. Participants were randomly assigned to a control group (without digital resource access) or an intervention group (with access to Lippincott Advisor during a clinical case study). Data were collected using preintervention and postintervention surveys assessing confidence, familiarity, satisfaction, and perceived challenges. Quantitative data were analyzed using descriptive statistics and Mann-Whitney U tests, while qualitative data underwent thematic analysis. Findings revealed that students in the intervention group reported significantly higher levels of confidence ( P < .001) and familiarity ( P = .0043) in using digital health tools compared to controls. However, no significant differences were observed in ease of use, satisfaction, or information-seeking ability. Qualitative responses highlighted persistent barriers, including navigation difficulties, limited content relevance, and technical issues such as access restrictions. Despite these challenges, many students expressed a desire for continued exposure to digital resources. The results support the integration of digital health tools into experiential learning as a strategy to enhance informatics competency and clinical reasoning. However, findings also underscore the need for repeated, structured, and faculty-supported engagement to build deeper digital fluency. Aligning with national nursing education standards, this intervention offers a promising approach to prepare students for the digital demands of modern clinical practice.

The objective of this study was to examine how mobile learning affected individuals with hypertension's adherence to the DASH diet, healthy lifestyle behaviors, body mass index, quality of life, mindfulness, and stress levels. A total of 134 individuals with hypertension participated in this randomized controlled study (68 in the experimental group and 66 in the control group). "Health-Promoting Lifestyle Profile (HPLP) II," "EQ-5D Questionnaire," "Mindful Attention Awareness Scale (MAAS)," and "Perceived Stress Scale (PSS)" were employed to gather information. Participants in the experimental group received comprehensive education through a mobile application developed for hypertension management. This education, which included videos, audio recordings, and meditation content, was provided over a 3-month period. It was found that after mobile learning, the healthy lifestyle behaviors of individuals with hypertension improved, their quality of life and mindfulness levels increased, and their stress levels and body mass index decreased ( P < .05). This study found that the use of mobile application-based education to improve the lifestyle changes of patients is an effective tool in hypertension management. We recommended that mobile health applications used in the management of hypertension should be widely used by nurses in training and counselling processes, and future research should be conducted to observe the long-term effects of this technology.

This study employed text network analysis to examine nurse managers' feedback journals for new nurses, highlighting the critical need for structured support during their transition period. A total of 429 feedback journals were documented by 32 nurse managers for 239 new nurses between September 2019 and March 2021 in South Korea, aiming to identify the challenges faced by new nurses and explore keyword relationships and thematic patterns. The analysis process included 4 stages: data preprocessing, keyword extraction, network and centrality analysis, and subtheme analysis. Using NetMiner 4.5.0, the analysis identified several central keywords in the feedback journals, with "performance" showing the highest centrality values (degree centrality = 0.448, closeness centrality = 0.580, betweenness centrality = 0.117), followed by "experience" and "explanation." Through community detection analysis using the eigenvector method, 3 distinct subthemes emerged from the network structure: "clinical practice fundamentals," "learning and development process," and "support and educational system." The findings of this study provide empirical evidence for developing structured feedback programs and enhancing nurse managers' interviewing competencies. The findings provide empirical evidence regarding linguistic patterns and thematic structures in nurse managers' feedback, suggesting systematic documentation protocols and utilization of these insights to inform structured feedback initiatives, thereby supporting new nurses' development.

Heart failure affects about 65 million people globally, with wearable devices being used more frequently as one of the treatment modalities. The purpose of this scoping review was to evaluate the psychometric properties of quality of life assessment instruments used with heart failure patients, to include patients who may be treated with a wearable left ventricular assist device. Eligible articles were retrieved from medical and allied health databases on studies evaluating the properties of instruments used to measure quality of life in heart failure patients. The COnsensus-based Standards for the selection of health Measurement INstruments taxonomy was used to assess measurement properties of instruments used in 18 studies selected for review. The Patient-Reported Outcomes Measurement Information System-Plus-Heart Failure, received the highest score. Fourteen of the 18 reviewed studies scored above the acceptable score of 72, while 4 scored below 72. The risk of bias was minimized with 2 reviewers providing feedback on the study protocol and literature review. However, using more than 2 reviewers may have further reduced this risk. Findings inform nurse clinicians, researchers, educators, and policymakers about selecting instruments for assessing heart failure patients' quality of life, to include patients using a wearable device.

Reducing health care-acquired urinary tract infections is a common goal among health care providers and organizations. This study took an initial step toward this goal by exploring context-based variables contributing to HAUTI. We included a comprehensive list of nursing assessments and applied multiple machine learning methods to process the datasets and manage missing data. Among the models tested, eXtreme Gradient Boosting (XGBoost) emerged as the most effective in predicting HAUTI, identifying associations between improved skin integrity, mobility, and neurological status monitoring, which may be linked to lower HAUTI rates. However, our results should be carefully interpreted, given the study's significant missing data. The findings of this study reinforce the necessity of high-quality data to support the interpretation of machine learning (ML) models in clinical settings.