We tested a novel hospice-specific patient decision aid to determine whether the decision aid could improve hospice knowledge, opinions of hospice, and decision self-efficacy in making decisions about hospice. Two patient-level randomized studies were conducted using two different cohorts. Recruitment was completed from March 2019 through May 2020. Cohort #1 was recruited from an academic hospital and a safety-net hospital and Cohort #2 was recruited from community members. Participants were randomized to review a hospice-specific patient decision aid. The primary outcomes were change in hospice knowledge, hospice beliefs and attitudes, and decision self-efficacy Wilcoxon signed rank tests were used to evaluate differences on the primary outcomes between baseline and 1-month. Participants were at least 65 years of age. A total of 266 participants enrolled (131 in Cohort #1 and 135 in Cohort #2). Participants were randomized to the intervention group (n = 156) or control group (n = 109). The sample was 74% (n = 197) female, 58% (n = 156) African American and mean age was 74.9. Improvements in hospice knowledge between baseline and 1-month were observed in both the intervention and the control groups with no differences between groups (.43 vs .275 points, = .823). There were no observed differences between groups on Hospice Beliefs and Attitudes scale (3.29 vs 3.08, = .076). In contrast, Decision Self-Efficacy improved in both groups and the effect of the intervention was significant (8.04 vs 2.90, = -.027). The intervention demonstrated significant improvements in decision self-efficacy but not in hospice knowledge or hospice beliefs and attitudes.
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http://dx.doi.org/10.1177/10499091231190776 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFPLoS One
January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
View Article and Find Full Text PDFBr J Nurs
January 2025
Associate Professor, Nursing and Midwifery, University of Limerick, Limerick, Ireland.
Critical thinking is required for successful nursing outcomes. For evidence-based practice, there is a need to understand and apply quantitative methods of research and statistical analysis in order to obtain evidence. However, the literature shows that the use of quantitative methods among nurse researchers can be problematic.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
School of Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia.
: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. : Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability.
View Article and Find Full Text PDFJ Am Heart Assoc
January 2025
Department of Cardiology Beijing Anzhen Hospital, Capital Medical University Beijing China.
Background: Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited.
Methods And Results: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated.
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