Background: Many maternity services in Australia offer women a variety of models of care including midwife led models. Childbearing women, however, need to understand the differences between these models if they are to make an informed decision about their choice of care. Decision Aids (DA) help people decide when there is not a single best option and the best decision will be based upon the values of the decision maker. There is no current tool that focuses on the choice of midwife led vs other models of maternity care.
Aim: This research aimed to develop, and pilot test a Decision Aid focusing on the choice between midwife led and standard models of maternity care.
Methods: The DA was developed using the International Patient Decision Aid Standards and pilot tested for acceptability with a group of clinicians who provide antenatal care in one jurisdiction in Australia. A posttest only study was conducted assessing knowledge, acceptability and decisional conflict, with a group of women of childbearing age living in the jurisdiction.
Findings: A DA was developed and pilot acceptability testing with 14 women and 13 clinicians of Australian Capital Territory (ACT) health demonstrated its acceptability and highlighting areas for further development.
Discussion: Some revisions may be needed to address issues of balance and bias toward midwife-led care identified by some recipients.
Conclusion: Pilot acceptability testing with women and staff of ACT health provides a steppingstone to further research, development and evaluation of this DA.
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http://dx.doi.org/10.1016/j.wombi.2020.12.007 | 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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