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An Explorative Study on Knowledge, Attitudes, and Coronavirus Disease 2019 Vaccine Intention among Parents in India.
Am J Trop Med Hyg
January 2025
Department of Pediatrics and Office of Global Health, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
India's National COVID Vaccination Program recommended vaccination of children ages 6-12 years in April 2022. This study assessed vaccine acceptance among mothers to better understand potential barriers and facilitators of national acceptance of pediatric coronavirus disease 2019 (COVID-19) vaccination. Qualitative data were collected through three focus group discussions (FGDs) with mothers who had children younger than 12 years of age; FGD-1 was composed of mothers who worked at a tertiary medical center in India, whereas FGD-2 and FGD-3 were composed of mothers who sought care at urban and rural community health centers.
View Article and Find Full Text PDFAccelerating the Future of Oncology Drug Development: The Role of Consortia in the Delivery of Precision Oncology Early Phase Clinical Trials.
J Clin Oncol
January 2025
The Kinghorn Cancer Centre, St Vincent's Hospital, Sydney, NSW, Australia.
Purpose: Over the past 15 years, the landscape of early phase clinical trials (EPCTs) has undergone a remarkable expansion in both quantity and intricacy. The proliferation of sites, trials, sponsors, and contract research organizations has surged exponentially, marking a significant shift in research conduct. However, EPCT operations suffer from numerous inefficiencies, such as cumbersome start-up processes, which are particularly critical when drug safety and the recommended phase II dose need to be established in a timely manner.
View Article and Find Full Text PDFEnhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.
PLoS One
January 2025
School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance.
View Article and Find Full Text PDFImplementation of a data-driven quality improvement program in primary care for patients with coronary heart disease: a mixed methods evaluation of acceptability, satisfaction, barriers and enablers.
Aust J Prim Health
January 2025
School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia; and The George Institute for Global Health, University of New South Wales, Barangaroo, NSW, Australia.
Background The study aimed to understand the acceptability, satisfaction, uptake, utility and feasibility of a quality improvement (QI) intervention to improve care for coronary heart disease (CHD) patients in Australian primary care practices and identify barriers and enablers, including the impact of COVID-19. Methods Within the QUality improvement for Effectiveness of care for people Living with heart disease (QUEL) study, 26 Australian primary care practices, supported by five Primary Health Networks (PHN) participated in a 1-year QI intervention (November 2019 - November 2020). Data were collected from practices and PHNs staff via surveys and semi-structured interviews.
View Article and Find Full Text PDFPredictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
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