Objective: To identify the technology and connectivity issues in rural and remote general practices, and the factors independently associated with these issues that negatively impact staff's capability to perform their job.
Methods: An annual cross-sectional survey of rural and remote general practice managers. Dependent variables included demographic data, practice size, geographic location, connection type and frequency of connectivity issues. Descriptive statistics are presented, and bivariate logistic regression was undertaken to determine factors independently associated with connectivity issues that negatively impact staff's capability to perform their job.
Participants: One hundred sixty-eight general practice managers from rural and remote New South Wales.
Results: The majority of respondents (87%, n = 146) indicated that technology and connectivity issues had impacted staff's capability to perform their job. Internet problems were the most frequently reported issue (36%, n = 61). In bivariate analysis, practices that had a total clinical staff headcount between 5 and 7 (OR 0.27; 95% CI 0.10-0.67; p = 0.005) or between 8 and 11 (OR 0.39; 95% CI 0.16-0.95; p = 0.038) were significantly less likely to report technology and connectivity issues that negatively impact staff's capability to perform their job, compared with practices with a total clinical headcount of less than five.
Conclusions: Technology and connectivity issues persist in rural and remote general practices. This is the first study to demonstrate that technology and connectivity issues impact on rural staff's capability to perform their job. Furthermore, smaller practices face more technology and connectivity issues that negatively impact staff's capability to do their job than larger practices. Further research is required to find solutions to address these challenges.
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http://dx.doi.org/10.1111/ajr.13129 | DOI Listing |
Front Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Nursing, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Objectives: Fear of falling is a psychological issue that adversely impacts the health of elderly individuals. The purpose of this study was to investigate the correlation among positive coping styles, psychological resilience, and fear of falling in older adults. The mediating role of psychological resilience was also investigated.
View Article and Find Full Text PDFCommun Psychol
January 2025
Cancer Control Center, Osaka International Cancer Institute, Osaka, Japan.
Postpartum depression and mother-to-infant bonding difficulties (MIBD), two issues crucial to maternal and infant mental health, often coexist and affect each other. Our study aims to dissect their complex relationship through a graphical LASSO network analysis of individual symptoms in 5594 Japanese postpartum women, whose geographical distribution was nationally representative. We identified 'fear', 'enjoyment', 'overwhelm', and 'insomnia' as common bridge symptoms linking postpartum depression and MIBD across three distinct postpartum periods.
View Article and Find Full Text PDFNeural Netw
January 2025
Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China. Electronic address:
Anomaly detection on graph data has garnered significant interest from both the academia and industry. In recent years, fueled by the rapid development of Graph Neural Networks (GNNs), various GNNs-based anomaly detection methods have been proposed and achieved good results. However, GNNs-based methods assume that connected nodes have similar classes and features, leading to issues of class inconsistency and semantic inconsistency in graph anomaly detection.
View Article and Find Full Text PDFJMIR Mhealth Uhealth
January 2025
Calydial, Vienne, France.
Background: The use of telemonitoring to manage renal function in patients with chronic kidney disease (CKD) is recommended by health authorities. However, despite these recommendations, the adoption of telemonitoring by both health care professionals and patients faces numerous challenges.
Objective: This study aims to identify barriers and facilitators in the implementation of a telemonitoring program for patients with CKD, as perceived by health care professionals and patients, and to explore factors associated with the adoption of the program.
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