Objective: The present study investigates the association between sleep in late adolescence and completion of upper secondary school.
Methods: The data are drawn from the youth@hordaland study, a large population-based study conducted in 2012, linked with official educational data in Norway (N = 8838).
Results: High school dropout was more prevalent among adolescents who had insomnia (20.6%) compared to those without insomnia (14.3%; adjusted risk ratios = 1.50; 95% confidence intervals: [2.19-2.92]). There was also a higher rate of school dropout among those who had symptoms of delayed sleep-wake phase (21%) compared to those without delayed sleep-wake phase (14.3%); adjusted risk ratios = 1.43, 95% confidence intervals: (1.28-1.59). School noncompleters were also characterized by reporting 44 minutes shorter sleep duration, longer sleep onset latency, and wake after sleep onset (both approx. 15 minutes) compared to school completers.
Conclusion: The importance of sleep for high school dropout rates highlights the importance of including sleep as a risk indicator and a possible target for preventive interventions in late adolescence.
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http://dx.doi.org/10.1016/j.sleh.2023.05.004 | DOI Listing |
Health Technol Assess
January 2025
School of Medicine, Keele University, Keele, Staffordshire, UK.
Background: For people receiving haemodialysis, a balance has to be struck between removing sufficient but not too much fluid during a treatment session and maintaining any remaining kidney function they might have. In the BISTRO trial, this study sought to establish if getting the balance right might be improved by the additional use of bioimpedance, a device that measures body fluid composition to help decide how much fluid to remove during dialysis. Designing and executing this trial, which incorporated complex and repeated trial procedures that would be dependent on participant engagement, presented challenges that demanded effective public and patient involvement.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
View Article and Find Full Text PDFJ Fluency Disord
January 2025
School of Speech-Language Pathology and Audiology, Université de Montréal, Montreal, Quebec, Canada; Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montreal, Quebec, Canada.
Purpose: Children who stutter (CWS) in clinical settings may present with concomitant disorders (CDs), which can complexify the delivery of the Lidcombe Program (LP). However, there is limited evidence on how CDs influence treatment outcomes in CWS, leaving clinicians with little guidance regarding best practices with these children. This exploratory study, conducted in partnership with a rehabilitation center's clinical team, aims to understand which CDs and suspected CDs speech-language pathologists document when treating CWS with the LP and their relationship to treatment characteristics and outcomes.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
National School of Public Health, Rabat, Morocco.
Objective: This study aimed to investigate loss to follow-up (LFU) rates within breast and cervical cancer screening programs in Kenitra-Morocco, identifying contributing factors from both patient and healthcare worker perspectives to enhance care continuity.
Methods: The study was a non-experimental, mixed-methods design conducted in three-phases. We started by identifying LFU women and their characteristics from medical records, interviewing LFU women to ascertain reasons for discontinuation, and surveying healthcare workers for perceived determinants of LFU through semi-structured questionnaires.
J Dent Sci
January 2025
School of Dentistry, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.
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