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by Lei Xiong, Ke Li, Wendy Siuyi Wong
BackgroundDigital media usage has become an integral part of daily life, but prolonged or emotionally driven engagement—especially during late-night hours—may lead to concerns about behavioral and mental health. Existing predictive systems fail to account for the nuanced interplay between users’ internal psychological states and their surrounding ecological contexts.
ObjectiveThis study aims to develop a psychologically and ecologically informed behavior prediction model to identify high-risk patterns of digital media usage and support early-stage intervention strategies.
MethodsWe propose a Dual-Channel Cross-Attention Network (DCCAN) architecture composed of three layers: signal identification (for psychological and ecological encoding), interaction modeling (via cross-modal attention), and behavior prediction. The model was trained and tested on a dataset of 9,782 users and 51,264 behavior sequences, annotated with labels for immersive usage, late-night activity, and susceptibility to health misinformation.
ResultsThe DCCAN model achieved superior performance across all three tasks, especially in immersive usage prediction (F1-score: 0.891, AUC: 0.913), outperforming LSTM, GRU, and XGBoost baselines. Ablation studies confirmed the critical role of both psychological and ecological signals, as well as the effectiveness of the cross-attention mechanism.
ConclusionsIncorporating psychological and ecological modalities through attention-based fusion yields interpretable and accurate predictions for digital risk behaviors. This framework shows promise for scalable, real-time behavioral health monitoring and adaptive content moderation on media platforms.
To investigate the factors that facilitate or hinder nurses in providing patient education.
A mixed-method systematic review.
Six databases (Cochrane Library, PubMed, EMBASE, Web of Science, MEDLINE and ERIC) were systematically searched for relevant publications.
The study was conducted following the JBI for mixed-method systematic reviews, and the reporting followed the PRISMA guideline. Two researchers independently performed literature screening, literature evaluation, data extraction and synthesis. PROSPERO registration number: CRD42023427451.
Twenty-six eligible articles were included, including 15 quantitative articles, 10 qualitative articles and 2 mixed-methods articles. The resultant synthesis of key findings led to the identification of these barriers and facilitators, categorised into five distinct levels: nurse-related factors, organisational factors, patient-related factors, the nurse–patient relationship and interdisciplinary collaboration.
The findings highlight the factors that facilitate or hinder nurses in providing patient education, suggesting that multifaceted interventions can enhance the practice of patient education in nursing and support the development of appropriate patient education guidelines or public policies.
This review delineates the facilitators and barriers influencing nurses' provision of patient education, offering an initial framework for nursing managers to craft interventions aimed at enhancing the quality of patient education provided by nurses, consequently elevating the overall quality of nursing.
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