Lu Qian

Advance care planning (ACP), a cornerstone of ethical end-of-life care, upholds patient autonomy. However, its practice in Confucian-influenced societies, like China, is significantly shaped by cultural norms where family preferences often precede individual choice. This study explored cultural and ethical barriers to ACP implementation among oncology nursing professionals, focusing on tensions between patient-centered care and deeply rooted social norms. A qualitative thematic analysis was conducted on open-ended responses from oncology hospitals across 22 provinces, 4 municipal cities, and 5 autonomous regions in China. Data were collected via a cross-sectional online survey and analyzed using Braun and Clarke’s framework to identify patterns in cultural, ethical, and communicative challenges. A total of 838 oncology nursing professionals participated in the study. Three main interdependent barriers emerged: (1) Cultural norms, including filial piety (15.6% of codes) and death-related taboos (11.0%), often led to family-mediated decision-making (33.1%) over patient autonomy; (2) Ethical dilemmas involved neglecting patient preferences (24.3%) and conflicts between life-prolonging treatments and quality-of-life considerations (8.1%); (3) Communication challenges arose from information asymmetry (7.9%) and power imbalances, which often silenced patient voices. These factors collectively created systemic obstacles to ACP implementation. Context-specific ACP strategies in China should integrate Confucian ethics into nursing education, support ethics consultation, and develop culturally sensitive communication models. Future research must assess these interventions’ impact on balancing cultural values and patient autonomy, advancing equitable end-of-life care in culturally diverse healthcare systems. Not applicable.

This study explored how the nature of CEOs’ human capital affects the scope of their firms’ corporate social responsibility initiatives. By integrating upper echelons theory with the attention-based view of the firm, the analyses showed that generalist CEOs with a broader range of knowledge and skills tend to aim their firms’ corporate social responsibility efforts toward a broader range of responsibility domains than do specialist CEOs with a narrower range of knowledge and skills. The difference is weaker when a firm’s board has a sustainability committee or when industry peers are active in a broad range of corporate social responsibility domains. These findings are supported by data on S&P 1500 firms spanning 2000 to 2018.

ObjectiveDespite its prevalence, insomnia disorder remains poorly understood. In this study, we used machine learning to analyze the functional connectivity disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation and pharmacotherapy.Materials and methods51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging, resting-stage functional MRI, and diffusion weighted imaging. Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.ResultsSubjects were classified into ID and control with an area under the receiver operating characteristic curve of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.ConclusionMachine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.