How comprehensive is our healthcare system, and who is being left behind? In this study, The University of Tokyo researchers synthesized the patients’ real-world experiences with complex genetic disorders into a single case. The study reveals how compartmentalized care leads to treatment refusal and patient harm, while coordinated interdisciplinary teams can restore well-being. It highlights the urgent need for reforms in medical education, care continuity, and health policy to create more inclusive, patient-centered healthcare systems.

Abstract

Purpose – We investigate latent higher-order dependencies in Chinese sectoral risk connectedness networks, characterize their topology and quantify resilience at both the system and sector levels, thereby offering new insights for mitigating systemic risk and preserving financial stability.

Design/methodology/approach – Employing the RHOSTS approach, we construct higher-order risk connectedness networks for Chinese stock sectors and analyze their structure with network-topology metrics. These metrics are then embedded in a coupled-map-lattice model to track the time-varying resilience of the overall network and its constituent sectors.

Findings – The sectoral network exhibits pronounced higher-order interactions, with four-sector synchronous resonance as the prevailing motif. Shock-specific core resonance clusters emerge and although system-wide resilience increases over time, marked heterogeneity across sectors persists.

Originality/value – By moving beyond traditional pairwise spillover models, our higher-order financial network reveals collective risk resonance spanning multiple sectors. The topology-based metrics we propose enable simultaneous assessment of system-level and sector-specific resilience and its evolution.

Abstract

Purpose – This paper aims to enhance the predictability of stock returns. Existing studies have used investor sentiment to forecast stock returns. However, it is unclear whether high-frequency intraday investor sentiment can enhance the forecasting performance of low-frequency stock returns.

Design/methodology/approach – Thus, we employ the MIDAS model and the high-frequency intraday sentiment extracted from the Internet stock forum to forecast Chinese A-shares returns at daily frequency.

Findings – The results illustrate that high-frequency sentiment data are better than daily sentiment data in predicting daily stock returns, and the sentiment in non-trading hours has been proved superior to those in trading hours.

Originality/value – First, our study adds to the growing literature on investor sentiment. We are the first to construct a proxy for high-frequency investor sentiment using intraday postings collected from Chinese Internet stock forum. Second, we confirm that sentiment in non-trading hours has a stronger predictive ability than those in trading hours. Third, we also contribute to the performance comparison of MIDAS-class models. The good performance of U-MIDAS is confirmed in our empirical applications.

More than 50 million U.S. workers quit during the “Great Resignation,” due to burnout and weak benefits. Now, a first-of-its-kind study shows paid time off is a powerful driver of retention. Drawing on 18 years of data and 32,000 early-career workers, the analysis finds that one to five PTO days barely reduce resignations. Meaningful declines begin at six to 10 days, with the strongest effects at 11 or more days, significantly lowering quits for both men and women.