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Academic healthcare systems face great challenges in coordinating services across a continuum of care that spans hospital, community providers, home and chronic care facilities. The Johns Hopkins Community Health Partnership (J-CHiP) was created to improve coordination of acute, sub-acute and ambulatory care for patients, and improve the health of high-risk patients in surrounding neighborhoods. The paper aims to discuss this issue.

J-CHiP targeted adults admitted to the Johns Hopkins Hospital and Johns Hopkins Bayview Medical Center, patients discharged to participating skilled nursing facilities (SNFs), and high-risk Medicare and Medicaid patients receiving primary care in eight nearby outpatient sites. The primary drivers of the program were redesigned acute care delivery, seamless transitions of care and deployment of community care teams.

Acute care interventions included risk screening, multidisciplinary care planning, pharmacist-driven medication management, patient/family education, communication with next provider and care coordination protocols for common conditions. Transition interventions included post-discharge health plans, hand-offs and follow-up with primary care providers, Transition Guides, a patient access line and collaboration with SNFs. Community interventions involved forming multidisciplinary care coordination teams, integrated behavioral care and new partnerships with community-based organizations.

This paper offers a detailed description of the design and implementation of a complex program to improve care coordination for high-risk patients in an urban setting. The case studies feature findings from each intervention that promoted patient engagement, strengthened collaboration with community-based organizations and improved coordination of care.

The Communist revolution in China has led to the appearance in this country of increasing numbers of Chinese books in Russian translation. The Chinese names in Cyrillic transcription have presented many librarians and students with a new problem, that of identifying the Cyrillic form of a name with the customary Wade‐Giles transcription. The average cataloguer, the first to meet the problem, has two obvious lines of action, and neither is satisfactory. He can save up the names until he has a chance to consult an expert in Chinese. Apart altogether from the delay, the expert, confronted with a few isolated names, might simply reply that he could do nothing without the Chinese characters, and it is only rarely that Soviet books supply them. Alternatively, he can transliterate the Cyrillic letters according to the system in use in his library and leave the matter there for fear of making bad worse. As long as the writers are not well known, he may feel only faintly uneasy; but the appearance of Chzhou Ėn‐lai (or Čžou En‐laj) upsets his equanimity. Obviously this must be entered under Chou; and we must have Mao Tse‐tung and not Mao Tsze‐dun, Ch'en Po‐ta and not Chėn' Bo‐da. But what happens when we have another . . . We can hardly write Ch'en unless we know how to represent the remaining elements in the name; yet we are loth to write Ch'en in one name and Chėn' in another.

The Metaverse is rapidly reshaping the understanding of tourism, yet the public perception of this new domain remains largely uncharted empirically. This paper aims to build on the technology acceptance model (TAM) and diffusion of innovations theory (DIT) to fill this gap, offering crucial insights that could inform scholars and practitioners in both the tourism and technology sectors.

Using a big-data approach, the authors applied machine learning to scrape comments made by social media users on recent popular posts or videos related to tourism in the Metaverse from three prominent social media platforms. The cleaning process narrowed down 15,461 comments to 2,650, which were then analysed using thematic, emotion and sentiment analysis techniques.

The thematic analysis revealed that virtual tourism evokes a complex range of public beliefs. While many express awe and excitement toward its immersive capabilities, others remain sceptical about authenticity compared to physical travel. Additional themes show people draw comparisons to real-world tourism, discuss technology’s role and note educational value and novelty. However, some comments raise concerns about potential societal harms, exploitation and mental health impacts. Sentiment analysis found over half of the comments positive, though some were negative. Emotion analysis showed contentment, happiness and excitement as most frequent, though sadness, worry and loneliness also featured. Overall, perceptions of Metaverse tourism encompass enthusiasm yet substantial ambivalence.

To the best of the authors’ knowledge, this study is one of the first to comprehensively analyse public discussions on Metaverse tourism. It takes TAM and Rogers’s DIT a step further and provides fresh insights into how these theories can be employed in the emerging field of Metaverse tourism. The themes revealed new conceptual insights into multidimensional factors shaping public beliefs about Metaverse tourism and thus informing scholarly research on virtual interaction and technology acceptance regarding Metaverse tourism. In addition, the results can help tourism providers, platforms and marketers address salient public beliefs and sentiments/attitudes in developing marketing offerings, experiences and communications. Over time, this analysis methodology can be used to track the evolving public perceptions of Metaverse tourism.