Background: As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective: The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods: The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results: The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions: This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Background: Alzheimer disease and related dementias (AD/RD) are progressive neurocognitive disorders that currently affect approximately 50 million people worldwide. Mobile phone apps have been well-integrated into daily lives and can be used to deliver and promote health care. There is an increase in the use of technology to provide care and support to AD/RD patients and their families. Objective: This study aimed to review apps designed for AD/RD patients and analyze the benefits of, and challenges to, such technological solutions. Methods: A systematic approach was applied to review the availability, content, features, and quality of mobile phone apps to support self-care among AD/RD patients. Results: The initial search for this review was conducted in January 2019, and the screening and analysis of the included apps were completed in May 2019. A total of 14 apps were included from an initial search of 245 apps. The top 3 features were alert (9/14, 64%), self-care tips (6/14, 42%), and social networking capacity (5/14, 35%). On average, the readability of the apps was a tenth-grade reading level (SD 3.06). The overall quality was 3.71 out of 5 (SD 1.37). Conclusions: Our findings suggest that currently available apps for AD/RD patients may not meet complex needs and may be challenging to use, given the possible impaired communication ability associated with AD/RD. Therefore, high-quality apps need to be developed and rigorously evaluated for feasibility and efficacy.

In rural China, the aging population faces unique challenges that affect their life satisfaction. These challenges are compounded by disparities in access to resources compared to urban areas, making it crucial to address these issues to ensure a dignified and fulfilling later life. Understanding and improving life satisfaction for this demographic is essential, not only for the well-being of the elderly but also for the overall social stability and economic sustainability of these communities. Using a rural sample from the Chinese Social Survey (CSS), this study investigates the interplay between social quality factors and life satisfaction among older adults in rural China. It focuses on the roles of socioeconomic security, social cohesion, social inclusion, and social empowerment in shaping the well-being of this demographic. It was found that the four conditional factors of social quality—socioeconomic security, social cohesion, social inclusion, and social empowerment—have different influences on life satisfaction among older adults in rural areas. The statistical model shows that the influence of absolute family income on social and economic security does not have statistical significance. In contrast, housing, pension insurance participation, and public safety perception positively affect life satisfaction among older adults in rural areas. Among the social cohesion factors, higher social morality, legal system evaluation, social identity, grassroots government, and interpersonal trust contribute to the life satisfaction of older adults in rural areas. Regarding social inclusion factors, good social tolerance, social equity, and perception of government public services can significantly improve life satisfaction among older adults in rural areas. For social empowerment factors, social participation helps expand the social support network of older adults in rural areas, enhancing their life satisfaction. Paths to improving the quality of rural society should be explored to improve relative poverty in rural areas. They should continue to be pursued to strengthen the old-age security system for older adults in rural areas. Further, the National Medium- and Long-Term Plan for Actively Responding to Population Aging should be the base for forming an elderly-friendly society. A good atmosphere promotes the participation of people, families, and society; supports social cohesion; enhances inclusion; and promotes social participation, improving life satisfaction.

The objective of this study was to examine factors associated with symptoms of depression and psychological distress during the COVID-19 pandemic in China. Convenience sampling and snowball sampling were used to recruit a sample of adults in China (n = 2130) from 14 February 2020 to 3 March 2020 to complete an online survey. Linear regression was used to examine the predictors of symptoms of depression and psychological distress. Living in a non-urban area and the number of confirmed cases in their city of residence were positively associated with symptoms of depression. Female gender, not being married, practicing social distancing, the amount of time spent daily on social media searching for and reading information on COVID-19, the number of confirmed cases of COVID-19 in their city of residence, and having confirmed or suspected cases of COVID-19 in personal networks were positively associated with psychological distress. Social distancing is a widely used public health approach for population-wide virus-containment of COVID-19. However, reductions in population-wide psychological well-being are inadvertent consequences of social distancing. There is an emerging need to negate factors that increase adverse mental health vulnerabilities during the COVID-19 pandemic.

The future of social work research relies on the intellect and competence of current doctoral students. These future scholars who receive doctoral education that values qualitative inquiry will create a system where qualitative research traditions receive the same privilege as quantitative research traditions. Project-based learning provides learning opportunities that can challenge assumptions about what academia considers “real” research. This descriptive qualitative study explored key attributes of using project-based learning within two consecutive social work doctoral courses to encourage qualitative research skill development. Students and instructors participated in ideawriting and focus groups to assess the usefulness of PBL within these courses. The findings suggested that PBL may be useful for deepening knowledge about qualitative inquiry and reducing epistemological unconsciousness.