Kristina Shiroma

Within the profession of librarianship, organizing has often been related to information representation or the organization of information in such a way that aids in its identification, retrieval, ...

Purpose: Online directories play an important apomediary role in shaping how older adults find and compare potential providers. However, little is known about how this type of health information represents older adult needs and aging-related mental health expertise. This study examines how mental health professionals represent older adults and age-related care in their Psychology Today profiles. Methods: We conducted a mixed-methods content analysis of 281 Psychology Today provider profiles across 8 U.S. cities. We used frequency counts and descriptive statistics to characterize provider attributes and areas of expertise. Using thematic analysis, we examined how providers describe their approach to serving older adults and aging-related mental health care. Quantitative findings informed and contextualized the qualitative analysis. Results: We identified one overarching theme “Older Adults at the Limits of Apomediated Visibility” and four subthemes: “Absent Narratives of Aging,” “Token Mentions of Older Adults”, “Misaligned Descriptions of Expertise”, and “Generalist Framings Obscure Age-Specific Care”. These results suggest that apomediary signals of relevance may not consistently match substantive descriptions of provider expertise in older adult mental health care. Conclusions: Findings reveal gaps between platform filters and profile narratives that may undermine the directory’s apomediary role, making “older adult” care appear searchable while obscuring the age-relevant information older adults need to make informed health decisions. Better alignment between structured search criteria and narrative self-representation would strengthen informed decision-making and advance equitable access to age-responsive mental health services.

Background: Older adults in rural communities face unique challenges, including geographic isolation, a shortage of health care providers, and limited access to specialized services. Artificial intelligence (AI) has emerged as a promising solution for improving health care access and delivery. However, concerns persist about equitable access and representation in these innovations, especially for marginalized populations where technological literacy and infrastructure may present additional barriers to effective use. Objective: This systematic literature review aims to: (1) identify existing literature on AI for health research focused on rural older adults, and (2) critically evaluate the identified literature to highlight gaps and inform the development of inclusive AI for health research and design practices. Methods: Between January 2024 and March 2025, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Protocol to conduct a systematic search of health and computer science literature. We searched 7 databases (PubMed, CINAHL Plus with Full Text, PsycINFO, Web of Science, IEEE Explore, ACM Digital Library, and Scopus) to identify relevant research papers published between January 2013 and December 2023. We used predetermined search terms and built-in result limiters, including English language, human subjects, full text, and aged 65+. Publications were excluded if they were not empirical or did not include a focus on older adults, rural populations, or AI. The resulting data were reviewed, coded, and analyzed using thematic analysis. Results: A total of 23 papers comprised the final sample. The results showed that the representation of rural older adults in AI for health literature is limited. We identified three salient themes: (1) Numbers over Narratives: The Quantitative Focus in AI for Health Research on Older Adults, (2) Efficacy over Impact: Prevalence Clinical Outcomes in AI for Health Research, and (3) Deepening Disparities: Representation of Rurality Missing in AI for Health Research. These themes underscore the need for a more nuanced understanding of how AI for health research can be tailored to the specific needs of rural older populations. Conclusions: Our systematic analysis identified a robust body of research on AI for older adults. However, a critical gap emerged with a dearth of studies explicitly focusing on older adults in rural communities. This lack of representation raises concerns about the generalizability of findings and the potential for exacerbating existing health care disparities in rural areas. Future research should: (1) prioritize targeted recruitment strategies for rural older adult participants to ensure better representation in AI for health research; (2) develop community-based AI policies, practices, and products that reflect the specific needs and contexts of rural populations; and (3) explore solutions that address the limited representation of rural communities, ensuring that AI interventions are equitable, accessible, and beneficial for all.

BACKGROUND The demographic shift towards an aging population is particularly pronounced in rural areas where access to healthcare is often limited. Older adults in rural communities face unique challenges, including geographic isolation, a shortage of healthcare providers, and limited access to specialized services. Artificial intelligence (AI) has emerged as a promising solution for improving healthcare access and delivery. However, concerns persist about equitable access to and representation in these innovations, especially for marginalized populations where technological literacy and infrastructure may present additional barriers to effective use. OBJECTIVE This systematic literature review aims to: (1) identify existing literature on AI for health research focused on rural older adults; and (2) critically evaluate the identified literature to highlight gaps and inform the development of inclusive AI for health research and design practices. METHODS On January 25, 2024, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Protocol to conduct a systematic search of health and information science literature. We searched PUBMED and EBSCOhost (CINAHL Plus with Full Text and PsycINFO) databases to identify relevant research articles published between January 2013 and January 2023. We used predetermined search terms and built-in result limiters including: English language, human subjects, full text, and aged 65+. Publications were excluded if they did not include a focus on older adults’, rural populations, or artificial intelligence. The resulting data was reviewed, coded, and analyzed using thematic analysis. RESULTS Nine articles comprised the final sample. The results showed that representation of rural older adults in AI for health literature is limited. We identified three (3) salient themes: (1) Numbers over Narratives: The Quantitative Focus in AI For Health Research on Older Adults, (2) Efficacy Over Impact: Prevalence Clinical Outcomes in AI for Health Research, and (3) Deepening Disparities: Representation of Rurality Missing in AI for Health Research. These themes underscore the need for a more nuanced understanding of how AI for health research can be tailored to the specific needs of rural older populations. CONCLUSIONS Our systematic analysis identified a robust body of research on AI for older adults. However, a critical gap emerged: a dearth of studies explicitly focusing on older adults in rural communities. This lack of representation raises concerns about the generalizability of findings and the potential for exacerbating existing healthcare disparities in rural areas. Future research should: (1) prioritize targeted recruitment strategies for rural participants to ensure better representation of rural older adults in AI for Health research; (2) develop community-based AI policies, practices, and products that reflect the specific needs and contexts of rural populations; and (3) explore solutions that address the limited representation of rural communities, ensuring AI interventions are equitable, accessible, and beneficial for all populations.

Background: The COVID-19 infodemic has imposed a disproportionate burden on older adults who face increased challenges in accessing and assessing public health information, but little is known about factors influencing older adults’ trust in public health information during COVID-19. Objective: This study aims to identify sources that older adults turn to for trusted COVID-19 public health information and factors that influence their trust. In addition, we explore the relationship between public health information sources and trust factors. Methods: Adults aged 65 years or older (N=30; mean age 71.6, SD 5.57; range 65-84 years) were recruited using Prime Panels. Semistructured phone interviews, guided by critical incident technique, were conducted in October and November 2020. Participants were asked about their sources of COVID-19 public health information, the trustworthiness of that information, and factors influencing their trust. Interview data were examined with thematic analysis. Results: Mass media, known individuals, and the internet were the older adults’ main sources for COVID-19 public health information. Although they used social media for entertainment and personal communication, the older adults actively avoided accessing or sharing COVID-19 information on social media. Factors influencing their trust in COVID-19 public health information included confirmation bias, personal research, resigned acceptance, and personal relevance. Conclusions: These findings shed light on older adults’ use of information sources and their criteria for evaluating the trustworthiness of public health information during a pandemic. They have implications for the future development of effective public health communication, policies, and interventions for older adults during health crises.

Background: Older adults tend to have insufficient health literacy, which includes eHealth literacy—the ability to access, assess, and use digital health information. Interventions using methods such as collaborative learning (CL) and individualistic learning (IL) may be effective in addressing older adults’ low eHealth literacy, but little is known about the short- and long-term effects of CL versus IL on older adults’ eHealth literacy. Objective: The objective of this study was to use a 3 × 2 × 3 mixed factorial design to examine older adults’ learning with CL versus IL for eHealth literacy. Methods: Older adults (N=466; mean age 70.5, SD 7.2; range 60-96 years) from diverse racial and ethnic groups were randomly assigned to either the CL or IL group (233/466, 50% in each). The intervention consisted of 4 weeks of training in 2-hour sessions held twice a week. Using ANOVA and multiple regression, we focused on the main effects of learning condition and interaction between learning condition and previous computer experience. Learning method (CL or IL) and previous computer experience (experienced, new, or mixed) were between-subject variables, and time of measurement (pretest measurement, posttest measurement, and 6-month follow-up) was the within-subject variable. Primary outcome variables were eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills. Control variables were age, sex, education, health status, race and ethnicity, income, primary language, and previous health literacy. Results: eHealth literacy efficacy, computer and web knowledge, basic computer and web operation skills, information-seeking skills, and website evaluation skills improved significantly (P<.001 in all cases) from before to after the intervention. From postintervention measurement to 6-month follow-up, there was a significant interaction between learning condition and previous computer experience based on 1 outcome measure, computer and web operation skills (F2,55=3.69; P=.03). To maintain computer and web operation skills 6 months after the intervention, it was more effective for people with little to no previous computer experience to learn individually, whereas for people with more previous computer experience, it was more effective to learn collaboratively. From postintervention measurement to 6-month follow-up, statistically significant decreases were found in 3 of the 5 outcome measures: eHealth literacy efficacy, computer and web knowledge, and basic computer and web operation skills (P<.001 for all 3 cases). Conclusions: Older adults’ eHealth literacy can be improved through effective intervention, and the IL or CL condition may have little effect on short-term outcomes. However, to maintain long-term benefits, it may be best to learn collaboratively with others who have similar previous computer experience. eHealth literacy is multidimensional, with some components retained better over time. Findings suggest a need for resources to provide continuous training or periodic boosting to maintain intervention gains.

Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID‐19. We present findings from a cross‐sectional survey of 454 US adults—243 older (65+) and 211 younger (18–64) adults—who responded to questionnaires on human values, trust in COVID‐19 information sources, attention to information quality, self‐efficacy, and factual knowledge about COVID‐19. Path analysis showed that trust in direct personal contacts (B = 0.071, p = .04) and attention to information quality (B = 0.251, p < .001) were positively related to self‐efficacy for coping with COVID‐19. The human value of self‐transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self‐efficacy in coping with COVID‐19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001–0.104) and factual knowledge about COVID‐19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003–0.089). Our path model offers guidance for fine‐tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID‐19 and other public health crises.

BACKGROUND The COVID-19 infodemic has imposed a disproportionate burden on older adults who face increased challenges in accessing and assessing public health information, but little is known about factors influencing older adults’ trust in public health information during COVID-19. OBJECTIVE This study aims to identify sources that older adults turn to for trusted COVID-19 public health information and factors that influence their trust. In addition, we explore the relationship between public health information sources and trust factors. METHODS Adults aged 65 years or older (N=30; mean age 71.6, SD 5.57; range 65-84 years) were recruited using Prime Panels. Semistructured phone interviews, guided by critical incident technique, were conducted in October and November 2020. Participants were asked about their sources of COVID-19 public health information, the trustworthiness of that information, and factors influencing their trust. Interview data were examined with thematic analysis. RESULTS Mass media, known individuals, and the internet were the older adults’ main sources for COVID-19 public health information. Although they used social media for entertainment and personal communication, the older adults actively avoided accessing or sharing COVID-19 information on social media. Factors influencing their trust in COVID-19 public health information included confirmation bias, personal research, resigned acceptance, and personal relevance. CONCLUSIONS These findings shed light on older adults’ use of information sources and their criteria for evaluating the trustworthiness of public health information during a pandemic. They have implications for the future development of effective public health communication, policies, and interventions for older adults during health crises.

People who are blind share their images and videos with companies that provide visual assistance technologies (VATs) to gain access to information about their surroundings. A challenge is that people who are blind cannot independently validate the content of the images and videos before they share them, and their visual data commonly contains private content. We examine privacy concerns for blind people who share personal visual data with VAT companies that provide descriptions authored by humans or artifcial intelligence (AI) . We frst interviewed 18 people who are blind about their perceptions of privacy when using both types of VATs. Then we asked the participants to rate 21 types of image content according to their level of privacy concern if the information was shared knowingly versus unknowingly with human- or AI-powered VATs. Finally, we analyzed what information VAT companies communicate to users about their collection and processing of users’ personal visual data through their privacy policies. Our fndings have implications for the development of VATs that safeguard blind users’ visual privacy, and our methods may be useful for other camera-based technology companies and their users.