Evidence Summary

Do Systemic Inequities Lead to Differences Between Information Behaviors of Older Adults in the USA and India During the COVID-19 Pandemic?

A Review of:

Lund, B. D., & Maurya, S. K. (2022). How older adults in the USA and India seek information during the COVID-19 pandemic: A comparative study of information behavior. IFLA Journal, 48(1), 205–215. https://doi.org/10.1177/03400352211024675

Reviewed by:

Christine Fena

Undergraduate Success Librarian

Stony Brook University Libraries

Stony Brook, New York, United States of America

Email: christine.fena@stonybrook.edu

Received: 25 Nov. 2022 Accepted: 26 Jan. 2023

2023 Fena. This is an Open Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.

DOI: 10.18438/eblip30257

Abstract

Objective – To investigate and compare the information-seeking behaviors of older adults in one developing and one developed country during the COVID-19 pandemic.

Design – Structured interviews via Zoom (video), telephone, or email.

Setting – Two towns with moderately large populations (about 300,000), one in eastern India and one in the Midwest of the USA.

Subjects – Sixty adults ages 65 and older, 35 in the India cohort and 25 in the USA cohort.

Methods – The researchers recruited participants from the communities in which their respective institutions are located by using online advertisements in Facebook groups, local (print) advertisements/flyers, and word of mouth. The ten interview questions were informed by Dervin’s (1998) sense-making methodology and sought to identify a specific information need, behavior to address the need, and the influences on and outcomes of the behavior. They conducted the interviews in July and August of 2020, translated the questions into Hindi for Hindi-speaking participants, and analyzed responses using qualitative content analysis. Within each of the resulting themes and categories, the researchers compared the responses of American and Indian participants.

Main Results – The researchers found many significant differences between the information behaviors of Indian and American participants. Some of the biggest differences were in the information needs expressed by the participants, as well as the sources consulted and the reasons for consulting those sources. For example, when asked about the types of information needed, 77% of Indians focused on a “COVID and health-related” information need, as opposed to only 33% of Americans. And 37% of Americans indicated information needs related to “political and economic issues,” especially the upcoming 2020 election, as opposed to only 3% of Indians. When asked about sources, 28% of Indians consulted television, compared to only 6% of Americans. Web-based sources were generally used more by Americans, with 31% of Americans consulting websites, compared to 13% of Indians. In regard to their reasons for consulting a source, 28% of Indians chose a source based on availability, compared to only 9% of Americans. And 32% and 36% of Americans chose information based on ease and familiarity (“I know how to find it”), compared to only 18% and 13% of Indians, respectively. Only 3% of Indians met all their information needs, as opposed to 43% of Americans, and Indians were more likely to stop searching after encountering barriers. Americans had more confidence in their information behavior overall, and only 32% of Americans were interested in taking a class on how to find information, as opposed to 97% of Indians.

Conclusion – Older adults in developing and developed countries described very different information-seeking experiences. The disparities between the types of information sought, sources consulted, and barriers encountered highlight not only cultural differences, but also systemic inequities that exist between the information infrastructure of the two countries, especially as concerns access to computers and the Internet. The study points to areas for future improvement, including the need for interventions such as information literacy instruction.

Commentary

Many research areas contextualize this study, including the digital divide, the impact of socioeconomic status, the issues facing older adults as a population, and the role of information access in mitigating a global pandemic and creating communities that are health literate and achieve mental wellness. Xie et al. (2020) recognized the interconnected relationship between information crises and global health crises. Those most vulnerable to a lack of information access also become vulnerable to the health crisis. Research on information behaviors of older adults demonstrates a unique set of challenges, including willingness to adapt to new technologies (Berkowsky et al., 2017) and the role of Internet use in reducing depression (Cotten et al., 2012). Furthermore, Hargittai and Dobransky (2017) point to the role of socioeconomic status in one’s Web-using skills.

This study was appraised using “The CAT: A generic critical appraisal tool” (Perryman & Rathbun-Grubb, 2014), and has many strengths. The authors have expertise in information behavior and are affiliated with the University of North Texas and BRM Government Model College, respectively. The methodology and objective are well matched. The methods used – Dervin’s (1998) sense-making approach, interview questions, and content analysis – address the complexity of the diverse research contexts well, since they are flexible ways to qualitatively investigate information behavior and identify gaps in individual information seeking. Another strength of the study is that the authors are transparent in locating themselves within the towns from which the participants were recruited; one of the authors lives in the American town and the other lives in the Indian town. Finally, the authors adequately represent the Hindi-speaking population by ensuring the interview questions were translated into Hindi. They included the interview questions in both English and Hindi within an appendix.

As the authors point out in their discussion of the study’s limitations, however, the sample size was not broad-based or the results statistically strong. Although the authors do list their methods of recruitment, they do not include their selection criteria for the 60 participants. They also leave out more detailed information about preparatory actions related to the study and development and execution of the interviews, such as whether they obtained IRB approval, methods of writing and piloting the interview questions, whether they considered the cultural relevance of the interview questions for both locations, and how many participants were interviewed within each modality (video, telephone, email). In the reporting of the results, they leave out demographic information beyond whether the participants were Indian or American. Additionally, as information and conditions change throughout the COVID-19 pandemic one might expect responses to the interview questions would also change, and thus these results only represent a “snapshot in time” (Lund & Maurya, 2022, p. 213).

Despite these weaknesses, the study demonstrates the extent to which information access impacted two groups of older adults differently in India and the U.S. at a specific historical moment. Practice implications include the potential desire and need for information literacy instruction within the Indian community studied, and the need for further research to determine the desire for instruction in similar communities. Finally, the authors point to the importance of library administrators’ understanding of how cultural differences and infrastructure constraints impact the delivery of services and resources.

References

Berkowsky, R., Sharit, J., & Czaja, S. (2017). Factors predicting decisions about technology adoption among older adults. Innovation in Aging, 1(3). https://doi.org/10.1093/geroni/igy002

Cotten, S., Ford, G., Ford, S., & Hale, T. (2012). Internet use and depression among older adults. Computers in Human Behavior, 28(2): 496–499. https://doi.org/10.1016/j.chb.2011.10.021

Dervin, B. (1998) Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of Knowledge Management 2(2): 36–46. https://doi.org/10.1108/13673279810249369

Hargittai, E., & Dobransky, K. (2017). Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication, 42(2): 195–212. https://doi.org/10.22230/cjc.2017v42n2a3176

Perryman, C., & Rathbun-Grubb, S. (2014). The CAT: A generic critical appraisal tool. http://www.jotform.us/cp1757/TheCat

Xie, B., He, D., Mercer, T., Want, Y., Wu, D., Fleischmann, K., Zhang, Y., Yoder, L., Stephens, K., Mackert, M., & Kyung Lee, M. (2020). Global health crises are also information crises: A call to action. Journal of the Association for Information Science and Technology, 71(12): 1419–1423. https://doi.org/10.1002/asi.24357