Do Systemic Inequities Lead to Differences Between Information Behaviors of Older Adults in the USA and India During the COVID-19 Pandemic?
Bibliographic record
Abstract
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 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.039 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".