Habit and Help—Experiences of Technology Use During the COVID-19 Pandemic: Interview Study Among Older Adults
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic compelled older adults to engage with technology to a greater extent given emergent public health observance and home-sheltering restrictions in the United States. This study examined subjective experiences of technology use among older adults as a result of unforeseen and widespread public health guidance catalyzing their use of technology differently, more often, or in new ways. OBJECTIVE: This study aimed to explore whether older adults scoring higher on the Unified Theory of Acceptance and Use of Technology questionnaire fared better in aspects of technology use, and reported better subjective experiences, in comparison with those scoring lower. METHODS: A qualitative study using prevalence and thematic analyses of data from 18 older adults (mean age 79 years) in 2 groups: 9 scoring higher and 9 scoring lower on the Unified Theory of Acceptance and Use of Technology questionnaire. RESULTS: Older adults were fairly competent technology users across both higher- and lower-scoring groups. The higher-scoring group noted greater use of technology in terms of telehealth and getting groceries and household items. Cognitive difficulty was described only among the lower-scoring group; they used technology less to get groceries and household items and to obtain health information. Qualitative themes depict the role of habit in technology use, enthusiasm about technology buttressed by the protective role of technology, challenges in technology use, and getting help regardless of technology mastery. CONCLUSIONS: Whereas the pandemic compelled older adults to alter or increase technology use, it did not change their global outlook on technology use. Older adults' prepandemic habits of technology use and available help influenced the degree to which they made use of technology during the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".