Older Adults’ Acceptance of Technology During the Pandemic: The COVID Technology Acceptance Model (TAM)
Bibliographic record
Abstract
Abstract During the pandemic, technology-mediated communication was one of the few ways to maintain social and community connections. We explored how the pandemic impacted older adults’ use and appraisal of technology. In a random sample of 407 older adults (M age = 81.1 years; range 65-105 years) almost half (n = 161) reported they changed how they used technology to virtually connect with others during the pandemic, and 78 of these reported that this was new technology for them. We adapted the technology acceptance model (TAM) for the pandemic, the COVID-TAM, and describe how physical distancing led to new acceptance of technology due to an increased perception of usefulness of technology for maintaining community and social connections. The 71 older adults who denied using technology were asked about the reasons underlying their reluctance to use technology to access social networks and community events during the pandemic. Thematic analysis revealed factors consistent with a double-digital divide; lack of physical exposure to technology creates an additional psychological barrier to adoption of new technology. Of the technology-reluctant subgroup of older adults, few reported lack of perceived usefulness of technology during the pandemic. Instead, most reported lack of self-efficacy or fear of technology underlying their lack of technology use for social and community connections during the pandemic, which we incorporate into the COVID-TAM. Findings indicate that technology training can help mitigate this fear and increase social and community connections that are technology-mediated in circumstances where physical distancing is necessary.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".