Exploring Older Adults’ Adoption and Use of a Tablet Computer During COVID-19: Longitudinal Qualitative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As mobile computing technology evolves, such as smartphones and tablet computers, it increasingly offers features that may be particularly beneficial to older adults. However, the digital divide exists, and many older adults have been shown to have difficulty using these devices. The COVID-19 pandemic has magnified how much older adults need but are excluded from having access to technologies to meet essential daily needs and overcome physical distancing restrictions. OBJECTIVE: This study sought to understand how older adults who had never used a tablet computer learn to use it, what they want to use it for, and what barriers they experience as they continue to use it during social isolation caused by the COVID-19 pandemic. METHODS: We conducted a series of semistructured interviews with eight people aged 65 years and older for 16 weeks, investigating older novice users' adoption and use of a tablet computer during the nationwide lockdown due to COVID-19. RESULTS: Participants were gradually yet successfully accustomed to using a tablet computer to serve various daily needs, including entertainment, social connectedness, and information-seeking. However, this success was not achieved through developing sufficient digital skills but rather by applying the methods they were already familiar with in its operation, such as taking and referring to instruction notes. CONCLUSIONS: Our findings imply that older adults without digital literacy can still benefit from a digital device for quality of later life if proper traditional methods they are already familiar with are offered in its use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it