Evaluation of a virtual 4-week digital literacy program for older adults during COVID-19: a pilot study
Bibliographic record
Abstract
Older adults have become more dependent on using technologies to connect and communicate with others across the globe. This insight has since become more evident with the COVID-19 pandemic. While many older adults have increased their skills with these technologies, many more lack the necessary knowledge and skillset to effectively benefit from their use. To provide them with an accessible and older adult friendly digital training, in the summer of 2021, we pilot tested a brief 4-week digital literacy program to train older adults on key skills related to navigating their computer such as sending e-mails or traversing the web, etc. A convenience sample of 5 older adults volunteered for this brief intervention study in which they were to participate in a 1.5-hour intervention twice a week, for 4 weeks (8 total sessions). Topics varied from class to class. Results suggest that average computer proficiency scores were higher post intervention compared to pre-intervention. Additionally, post intervention scores were higher on computer basics, communication, and Internet subscales. All sessions were typically completed within the proposed time. The main technical issues identified were related to connecting to the digital sessions as well as navigating application interfaces across devices (i.e., differences in icons or application names between iOS and Android powered devices). Overall, these findings would suggest that older adults may be able to quickly gain digital literacy skills in a short period of time, provided that they are well supported.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".