Gluu Essentials Digital Skills Training for Middle-Aged and Older Adults That Makes Skills Stick: Results of a Pre-Post Intervention Study
Bibliographic record
Abstract
BACKGROUND: A number of real-world digital literacy training programs exist to support engagement with mobile devices, but these have been understudied. OBJECTIVE: The purpose of this study was to examine the effectiveness and program acceptability of a digital skills training program among middle-aged and older adults (aged ≥50 years) and to gather participants' recommendations for lifelong digital skills promotion. METHODS: The Gluu Essentials digital skills training program includes learning resources to support tablet use. Through pre-post surveys, this study assessed mobile device proficiency, confidence in going online and in avoiding frauds and scams, the frequency of engaging in online activities, program engagement, acceptability, and suggestions for continued support. RESULTS: A total of 270 middle-aged and older adults completed baseline surveys. Of these 270 participants, 145 (53.7%) completed follow-up surveys. Our findings indicate that mobile device proficiency increased (P<.001), whereas confidence was unchanged. Participants also reported going online more frequently to shop (P=.01) and access government services (P=.02) at follow-up. Program engagement varied considerably, but program acceptability was high. Participants' recommendations included the need for providing ongoing programs for support and training because technology constantly changes, reducing costs for technology and internet access, and keeping learning resources simple and easy to access. CONCLUSIONS: The Gluu Essentials digital skills training program increased mobile device proficiency and frequency of web-based activities (shopping and accessing government services) among middle-aged and older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".