Evaluation of a Tailored Digital Literacy Intervention in Affordable Older-Adult Housing: Case Study
Bibliographic record
Abstract
Background Older age, low socioeconomic status, living alone, and low English proficiency are independent factors associated with low information communication technology (ICT) use. Evidence-based interventions are needed to increase digital access and literacy among underrepresented groups. Objective This study aimed to increase the understanding of factors influencing ICT adoption and sustainable resources for training and support in affordable older-adult housing. Methods Broadband, tablet computers, training, and support were offered at 1 affordable older-adult housing community. Three 60-minute classes covered device basics, Google Translate, YouTube, and Zoom; in-language user guides were provided. Resident Ambassadors offered weekly in-language tech support. Mixed methods evaluation included surveys at entry, 30 days, and 90 days and key informant interviews. Results Overall, 72% (N=76) of residents participated. The average age was 78 (SD 8) years, and the participants were primarily Asian (62%), lived alone (68%), and had low English proficiency (65%). About half (49%) of the participants had less than a high school degree. Reasons to decline initial participation included: already owned another device, visual or cognitive challenges, or unwillingness to complete surveys. Of the participants, 89% attended at least 1 class and 37% attended all 3 classes. Over 90% of participants found the classes helpful, 87% found the user guide helpful, and 49% received help from a neighbor. At 30 and 90 days, 82% of the participants reported using their tablet at least twice per week for various activities. However, over half of participants reported the tablet was difficult to learn, and from 30 to 90 days, confusion and the fear of making mistakes when using the tablet slightly increased. Conclusions Overall, participants reported high satisfaction with the devices and tech support, although the decreasing comfort with technology over time indicates a need for additional training and ongoing support. This case study provides a model to increase ICT use among older adults in affordable older-adult housing communities.
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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 | 0.003 | 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".