Toward Proactive Support for Older Adults
Bibliographic record
Abstract
Peer support is a powerful tool in improving the digital literacy of older adults. However, while existing literature investigated reactive support, this paper examines proactive support for mobile safety. To predict moments that users need support, we conducted a user study to measure the severity of mobile scenarios (n=300) and users' attitudes toward receiving support in a specific interaction around safety on a mobile device (n=150). We compared classification methods and showed that the random forest method produces better performance than other regression models. We show that user anxiety, openness to social support, self-efficacy, and security awareness are important factors to predict willingness to receive support. We also explore various age variations in the training sample on moments users need support prediction. We find that training on the youngest population produces inferior results for older adults, and training on the aging population produces poor outcomes for young adults. We illustrate that the composition of age can affect how the sample impacts model performance. We conclude the paper by discussing how our findings can be used to design feasible proactive support applications to provide support at the right moment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".