Caregivers' Role in Cybersecurity for Aging Information Technology Users with Intellectual Disabilities
Bibliographic record
Abstract
Information technology (IT) users with intellectual disabilities (ID) are likely to experience online privacy violations without adequate support from their caregivers. Given that aging users face additional challenges when using IT than their younger counterparts, the goal of this exploratory study is to investigate caregivers' strategies and barriers for helping to protect the privacy of aging IT users with ID. Six caregivers (four paid caregivers, two family members) of aging users with ID completed a series of six focus groups about their experiences assisting the people they support with using IT, including their strategies and barriers for helping to protect these users' privacy. Participants were also asked about their own attitudes and experiences related to online privacy and information security. Based on our inductive thematic analysis of the qualitative data, participants used three main strategies to help protect the privacy of aging users with ID: (1) restricting access to personal information, (2) limiting disclosure of personal details, and (3) providing just-in-time instruction and feedback. We also identified four key barriers to privacy protection: (1) limited awareness and knowledge about information security, (2) balancing privacy and autonomy, (3) maintaining professional boundaries, and (4) residential care services' policies. Inclusive and transdisciplinary research is needed to address the elevated privacy and security risks for aging IT users with ID, and provide caregivers with training on how to support this population to use IT safely. Technology developers should create solutions to decrease aging users with ID's dependence on caregivers for privacy protection.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".