COMMUNITY-DWELLING OLDER ADULTS’ PERCEPTIONS OF SMART HOME SURVEILLANCE: AN INTEGRATIVE REVIEW
Bibliographic record
Abstract
Abstract Background Many older adults wish to use smart homes for aging in place, health monitoring, and enhanced safety. However, concerns over privacy and security remain pressing. User perception studies can help to inform policy and design solutions. Aim: To explore community-dwelling older adults’ (50+) perceptions of smart home surveillance. Methods As part of a larger scoping review of smart home user perception based on four non-mutually exclusive categories: privacy, safety, purpose of data collection, and risk, we found 68 results. 15 studies focused on older adults exclusively and were included in this review. Results The included studies mainly focused on smart speakers, motion sensors, or home monitoring systems. 13 studies (87%) discussed user privacy concerns in terms of data collection and access. Nine studies (60%) reported that users were enthusiastic about the potential for home safety, improved health outcomes and independent living with smart homes. Seven risk awareness studies (47%) featured a range of perspectives on sharing sensitive information due to the possibility of data breaches and third-party misuse, with some reporting a willingness to trade privacy for enhanced safety. Finally, four studies (27%) explored user knowledge of data collection purposes. While many were uncertain of the details, users were generally more comfortable sharing smart home data with healthcare professionals than others. Conclusion This review has helped us in creating a user perception survey that is currently in the fielding stage. Given Canada’s increasing aging population and technological advances, privacy regulators and designers should focus on older adults’ concerns.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".