Health Applications of Gerontechnology, Privacy, and Surveillance: A Scoping Review
Bibliographic record
Abstract
In this era of technological advances designed to assist older adults to age in place and monitor health challenges, the emphasis has been on the surveillance of older adults for their safety and the peace of mind of caregivers. This article focuses on two emerging gerontechnologies: wearables and smart home or ambient assistive living (AAL) devices. In order to explore the intersections of the ageing enterprise and surveillance capitalism, this scoping review addresses the following questions: (1) what are the existing technologies; (2) what are the privacy concerns raised by participants, researchers, and caregivers due to intended and unintended uses of these technologies? Specifically, this article synthesizes twenty relevant sources concerning the surveillance potentials of these gerontechnologies and the privacy implications for adults aged sixty-five and over. While these technologies may offer older adults greater autonomy/safety and caregivers peace of mind, their surveillance and privacy infringement potentials cannot be overlooked or cast as a trade-off. Amidst the automation of the care, collection, combination, and commodification of various forms of personal, health, and wellness metadata, the right to privacy, dignity, and ageing in place must remain central to the adoption and use of these technologies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".