SURVEILLANCE TECHNOLOGIES IN LONG-TERM CARE: A BLIND SPOT FOR GERONTOLOGISTS?
Bibliographic record
Abstract
The commercial visibility of technological devices that monitor in the name of care has exploded with the expansion of internet-connected home surveillance products. Cameras that are popular for pet or child monitoring are now accessible for a variety of purposes in elder care. In multiple countries, public interest in the use of surveillance cameras in nursing home resident rooms is renewed periodically when major news outlets highlight a case of abuse captured by a hidden camera. Whether initiated by facilities or family members, camera use is at once a complex practice, policy, and ethical issue. Neither facility staff nor the resident who is placed under surveillance is likely to be a decision-maker when the question of camera use comes up in long-term care facilities. Policy makers, facilities, and family members thus carry a heavy responsibility to understand the nuances and consider all consequences of camera use in older adults’ living spaces. In this symposium, gerontologists from Canada, Scotland, and the U.S. will present findings from their research on camera use in nursing homes, assisted living communities, and private homes. Drawing on research in long-term care facilities, we will consider how cameras are used in practice by facilities to monitor the behavior of residents and care workers. The experiential complexities of living with surveillance technologies at home will be described along with ethical implications. We will then present a legal analysis of U.S. state nursing home electronic monitoring laws to examine how these laws balance multiple stakeholders’ vulnerabilities.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".