INVESTIGATING ETHICAL DIMENSIONS AND TENSIONS WITH RESPECT TO THE USE OF RTLS IN DEMENTIA CARE
Bibliographic record
Abstract
Abstract Real-time location systems (RTLS) are increasingly being developed to track older adults with dementia across settings. Specifically, it is believed that such technologies can support automation of care tasks (e.g. locating) and the development of clinical algorithms to predict changes in health and wellbeing. The limited available research suggests that these technologies can have significant ethical implications, including increasing control over older adults, and undermining their rights. In this study, we explored the experiences and perceptions of RTLS by older residents, care partners, and organizational decision-makers in one care home in Ontario, Canada. Findings demonstrate that participants (N=47) had a limited understanding of RTLS (e.g., how it could be used, data storage, ethical issues) and this in turn influenced their perceptions of its value. Residents were generally unaware of its purpose and more concerned with its aesthetics. Care partners and organizational decision-makers valued the RTLS for enhancing their efforts to control risk to the physical safety of residents and believed this was central to enhancing quality of care. Most had limited awareness of residents’ preferences and believed that their cognitive impairment and/or frailty disqualified their current or prior values and wishes, including their desire for privacy and autonomy, and thus could be legitimately overridden in the interest of protecting their safety. We discuss these findings in relation to digital ageism and offer suggestions to guide future policy and educational interventions to better align development and use of RTLS and similar surveillance technologies with the wishes and preferences of older adults.
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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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".