Real-time location system implementation in dementia care: Stakeholder perspectives
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite interest in using real-time location systems (RTLS) to improve quality and efficiency of care and to collect data for the development of clinical algorithms, research on their implementation and use in long-term care homes is scarce. This study examines RTLS implementation in one long-term care home identifying failure points, ethical tensions, and sociotechnical misalignments that led to abandonment. RESEARCH DESIGN AND METHODS: Semistructured interviews were conducted with 47 participants (residents, care partners, direct care staff, managers, administrators) across two time points; all also completed a demographic survey. Thematic analysis of the data was guided by the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability framework, and Sociotechnical Systems theory. RESULTS: Initial enthusiasm for RTLS stemmed from safety and efficiency expectations, but misaligned functionalities, limited staff engagement, and ethical tensions undermined adoption. Stakeholders anticipated real-time monitoring and fall detection, neither of which were provided. Residents frequently removed the wearables, citing discomfort and privacy, while staff encountered barriers due to the limited integration of the RTLS into workflows. Ethical tensions emerged as residents' autonomy and preferences were overridden by care partners. DISCUSSION AND IMPLICATIONS: These failures underscore the need for iterative and participatory approaches to implementation, transparent communication, and stakeholder alignment. The findings highlight the sociotechnical complexities of implementing surveillance technologies in long-term care. Ethical concerns surrounding resident autonomy, workforce surveillance, and data governance, must be addressed in future implementations to ensure that RTLS supports rather than compromises the dignity and rights of residents and staff.
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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 | 0.052 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".