Values of Stakeholders Involved in Applying Surveillance Technology for People With Dementia in Nursing Homes: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Due to the progressive nature of dementia, concerns about the safety of nursing home residents are frequently raised. Surveillance technology, enabling visual and auditory monitoring, is often seen as a solution for ensuring safe and efficient care. However, tailoring surveillance technology to individual needs is challenging due to the complex and dynamic care environment involving multiple formal and informal stakeholders, each with unique perspectives. OBJECTIVE: This study aims to explore the scientific literature on the perspectives and values of stakeholders involved in applying surveillance technology for people with dementia in nursing homes. METHODS: We conducted a scoping review and systematically searched 5 scientific databases. We identified 31 articles published between 2005 and 2024. Stakeholder characteristics were extracted and synthesized according to the theory of basic human values by Schwartz. RESULTS: In total, 12 stakeholder groups were identified, with nursing staff, residents, and informal caregivers being the most frequently mentioned. Among stakeholder groups close to residents, values related to benevolence, security, conformity, and tradition were most commonly addressed. Furthermore, values such as self-direction, power, and achievement seemed important to most stakeholder groups. CONCLUSIONS: Several stakeholder groups emphasized the importance of being and feeling involved in the application of surveillance technologies. In addition, they acknowledged the necessity of paying attention to stakeholders' perspectives and values. Across these stakeholder groups, values related to benevolence, security, and self-direction were represented, although various stakeholders assigned different meanings to these values. Awareness of stakeholders' perspectives demands a willingness to acknowledge each other's values and bridge differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".