Technological risks and ethical implications of using robots in long-term care
Bibliographic record
Abstract
Introduction The pandemic provides a unique opportunity to examine new directions in innovative technological approaches in long-term care (LTC) homes. While robotics could enhance staff capacity to provide care, there are potential technology risks and ethical concerns involved in technology use among older people residing in communal aged care homes. This qualitative descriptive study explores the technological risks and ethical issues associated with the adoption of robots in the specific context of LTC homes. Methods The research team including patient and family partners employed purposive and snowballing methods to recruit 30 LTC participants: frontline interdisciplinary staff, operational leaders, residents and family members, and ethics experts in dementia care. Semi-structured interviews were conducted. Thematic analysis was performed to identify themes that capture empirical experiences and perspectives of a diverse group of LTC stakeholders about robotic use. Results Technological risks include safety, increased workload, privacy, cost and social justice, and human connection. The findings offer practical insights based on the LTC perspective to contribute to the robot ethics literature. We propose a list of pragmatic recommendations, focusing on six principles (ETHICS): Engagement of stakeholders, Technology benefit and risk assessment, Harm mitigation, Individual autonomy, Cultural safety and justice, Support of privacy. Conclusions There is both a growing interest as well as fear in using robotics in LTC. Practice leaders need to reflect on ethical considerations and engage relevant stakeholders in making technology decisions for everyday care.
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 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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".