The perceptions of university students on technological and ethical risks of using robots in long-term care homes
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has disproportionately impacted long-term care (LTC) residents and exacerbated residents’ risks of social isolation and loneliness. The unmet emotional needs of residents in LTC have driven researchers and decision-makers to consider novel technologies to improve care and quality of life for residents. Ageist stereotypes have contributed to the underuse of technologies by the older population. Telepresence robots have been found to be easy to use and do not require older adults to learn how to operate the robot but are remotely controlled by family members. The study aimed to understand the perspectives of multidisciplinary university students, including healthcare students, on using telepresence robots in LTC homes. The study would contribute to the future planning, implementation, and design of robotics in LTC. Methods: Between December 2021 and March 2022, our team conducted interviews with 15 multidisciplinary students. We employed a qualitative descriptive (QD) approach with semi-structured interview methods. Our study aimed to understand the perspectives of university students (under the age of 40) on using telepresence robots in LTC homes. Participants were invited to spend 15 min remotely driving a telepresence robot prior to the interview. A diverse team of young researchers and older adults (patient and family partners) conducted reflexive thematic analysis. Results: Six themes were identified: Robots as supplementary interaction; privacy, confidentiality, and physical harm; increased mental well-being and opportunities for interactions; intergenerational perspectives add values; staffing capacity; environmental and cultural factors influence acceptance. Conclusion: We identified a diverse range of perspectives regarding risk and privacy among participants regarding the implementation of telepresence robots in long-term care. Participants shared the importance of the voice of the resident and their own for creating more equitable decision-making and advocating for including this type of technology within LTC. Our study would contribute to the future planning, implementation, and design of robotics in LTC.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".