HEALTHCARE WORKERS’ PERSPECTIVES ON AI-ENABLED ROBOTS USE IN LONG-TERM CARE: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Artificial intelligence (AI) enabled robots are increasingly implemented in long-term care homes (LTC). However, the views of LTC staff on these robots remain largely unexplored. Our scoping review delves into the staff’s perceptions, outlining the advantages and challenges of using AI robots in LTC settings. Using the Joanna Briggs Institute’s methodology, we screened 86 articles from 2013 to 2023, with 35 fitting our criteria. Our analysis was informed by McCormack’s Person-centred Care Practice (PCP) Framework and the Consolidated Framework for Implementation Research (CFIR). We identified five key barriers: 1) the complexity of the robot, 2) potential job losses and increased workload, 3) concerns about safety and efficacy, 4) risk of depersonalized care, and 5) a lack of supportive regulations and resources in LTC facilities. To address these challenges, we recommend strategies: a) staff training, b) clarifying robot benefits to staff, c) demonstrating how robots can fulfill resident needs, d) implementing ethical guidelines, and e) aligning robot use with LTC policies while ensuring resource availability. In conclusion, partnership is required among healthcare workers, organizational leaders, robot developers, and researchers; they should not work in silos. More research is needed to explore how to facilitate effective partnerships.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".