Implementing Robotic Pets in Continuing Care Settings: A Scoping Review of Barriers and Facilitators
Bibliographic record
Abstract
BACKGROUND: Robotic pets are a unique technological innovation found among facility-based continuing care (CC) settings for older adults living with or without dementia. While researchers have reported positive outcomes for older adults who interact with robotic pets, unintended negative consequences may occur if robotic pets are not implemented properly. We examine the current evidence describing barriers and facilitators of implementing robotic pets for older adults residing in CC facilities to inform implementation practices. METHODS: A scoping review was conducted following the methodological framework outlined by Arksey and O'Malley. Five databases and the CADTH Gray Matters tool were used to identify relevant articles and gray literature. Our inclusion criteria followed the Population, Concept, Context (PCC) framework: Population: older adults living with or without dementia; Concept: barriers and facilitators to implementing robotic pets to older adults; Context: CC facilities. Two reviewers independently screened all titles, abstracts, and full-text articles and extracted the data. Two reviewers also organized barriers and facilitators into the theoretical domains framework (TDF) domains, which map onto the capability, opportunity, and motivation behavioral Change Wheel (COM-B). RESULTS: We identified 518 unique articles from our database and gray literature search, 42 of which met our inclusion criteria. Barriers and facilitators were identified across all 14 domains of the TDF and all six components of the COM-B. Domains mentioned in ≥ 50% of the articles include environmental context and resources, beliefs about consequences, and social influences. Common facilitators include knowledge of the benefits of robotic pets and how to use robotic pets, while common barriers include concerns over infantilization and hygiene. CONCLUSIONS: Our findings will help inform care providers of the barriers and facilitators to implementing robotic pets within CC settings with the goal of improving the quality of life of older adults.
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 | 0.056 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".