A Seal That Heals: Resident & Staff Perspectives on PARO in a Long-Term Care Home
Bibliographic record
Abstract
Abstract In Canada, dementia is highly prevalent in long-term care (LTC), with estimates suggesting that nearly two-thirds of residents live with some form of cognitive impairment. Within LTC, residents often face social isolation, loneliness, and agitation, challenges compounded by limited staffing resources. Socially assistive robots, such as PARO, have been developed to reduce stress and promote well-being. Yet, most research has examined either LTC residents’ or staff’s perspectives in isolation, limiting understanding of its broader impact. Our study explored the experiences of both residents with dementia and staff when using PARO in an LTC setting. Over four weeks from February to March 2025, residents (n = 10) engaged with PARO during group session, while staff (n = 10) participated in reflective sessions informed by video excerpts of these interactions. Reflexive thematic analysis revealed three themes from residents’ and staff’s perspectives respectively. For residents, PARO brought delight, fostered emotional validation, and promoted social connections and shared experiences. Staff feedback corroborated those themes, adding that PARO was a potential resource in the care toolkit to support person-centered care, and that its unique features generated strong engagement, but also presented physical accessibility, feasibility, and sustainability challenges. Findings highlight that PARO can be a valuable tool to enhance dementia care when both resident and staff perspectives are considered. Sustainable adoption requires collaboration among staff, administrators, and researchers to address accessibility, integration, and long-term feasibility. This study contributes to a holistic understanding of PARO’s potential role in LTC and informs strategies for advancing socially assistive technologies in dementia care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".