Care partner experience with telepresence robots in long-term care during COVID-19 pandemic
Bibliographic record
Abstract
Objective: As people living with dementia move into long-term care (LTC), their care partners face a difficult role change from primary caregiver to visitor, losing a significant degree of control and direct care involvement. The COVID-19 pandemic exacerbated these challenges with health risks, changing care home protocols, and government policies. To help address these challenges, this study aimed to investigate the experiences of care partners who used telepresence robots to maintain contact with and care for their loved ones during the pandemic. Methods: This study was guided by the Collaborative Action Research (CAR) approach. Along with interdisciplinary researchers and trainees, our team included patient and family partners as co-researchers throughout the project. We conducted semi-structured interviews with 20 care partners who used the robots in five urban Canadian LTC homes between May 2021 and August 2023. Results: Thematic analysis identified four key themes characterizing their experiences using the robot: (a) decreases care partner burden, (b) facilitates care partner-staff relationship, (c) creates relational autonomy, and (d) expands the scope of what is possible. Conclusion: The results of the study suggest that telepresence robots can play a useful role in enhancing the caregiving experience for informal care partners in multifaceted ways. Care partners reported positive benefits of having the robot assist their virtual visits. However, further research is needed to determine the sustainability of robot implementation among diverse geographic regions and care home compositions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".