FAMILY EXPERIENCE IN USING TELEPRESENCE ROBOTS WITH RESIDENTS IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Informal care, most often provided by family or friends, remains a hidden and underrecognized undertaking within long-term care (LTC) settings. As care recipients transition into LTC, informal care partners experience a parallel shift in their caregiving roles: transitioning from primary care partner to visitor. Many care partners report this transition to be difficult, as they must surrender an unpredictable level of control and involvement over their loved one’s care. The COVID-19 pandemic intensified this experience by introducing new challenges: individual health concerns, fluctuating care home protocols, and isolating government policies. Telepresence robots are emerging as a tool that can ease the care partner transition. The main aim of this study is to examine the experiences of care partners who used telepresence robots with loved ones living in LTC settings, during the COVID-19 pandemic. This study took place between May 2021 and August 2023 in five urban LTC homes located in British Columbia, Canada. A total of 20 care partners were recruited through purposive sampling. The care partners participated in semi-structured interviews, in-person or virtually, and thematic analysis was employed to identify four key themes characterizing their experiences using the robot: 1) Decreases care partner burden, 2) Facilitates care partner-staff relationship, 3) Creates relational autonomy, and 4) Expands the scope of what is possible. Findings capture the ability of the robot to enhance the caregiving experience for informal care partners. Further research on the sustainability of robot implementation amongst diverse geographic regions and care home compositions is needed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".