Using telepresence robots as a tool to engage patient and family partners in dementia research during COVID-19 pandemic: a qualitative participatory study
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) settings have been disproportionately affected by the COVID-19 pandemic; it is important to address unmet needs and explore practical strategies for supporting LTC residents and staff. The involvement of patient partners and family community members in research planning, implementation and evaluation is the basis of Patient and Public Involvement approach and has been challenging during the COVID-19 pandemic, as visitation restrictions have created barriers to conducting research in healthcare settings. Innovative methods and tools are needed for conducting participatory research. This study investigated the use of telepresence as innovative tools for participatory research based on three projects conducted with patient and family partners during the COVID-19 pandemic. METHODS: The data source includes (a) team reflective discussions, (b) weekly meeting notes, (c) field notes, and (d) interviews with ten researchers. We applied purposive sampling to select ten researchers who used a telepresence robot to conduct research in British Columbia, Canada. Semi-structured one-to-one interviews were conducted via Zoom virtual meetings. Patient and family partners contributed to team analysis to identify themes. RESULTS: Analysis of the data produced five themes: (1) Research Enabler, (2) User-Friendly Technology, (3) Increased Engagement, (4) Lack of Infrastructure and Resources, and (5) Training and Technical Obstacles. Based on the results, we propose "ROBOT"-an acronym for five actionable recommendations to support the use of telepresence robots for research. The ROBOT recommendations represent: Realign to adapt, Organize with champions, Blend strategies, Offer timely technical assistance, and Tailor training to individual needs. CONCLUSIONS: This study offers practical insights into the use of telepresence robots as a safe and innovative tool for conducting remote research with people with dementia, even in times of restricted access, as with COVID-19. Future research should apply more creativity and flexibility in adopting technology to expand possibilities for involving people with dementia in research.
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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 | 0.026 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".