Using telepresence robots as a tool for virtual research during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract Background Long‐term care (LTC) settings have been disproportionately affected by the COVID‐19 pandemic. It is necessary to investigate unmet needs and explore practical strategies for supporting LTC residents and staff. However, visitation restrictions and staff shortages have created barriers to conducting research in healthcare settings. Innovative methods and tools are needed for conducting research to support the research process. Telepresence robots enable virtual connections via videoconferencing and give a feeling of the person’s presence from a remote location. This study focuses on exploring the researchers’ experiences of using a telepresence robot as an interview tool. Method We interviewed a team of 10 researchers who used a telepresence robot to virtually conduct research during the COVID‐19 pandemic in British Columbia, Canada. The team includes academic researchers, graduate students and people living with dementia. Semi‐structured one‐to‐one interviews were conducted by Zoom virtual meetings. Thematic analysis was performed to identify themes. Result Analysis of the data produced five themes on benefits and challenges with respect to using a telepresence robot to conduct interviews with residents in LTC. Themes of benefits: (1) Use as a Research Enabler, (2) More accessible and engaged research process, and (3) Increased Environmental Inclusion and Engagement. Themes of challenges: (4) Lack of Infrastructure and Resources, and (5) Training and Technical Obstacles. Based on the results, we offer “ROBOT” – an acronym created for actionable recommendations that inspire and support others to use telepresence robots for research. These recommendations include Realign to adapt, Organize with champions, Blend strategies, Offer timely technical assistance, and Tailor training to individual needs. Conclusion This study offers unique and practical insights into using telepresence robots as a safe and innovative tool for conducting research remotely. Our results demonstrate that people living with dementia can be engaged meaningfully in research during the pandemic. 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 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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".