Using a smart audio device, Tochie to deliver family messages in long‐term care
Bibliographic record
Abstract
Abstract Background Long‐term care (LTC) residents are more likely to experience loneliness, social isolation and have dementia (Garner et al., 2018; Hou & Ngo, 2021). The effects of isolation have been exacerbated by the COVID‐19 pandemic. While assistive technology has been a popular topic in gerontological health research, there have been limited findings on the experience of technology adoption in complex care environments such as LTC, and on family member and staff perspectives. “Tochie” is a smart audio device that allows family and significant others to remotely record and schedule messages, such as daily reminders or comforting audio recordings, to send to their loved ones in care settings. Our research explores the experiences of residents, families, and staff using the Tochie device in LTC. Method We applied qualitative descriptive design with interview and focus group methods. 10 residents, 9 family members, and 6 care staff from two long‐term care homes in British Columbia, Canada, participated in the study. Each resident was given a device to use with their family member for a four‐week intervention period. The research team checked in with family members and staff weekly via telephone and email to provide support and gather feedback. Pre‐ and post‐intervention focus groups and interviews were held via Zoom and phone correspondence to learn about participants’ experiences using Tochie. Thematic analysis was performed to identify themes. Result Four common themes were identified to describe the experience of using Tochie in LTC: (a) Tochie as a means to facilitate emotional connection (b) Using Tochie in creative, personalized ways (c) Challenges and contextual considerations in LTC (d) Ideas for future developments Conclusion The COVID‐19 pandemic has provided us an opportunity to redefine and reconstruct what it means to “keep in touch” with loved ones in care settings. In our study, residents, families and staff highlighted the ways in which Tochie has enabled this connection and provided feedback on their challenges and device recommendations. This study explores the valuable perspectives of residents, their loved ones, and care staff in the use of assistive audio devices to inform product development and adoption into LTC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".