‘I want him to tell me he loves me’: A smart audio device, Tochie, for resident‐family connection in long‐term care
Bibliographic record
Abstract
BACKGROUND: Older people living in long-term care homes are particularly susceptible to loneliness and social isolation, which the COVID-19 pandemic has exacerbated further. 'Tochie' is a smart audio device that allows family members to remotely record and send messages, such as daily reminders and comforting recordings, to their loved ones in LTC settings. The purpose of this study was to assess the feasibility and acceptability of using Tochie to improve resident-family connections, and to investigate user experience, impact and lessons learned. METHODS: Participants included 10 residents, nine family members and six care staff from two LTC homes in British Columbia, Canada. A Tochie was provided to each resident to use with their family members over a 4-week intervention period. The research team provided support and gathered feedback from family members and care staff through weekly phone and email correspondence. Qualitative descriptive design was used, including pre- and post-intervention focus groups and interviews held via Zoom and phone to gather participants' experiences with Tochie. Themes were identified through thematic analysis. RESULTS: Three themes were identified: (1) Facilitating emotional connection, (2) Using the device in creative and personalised ways and (3) Structural challenges and supports. Based on these findings, recommendations for future research and practice are provided. CONCLUSION: The COVID-19 pandemic has prompted a rethinking of what it means to 'stay in touch' with loved ones in LTC settings. This study found that Tochie has opened up new opportunities for family connection and provided emotional support for residents. The results of this study offer valuable insights into the implementation of assistive technology in LTC homes to support resident care.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".