Exploratory study of Google Nest Hubs in the long-term care setting in Manitoba – Canada
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, many LTC facilities limited recreational and social activities to minimize the chances of an outbreak, leaving residents isolated.In response, we provided Google Nest Hub devices to 80 PCHs/supportive housing residences as an on-demand engagement mechanism for the residents and staff.Objective: To evaluate the experiences in setting up and using Google Nest Hub devices in long-term care settings.Method: We employed an online survey that explored the challenges and benefits of setting up and using the devices, who was using the devices, and how the devices were used.We analyzed the frequencies of the close-ended responses, and manually coded the open-ended responses before again analyzing the frequencies.Results: Thirty staff members from facilities that received a device completed the survey.The majority (N = 25) had already set up a device, while a few (N =5) had not.The experiences reported by the participants were overwhelmingly positive.The devices were used most by recreation staff, residents, and nursing staff.The most common uses were music, weather forecasts, and videos.The majority of respondents reported that the use of these devices provided ongoing interactions, and nearly all agreed that the effort of using the devices was worth the value.A few issues were encountered, largely related to facilities' Wi-Fi resources, and challenges surrounding speech as a means of using the devices.Many benefits were reported, and the use of the devices varied.Conclusion: Our initial analysis revealed a largely positive response to the varied use of these devices that may serve to help combat residents' isolation and boredom in the longterm care setting and contribute to the resident's quality of life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".