Health service experiences and preferences of frail home care clients and their family and friend caregivers during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has brought about a major upheaval in the lives of older adults and their family/friend caregivers, including those utilizing home care services. In this article, we focus on results from a qualitative component added to a pragmatic randomized controlled trial that focuses on the experiences of our study participants during COVID-19. A total of 29 participants responded to the COVID-19 related questions focused on their health services experiences and preferences from March-June 2020 including 10 home care clients and 19 family/friend caregivers in the provinces of Ontario and Nova Scotia, Canada. RESULTS: Many participants were affected drastically by the elimination or reduction of access to services, highlighting the vulnerability of home care clients and their caregivers during COVID-19. This took an emotional toll on home care clients and increased the need for family/friend caregiver support. While many participants expressed reduced desire to utilize residential long-term care homes, some caregivers found that passive remote monitoring technology was particularly useful within the COVID-19 context. Our results provide important insights into the ways the older adults and their caregivers have been affected during the COVID-19 context and how to better support them in the future.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".