Toward improved homecare of frail older adults: A focus group study synthesizing patient and caregiver perspectives
Bibliographic record
Abstract
BACKGROUND: Adopting a better understanding of how both older adults and health care providers view the community management of frailty is necessary for improving home health, especially facing the coronavirus disease 2019 (COVID-19) pandemic. We conducted a qualitative focus group study to assess how both older adults and health care providers view frailty and virtual health care in home health. METHODS: Two focus groups enrolled home-living older adults and health care professionals, respectively (n = 15). Questions targeting the use of virtual / telehealth technologies in-home care for frail older adults were administered at audio-recorded group interviews. Transcribed discussions were coded and analyzed using NVivo software. RESULTS: The older adult group emphasized the autonomy related to increasing frailty and social isolation and the need for transparent dissemination of health care planning. They were optimistic about remote technology-based supports and suggested that telehealth / health-monitoring/tracking were in high demand. Health care professionals emphasized the importance of a holistic biopsychosocial approach to frailty management. They highlighted the need for standardized early assessment and management of frailty. CONCLUSIONS: The integrated perspectives provided an updated understanding of what older adults and practitioners value in home-living supports. This knowledge is helpful to advancing virtual home care, providing better care for frail individuals with complex health care needs.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".