HOME AND COMMUNITY HEALTHCARE FOR FRAIL OLDER ADULTS: A PATIENT, CAREGIVER, AND PRACTITIONER PERSPECTIVE
Bibliographic record
Abstract
Abstract Frailty and the decline in ability to maintain independent living may be forestalled through discussions with healthcare providers and seniors about managing health at home. In addition, the use of technology in supplementing doctors’ visits to assess frailty progression may be easily adopted by some but not others. We conducted this qualitative study to elucidate the context in which seniors access care at home and current barriers to independent living, from the perspectives of both seniors and practitioners. Pre-approved discussion questions were administered to two audio-recorded focus group sessions of 14 participants. The first group were community-dwelling older adults and informal caregivers, while the second consisted of healthcare practitioners. Group members were sampled to include a range of health backgrounds and levels of technological expertise. Thematic analysis with NVivo Software was used to parse out key discussion topics from the audio transcripts. The caregiver/patient group emphasized the stigma of frailty and age-related isolation, desiring transparency and advocacy from care teams. Practitioners/researchers discussed the importance of a holistic biopsychosocial approach to frailty management and the need for standardized frailty measurement. Patients/caregivers used health-tracking devices at home and were more optimistic about telehealth/video-conferencing than practitioners. Awareness of contextual factors surrounding “aging in place” and what aspects of care are valued by patients and practitioners is key to advancing home health and paving the way for new evidence-based services in the home.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".