HEALTHCARE PROVIDERS’ EXPERIENCES IN CARING FOR OLDER ADULTS WITH MULTIPLE CHRONIC CONDITIONS
Bibliographic record
Abstract
Abstract The management of multiple chronic conditions (MCC) in older adults living in the community is complex. Little is known about the experiences of interdisciplinary primary care and home providers who care for this vulnerable group. The aim of this study was to explore the experiences of healthcare providers in managing the care of community-living older adults with MCC and to highlight their recommendations for improving care delivery for this group. A qualitative interpretive description design was used. A total of 42 healthcare providers from two provinces in Canada participated in semi-structured interviews. Participants represented diverse disciplines (e.g., physicians, nurses, social workers, personal support workers) and settings (e.g., primary care and home care). Thematic analysis was used to analyze interview data. The experiences of healthcare providers managing care for older adults with MCC were organized into six major themes: (1) managing complexity associated with MCC, (2) implementing person-centred care, (3), involving and supporting family caregivers, (4) using a team approach for holistic care delivery, (5) encountering rewards and challenges in caring for older adults with MCC, and (6) recommending ways to address the challenges of the healthcare system. Healthcare providers highlighted the need for a more comprehensive integrated system of care to improve care management for older adults with MCC and their family caregivers. Specifically, they suggested increased care coordination, more comprehensive primary care visits with an interprofessional team, and increased home care support.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".