Healthcare providers’ experiences in supporting community-living older adults to manage multiple chronic conditions: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Living with multiple chronic conditions (MCC), the coexistence of two or more chronic conditions, is becoming more prevalent as the population ages. Primary care and home care providers play key roles in caring for older adults with MCC such as facilitating complex care decisions, shared decision-making, and access to community health and support services. While there is some research on the perceptions and experiences of these providers in caring for this population, much of this literature is focused specifically on family physicians. Little is known about the experiences of other primary care and home care providers from multiple disciplines who care for this vulnerable group. The purpose of this study was to explore the experiences of primary and home care healthcare providers in supporting the care of older adults with MCC living in the community, and identify ways of improving care delivery and outcomes for this group. METHODS: The study used an interpretive descriptive design. A total of 42 healthcare providers from two provinces in Canada (Ontario and Alberta) participated in individual semi-structured, face-to-face 60-min interviews. Participants represented diverse disciplines from primary care and home care settings. Inductive thematic analysis was used for data analysis. RESULTS: The experiences and recommendations 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), supporting caregivers, (4) using a team approach for holistic care delivery, (5) encountering challenges and rewards, and (6) recommending ways to address the challenges of the healthcare system. Healthcare providers identified the need for a more comprehensive, integrated system of care to improve the delivery of care and outcomes for older adults with MCC and their family caregivers. CONCLUSIONS: Study findings suggest that community-based healthcare providers are using many relevant and appropriate strategies to support older adults living with the complexity of MCC, such as implementing person-centred care, supporting caregivers, working collaboratively with other providers, and addressing social determinants of health. However, they also identified the need for a more comprehensive, integrated system of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".