Managing multiple chronic conditions in the community: a Canadian qualitative study of the experiences of older adults, family caregivers and healthcare providers
Bibliographic record
Abstract
BACKGROUND: The prevalence of multiple chronic conditions (MCC) among older persons is increasing worldwide and is associated with poor health status and high rates of healthcare utilization and costs. Current health and social services are not addressing the complex needs of this group or their family caregivers. A better understanding of the experience of MCC from multiple perspectives is needed to improve the approach to care for this vulnerable group. However, the experience of MCC has not been explored with a broad sample of community-living older adults, family caregivers and healthcare providers. The purpose of this study was to explore the experience of managing MCC in the community from the perspectives of older adults with MCC, family caregivers and healthcare providers working in a variety of settings. METHODS: Using Thorne's interpretive description approach, semi-structured interviews (n = 130) were conducted in two Canadian provinces with 41 community-living older adults (aged 65 years and older) with three or more chronic conditions, 47 family caregivers (aged 18 years and older), and 42 healthcare providers working in various community settings. Healthcare providers represented various disciplines and settings. Interview transcripts were analyzed using Thorne's interpretive description approach. RESULTS: Participants described the experience of managing MCC as: (a) overwhelming, draining and complicated, (b) organizing pills and appointments, (c) being split into pieces, (d) doing what the doctor says, (e) relying on family and friends, and (f) having difficulty getting outside help. These themes resonated with the emotional impact of MCC for all three groups of participants and the heavy reliance on family caregivers to support care in the home. CONCLUSIONS: The experience of managing MCC in the community was one of high complexity, where there was a large gap between the needs of older adults and caregivers and the ability of health and social care systems to meet those needs. Healthcare for MCC was experienced as piecemeal and fragmented with little focus on the person and family as a whole. These findings provide a foundation for the design of care processes to more optimally address the needs-service gap that is integral to the experience of managing MCC.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".