The complexity of caregiving for community-living older adults with multiple chronic conditions: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Older adults with multiple chronic conditions (MCC) rely heavily on caregivers for assistance with care. However, we know little about their psychosocial experiences and their needs for support in managing MCC. The purpose of this study was to explore the experiences of caregivers of older adults living in the community with MCC. METHODS: This qualitative study was a secondary analysis of previously collected data from caregivers in Ontario and Alberta, Canada. Participants included caregivers of older adults (65 years and older) with three or more chronic conditions. Data were collected through in-depth, semi-structured interviews. Interview transcripts were coded and analyzed using Thorne's interpretive description approach. RESULTS: Most of the 47 caregiver participants were female (76.6%), aged 65 years of age or older (61.7%), married (87.2%) and were spouses to the care recipient (68.1%). Caregivers' experiences of caring for community-living older adults with MCC were complex and included: (a) dealing with the demands of caregiving; (b) prioritizing chronic conditions; (c) living with my own health limitations; (d) feeling socially isolated and constrained; (e) remaining committed to caring; and (f) reaping the rewards of caregiving. CONCLUSIONS: Healthcare providers can play key roles in supporting caregivers of older adults with MCC by providing education and support on managing MCC, actively engaging them in goal setting and care planning, and linking them to appropriate community health and social support services. Communities can create environments that support caregivers in areas such as social participation, social inclusion, and community support and health services.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".