What is Important to Older People with Multimorbidity and Their Caregivers? Identifying Attributes of Person Centered Care from the User Perspective
Bibliographic record
Abstract
INTRODUCTION: Health systems are striving to design and deliver care that is 'person centred'-aligned with the needs and preferences of those receiving it; however, it is unclear what older people and their caregivers value in their care. This paper captures attributes of care that are important to older people and their caregivers. METHODS: This qualitative descriptive study entailed 1-1 interviews with older adults with multimorbidity receiving community based primary health care in Canada and New Zealand and caregivers. Data were analyzed to identify core attributes of care, important to participants. FINDINGS: Feeling heard, appreciated and comfortable; having someone to count on; easily accessing health and social care; knowing how to manage health and what to expect; feeling safe; and being independent were valued. Each attribute had several characteristics including: being treated like a friend; having contact information of a responsive provider; being accompanied to medical and social activities; being given clear treatment options including what to expect; having homes adapted to support limitations and having the opportunity to participate in meaningful hobbies. CONCLUSIONS: Attributes of good care extend beyond disease management. While our findings include activities that characterize these attributes, further research on implementation barriers and facilitators is required.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".