Caregiver status affects medication adherence among older home care clients with heart failure.
Bibliographic record
Abstract
BACKGROUND AND AIMS: For older individuals living in the community with chronic diseases such as heart failure (HF), caregivers may play an important role in medication adherence. This role may be increasingly important as cognition declines. This study aimed to 1) examine the role of caregivers in medication adherence in a complex population of older home care clients with mild cognitive impairment (MCI) and 2) examine the effect of caregiver stress on medication non-adherence. METHODS: The interRAI Resident Assessment Instrument - Home Care (RAIHC) instrument collects comprehensive information about all individuals receiving long-term home care services in the Canadian province of Ontario. This analysis of secondary data utilized this database to examine the relationship between caregiver residence and stress on medication adherence among a subset of clients with MCI who were over age 75. RESULTS: The prevalence of HF among the sample was 15.5%, while MCI was present in 42.3% of the sample. Among individuals with MCI, having a caregiver at the same residence reduced medication non-adherence. Additionally, caregiver stress was significantly associated with higher rates of non-adherence. CONCLUSIONS: MCI can impair medication adherence. The presence of a caregiver at home significantly improves medication adherence in patients with HF and MCI. Supporting caregivers is an important strategy in allowing clinically complex older adults to remain safely at home.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".