The process of medication management for older adults with dementia
Bibliographic record
Abstract
kaasalainen s, dolovich l, papaioannou a, holbrook a, lau e, ploeg j, levine m, cosby j & emily a (2011) Journal of Nursing and Healthcare of Chronic Illness 3, 407–418 The process of medication management for older adults with dementia Aim. The purpose of this study was to explore the personal experiences related to medication management of community-dwelling older adults diagnosed with dementia, their informal caregivers, as well as healthcare professionals who assist them. Background. Older adults who have dementia face many challenges in managing their medications while living in the community. Medication regimens used to treat a variety of conditions in older adults with dementia are usually overseen and coordinated by healthcare professionals such as community nurses, physicians and pharmacists, but often, more supports are needed. However, little research has been conducted to study the barriers and facilitators to medication adherence in this unique population. Methods. Using a grounded theory approach, 57 interviews were completed (10 nurses, 10 pharmacists, 6 physicians, 20 caregivers and 11 patients) in southern Ontario, Canada, in 2007. Findings. The findings indicate that the processes of medication management differ according to the level of dementia. A number of corresponding facilitators and barriers to medication management were identified. Medication management in early stage dementia is characterised by patients’ desire to maintain independence, denial of issues or disease, and a refusal to take medications owing to feeling angry. In late-stage dementia, older adults often refuse medications owing to delusional or suspicious thinking, which results in caregivers assuming responsibility for managing their medications. Conclusions. Older adults with dementia, their informal caregivers and the healthcare professionals who assist them are faced with the challenges of declining cognitive function and memory while trying to manage medications at home. However, a number of adherence strategies appear to be helpful and should be considered. Relevance to clinical practice. Healthcare professionals struggle with helping older adults who have dementia manage medications safely and therapeutically, often with little resources to draw from. Future work is needed to design, implement and evaluate supportive networks and interventions with the goal of helping older adults with dementia manage their medications better while living in the community.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".