Elder and caregiver solutions to improve medication adherence
Bibliographic record
Abstract
Medication mismanagement is a growing public health concern, especially among elders. Annually, it is a major contributor to emergency hospitalization and nursing home placement. Elders and their caregivers, as healthcare consumers and stakeholders in this issue, are uniquely qualified to inform strategies to improve medication adherence. We conducted a qualitative study to ascertain caregiver and elder perceptions of barriers to medication management and to identify community-derived solutions to improve medication management. Nine focus groups (N = 65, mean age = 71) were conducted with caregivers or elders from five communities. Participants were recruited by key informants utilizing snowball sampling methodology. The following themes were identified in the participant-recommended proposed solutions improving medication adherence: (i) use of personal systems to overcome barriers to medication adherence, (ii) various solutions to address cost concerns, (iii) the need for regular review of medications by doctors or pharmacists to eliminate unnecessary medications, (iv) desire for community-driven support systems, and (v) using medical advocates. Elders and caregivers recognized medication non-adherence as a community-wide issue and were eager to offer solutions they thought would work in their communities. These solutions can lend credibility to strategies currently being developed/utilized and offer innovative recommendations for future interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".