Factors associated with medication adherence among heart failure patients and their caregivers
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Reducing the rate of rehospitalization among heart failure patients is a major public health challenge; medication non-adherence is a crucial factor shown to trigger rehospitalizations. Objective: To collect pilot data to inform the design of educational interventions targeted to heart failure patients and their caregivers to improve medication adherence. METHODS: Heart failure patients with an implantable cardioverter defibrillator and their family caregivers were recruited from an outpatient electrophysiology clinic at an urban university medical center (N = 10 caregiver and patient dyads, 70% race/ethnic minority, mean patient age = 63 years). Quantitative and qualitative research methods were utilized. Semi-structured individual interviews were conducted to assess patients' and caregivers' individual interest in, and access to, new medication adherence technologies. Patient adherence to medications, medication self-efficacy, and depression were assessed by validated questionnaires. Medication adherence and hospitalization rates were assessed among patients at 30-days post-clinic visit by mailed survey. RESULTS: At baseline, 60% of patients reported sometimes forgetting to take their medications. The most common factors associated with non-adherence included forgetfulness (50%), having other medications to take (20%), and being symptom-free (20%). At 30-day follow-up, half of patients reported non-adherence to their medications, and 1 in 10 reported being hospitalized within the past month. Dyads reported widespread access to technology, with the majority of dyads showing interest in mobile applications and text messaging. There was less acceptance of medication-dispensing technologies; caregivers and patients were concerned about added burden. CONCLUSIONS: The majority of etiologies of medication non-adherence were subject to intervention. Enthusiasm from patients and caregivers in new technologies to aid in adherence was tempered by potential burden, and should be considered when designing interventions to promote adherence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it