Predictors of refill non‐adherence in patients with heart failure
Bibliographic record
Abstract
What is already known about this subject • Non‐adherence to recommended treatment is common in patients with heart failure and is associated with poor outcomes. • Personal beliefs as well as experiences with medications and illness could influence medication use. What this study adds • Perception regarding barrier to medication use was a stronger predictor of non‐adherence than demographic or clinical variables. • Patients who were non‐adherent to nonpharmacological management of heart failure were more likely to be non‐adherent to their medications. • Regimen complexity should not be considered in isolation when strategies for addressing adherence issues are designed. Aim To identify the health beliefs and patient characteristics associated with medication non‐adherence in patients attending a heart failure outpatient clinic. Methods A survey was administered to 350 consenting clinic patients. Questions focused on relevant demographic and clinical characteristics, the Health Belief Model, the Beliefs About Medicines Questionnaire and the Multidimensional Health Locus of Control. Multivariate logistic regression was used to identify independent predictors of refill non‐adherence (<90%). Results Refill non‐adherence was found in 77 (22%) participants. Being a smoker [odds ratio (OR) 2.4, 95% confidence interval (CI) 1.0, 5.8, P = 0.045], two or fewer medication administration times (OR 2.4, 95% CI 1.2, 4.6, P = 0.01), and positive response to ‘Have you changed your daily routine to accommodate your heart failure medication schedule’ (OR 2.4, 95% CI 1.2, 4.5, P = 0.01) were the independent predictors of refill non‐adherence. Conclusion Perceptions regarding barriers to medication taking and fewer administration times could result in medication non‐adherence in congestive heart failure patients. Medication regimens should be designed after accounting for patients' existing routines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".