Determinants of the medication adherence behavior among elderly patients with coronary heart diseases
Bibliographic record
Abstract
Background : Coronary heart disease (CHD) is one of the most common chronic diseases among elders, and lifelong medication is necessary for them to control the disease. More than 60% of elders fail to adhere to their medication regimen. The purpose of this study was to explore the determinants of medication adherence behavior (MAB) of elderly with CHD . Methods : A cross-sectional correlation design was used. Convenience sampling was used to enroll subjects from the outpatient department of a cardiovascular clinic in a medical center in southern Taiwan. The study consecutively recruited 241 patients over 65 years old with CHD under medication for over one year and expected to take medication for life. A structured questionnaire and face to face interview was applied for data collection. The questionnaire which was developed by the researchers included a demographic sheet and five scales to measure perceived effects, perceived partnership, perceived reality, interpersonal influence, and medication-taking behaviors. Results : Only perceived effects and interpersonal influence remained in the predicting model and both factors accounted for 17% variance of the MABs. Conclusions : How the elderly perceive the effect of the medication and how they are influenced by others are two important determinants for medication adherence in elderly with CHD. Also, Clinical nurses can play a key role in the process of educating elderly patients so that they can achieve regular medication adherence.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".