Adherence and Knowledge among Geriatric Cardiac Patients
Bibliographic record
Abstract
BACKGROUND: Non-adherence to medication is a public health problem that affects every age group, but geriatric patients are particularly at risk because they face a high likelihood of disease, cognitive decline, and polypharmacy. AIM: In this study, we evaluated the knowledge of prescribed medication and adherence to medication regimens among geriatric cardiac patients in Saudi Arabia. METHODS: We interviewed 750 geriatric patients at the cardiac center of King Fahad Medical City. We assessed their knowledge about their medications using the Medication Knowledge Assessment questionnaire and a validated Arabic version of The Medication Adherence Report Scale (MARS‐5). We analyzed the relationship between their knowledge of medication and their adherence to it. We assessed how well these patients understood the information given to them by their healthcare providers. RESULTS: The estimated mean rate of adherence to long-term medication regimens was 56%. There was a positive relationship between knowledge and adherence. CONCLUSION: Geriatric patients in Saudi Arabia have low overall adherence to medication regimens, and patients with higher knowledge levels are more adherent. Geriatric patients should receive detailed counseling from their care providers to ensure better adherence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".