Factors Influencing Medication Adherence in Elderly Patients with Hypertension: A Single Center Study in Western China
Bibliographic record
Abstract
Purpose: To develop and empirically test a conceptual model that explains the factors influencing antihypertensive medication adherence behavior in elderly patients in a city in western China. Patients and Methods: The conceptual model was based on the Theory of Planned Behavior and the Health Belief Model and was empirically tested using cross-sectional survey data from Nanchong City, a city in western China, collected between October and December 2020. Data were analyzed using structural equation modeling. Results: Behavioral intentions were the main predictor of medication adherence behavior (path coefficient of 0.353). Perceived benefits and perceived barriers directly (path coefficient = 0.201 and -0.150, respectively), and indirectly (path coefficient = 0.118 and -0.060) through behavioral intentions, influenced medication adherence behavior. Perceived susceptibility (path coefficient = 0.390) and perceived severity (path coefficient = 0.408) influenced behavioral attitudes, which influenced behavioral intentions (path coefficient = 0.298). Conclusion: The conceptual model demonstrates a robust ability to predict and explain medication adherence behavior among elderly patients with hypertension, facilitating the adoption and maintenance of changes in adherence behavior and the potential for preventing disease progression and improving quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".