Factors associated with nonadherence to antihypertensive medication among middle-aged adults with hypertension: findings from the Taiwan National Health Interview Survey
Bibliographic record
Abstract
Objective We aimed to investigate factors associated with nonadherence to antihypertensive medication among middle-aged Taiwanese adults with hypertension. Methods We used data from the 2009 Taiwan National Health Interview Survey (NHIS) to identify adults age 40 to 65 years with hypertension. We used logistic regression analyses to investigate factors associated with nonadherence to antihypertensive medication. Results A total 1,256 respondents with hypertension taking antihypertensive medication were included in this study. Multiple logistic regression analyses revealed that six factors were significantly and independently associated with nonadherence to medication: younger age (odds ratio, [OR] = 1.85), mean monthly personal income < TWD 20,000 (USD 660) (OR = 1.87), outpatient medical services use in the past month (OR = 0.57), hospitalization in the past year (OR = 1.70), diabetes or dyslipidemia (OR = 0.63), and alcohol use in the past month (OR = 2.38). Conclusions This secondary data analysis of the population-based NHIS identified six factors associated with nonadherence to antihypertensive medication. These factors should be considered when planning and implementing blood pressure control interventions among middle-aged adults with hypertension.
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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 | 0.003 |
| 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) | 0.001 | 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".