Medication Adherence among Rural, Low-Income Hypertensive Adults: A Randomized Trial of a Multimedia Community-Based Intervention
Bibliographic record
Abstract
PURPOSE: Examine the effectiveness of a community-based, multimedia intervention on medication adherence among hypertensive adults. DESIGN: Randomized controlled trial. SETTING: Rural south Alabama. SUBJECTS: Low-income adults (N = 434) receiving medication at no charge from a public health department or a Federally Qualified Health Center. INTERVENTION: Both interventions were home-based and delivered via computer by a community health advisor. The adherence promotion (AP) intervention focused on theoretical variables related to adherence (e.g., barriers, decisional balance, and role models). The cancer control condition received general cancer information. MEASURES: Adherence was assessed by pill count. Other adherence-related variables, including barriers, self-efficacy, depression, and sociodemographic variables, were collected via a telephone survey. ANALYSIS: Chi-square analysis tested the hypothesis that a greater proportion of participants in the AP intervention are ≥80% adherent compared to the control group. General linear modeling examined adherence as a continuous variable. RESULTS: Participants receiving the intervention did not differ from individuals in the control group (51% vs. 49% adherent, respectively; p = .67). Clinic type predicted adherence (p < .0001), as did forgetting to take medications (p = .01) and difficulty getting to the clinic to obtain medications (p < .001). CONCLUSIONS: Multilevel interventions that focus on individual behavior and community-level targets (e.g., how health care is accessed and delivered) may be needed to improve medication adherence among low-income rural residents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".