Interventions to Improve Medication Adherence in Ethnically Diverse Patients: A Narrative Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Ethnically diverse patients often have lower medication adherence relative to Whites. Certain ethnic groups are also more susceptible to cardiovascular and related diseases. It is critical to develop culturally tailored interventions to improve medication adherence in these ethnically diverse patients. Thus, the aim of this systematic review was to identify what interventions have been developed and tested to improve medication adherence in ethnically diverse patients with cardiovascular-related diseases. METHOD: A systematic search of peer-reviewed literature (MEDLINE, Cumulative Index to Nursing and Allied Health Literature, EMBASE, and Cochrane Central Register of Controlled Trials) was conducted to identify relevant articles. The narrative synthesis was performed based on elements offered by Popay et al. The mixed methods appraisal tool was used to appraise the quality of the included studies. RESULTS: A total of 11,294 records were retrieved, and 34 articles met the inclusion criteria for this systematic review. Synthesis of the literature revealed four overarching intervention strategies used to improve medication adherence: pharmacist-mediated, primarily nurse-led, community-based and community-health worker led, and text-message and phone-based. DISCUSSION: Several approaches can be used to improve medication adherence in ethnically diverse patients, although details on the approaches and conditions to produce optimal improvements for particular ethnic groups need to be determined in future studies. How does this affect culturally congruent health care?
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".