Strategies to improve medication adherence in adult patients with hypertension in primary care
Bibliographic record
Abstract
Background: Hypertension affects 1.28 billion individuals globally and is one of the key public health issues in Canada impacting nearly one in four adults. Non-adherence to antihypertensive medications is a major contributor to uncontrolled hypertension and related complications. Objective: To examine the strategies that can be used by primary care providers to improve medication adherence in adult patients with hypertension. Methods: An integrative literature review approach (Toronto & Remington, 2020) was used along with the PRISMA guidelines (2021). A systematic search of CINAHL and Ovid MEDLINE was undertaken. A critical appraisal was conducted using two tools. Results: Six studies met the inclusion criteria. Study designs included randomized controlled trials, observational cohorts, and mixed-methods studies from developed countries. Three key strategies were identified to improve medication adherence in adults with hypertension within primary care settings: patient-centered interventions (e.g., education tools, reminders, self-monitoring); collaborative care strategies involving pharmacists and nurses; and fixed-dose combination therapies. Conclusion: Findings indicate that while several single strategies can support medication adherence in adults with hypertension, primary care providers should use a combination of strategies to achieve the most effective improvements in medication adherence and clinical outcomes.,
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".