Role of the Health Belief Model in the Management of Hypertension: A Systematic Review
Bibliographic record
Abstract
Theory-driven behavioral models such as the health belief model (HBM) offer predictive and influential insights into hypertension management. This study aims to explore the role of the health belief model in hypertension management, with a focus on blood pressure control, medication adherence, and self-management. This review included English full-text quantitative studies on HBM and hypertension management in low- and middle-income countries (LMICs), excluding qualitative, mixed methods, protocols, and nonoriginal data. The review was registered in PROSPERO (PROSPERO 2023 CRD42023467247) and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was carried out across six electronic databases, PubMed, APA PsycINFO, CINAHL, Scopus, Embase, and the Cochrane Library, between September 26 and October 2, 2023, to identify relevant published studies. The risk of bias was assessed via the RoB-2 (The Risk of Bias 2) and JBI (Joanna Briggs Institute) Critical Appraisal Tools. Data were extracted into an Excel data sheet (Microsoft Corporation, Redmond, Washington) for result synthesis and tabulation. An initial total of 1,064 articles were identified for review. Following the removal of duplicates and a full-text assessment, 24 articles with a sample size of 6,106 met the inclusion criteria. The application of interventions based on the HBM constructs was associated with reduced blood pressure, improved medication adherence, and self-management. Most studies have shown that perceived susceptibility, severity, and self-efficacy are positively associated with BP reduction, whereas perceived barriers have a negative impact on adherence. Perceived susceptibility and self-efficacy are also frequently linked to better self-management. The HBM has the potential to predict health behaviors among individuals with hypertension. Interventions based on the HBM offer potential for effective hypertension control.
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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 | 0.023 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".