Using community-based participatory research in improving the management of hypertension in communities: A scoping review
Bibliographic record
Abstract
BACKGROUND: Hypertension (HT) is a key contributor to cardiovascular diseases (CVDs). The improved management of HT in the community and primary care settings should be a priority for low- and middle-income countries (LMICs). Improving the prevention and management of HT in primary care settings should also be a priority for developing countries. There is a need for more studies using community-based approaches that show the impact of these programmes on HT outcomes, which may motivate policymakers to invest in such approaches. The ward-based outreach team or village healthcare worker models were meant to provide such approaches, but many of these have become lower levels of curative care. We conducted a scoping review to examine how community-based participatory research (CBPR) was being used to improve HT management. METHODS: Several electronic databases were searched, namely PubMed, MEDLINE, Google Scholar and Web of Science, generating 798 references. The publications were screened through several rounds. Data were extracted and imported into a Microsoft Excel spreadsheet, numerically summarised and qualitatively analysed. RESULTS: Nine articles were included. These publications originated from the United States, Colombia, Canada, China, South Africa and Zimbabwe. Mixed methods, qualitative, randomised control trials and quasi-experimental studies were used to implement CBPR in the studies included. All the studies addressed complex health problems and inequities among the minorities utilising multiple stakeholder participation. Academic-community coalitions were formed, which enabled engagement and sharing of power equitably. As a result, there was acceptability and sustainability of interventions. CONCLUSION: A CBPR framework can be used to define the context, group dynamics, implementation and outcomes of HT. It is possible to apply CBPR in HT management to appropriately address health disparities while emphasising a community-driven approach. To achieve this, tailored health education platforms should be developed and implemented.
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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 | 0.070 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".