Home Blood Pressure Management Intervention in Low- to Middle-Income Countries: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Control of hypertension in low- and middle-income countries (LMICs) is poor, often less than 10%. A strong body of evidence demonstrates that home blood pressure management lowers blood pressure, and recent guidelines from the National Institute for Clinical Health and Excellence recommends home blood pressure monitoring. However, the preponderance of data on the benefits of home blood pressure management comes from studies in high-income countries. OBJECTIVE: The objective of the study is to examine whether an intervention of home blood pressure management is feasible in LMICs. Home blood pressure management is defined as self-monitoring of blood pressure and self-titration of antihypertensive medications. We will identify barriers and facilitators of home blood pressure management and explore unique contextual factors in LMICs that influence implementation of home blood pressure management. METHODS: Participants will be recruited from 6 sites from 2015 to 2018. Patients and health care workers will be included. We will use mixed methods including focus groups, interviews, and standardized checklists. When possible, we will adapt materials from prior successful studies so that they are culturally and contextually appropriate. RESULTS: This ongoing study is funded by the World Heart Federation. The information that is obtained will be used to develop a randomized clinical trial of home blood pressure management in LMICs. CONCLUSIONS: The data generated from this qualitative study will provide much needed information from patients and health care workers about barriers and facilitators of home blood pressure management and unique contextual factors that might influence implementation of home blood pressure management in LMICs.
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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 | 0.056 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.079 | 0.015 |
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".