Development of cardiovascular health education program for primary care patients with hypertension in rural Nigeria: a qualitative study
Bibliographic record
Abstract
Background: Patient-centered, culturally tailored cardiovascular health education has the potential to improve hypertension self-management. Despite the high prevalence of hypertension in Sub Sahara Africa, this type of health education is hardly available in this region. Objective: To describe how we developed and evaluated a culturally adapted Cardiovascular Health Education Program for insured hypertensive patients in rural Nigeria. Methods: Applying concepts of “cultural adaptation”, we took a hypertension education program from Europe as a starting point for program-development. First, we collected information on socio-cultural perspectives on hypertension care through a literature review and qualitative interviews with 40 hypertensive patients and 15 healthcare professionals/insurance managers in Kwara State Nigeria. Second, we used this information to adapt the content (deep structure) and the form (surface structure) of the European program to the unique patient population and circumstances of a primary care clinic in Kwara. Third, we evaluated the adapted program among 149 hypertensive patients from this clinic. Results: The interviews offered insight into patient perspectives on hypertension, socio-cultural and environmental inhibitors and facilitators for medication/ behavioral self-management (e.g. exercise) and on healthcare professional perspectives on optimal education delivery platforms – group counseling, posters, audiovisuals. These insights were used to adapt elements (e.g., educational tools, content) of the existing educational program. The adapted program has been shown to strengthen medication adherence and consequently blood pressure control among the targeted population. Conclusion: A culturally tailored Cardiovascular Health Education Program, developed by using a qualitative research approach, offered an effective means for educating patients about blood pressure control in a rural primary care setting in Africa. Implications: Our description of the program and the process by which it was developed offers a practical framework for developing cardiovascular health education for other patient populations in Africa.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".