Quality of Care for Patients with Hypertension in selected Health Centres in Rwamagana District, Rwanda
Bibliographic record
Abstract
Background: Hypertension is the main risk factor for cardiovascular diseases and its prevalence is high in Rwanda. Rwanda has integrated the management of hypertension in health centres (HCs). However, little is known about the quality of hypertension care in HCs in Rwanda. Study objective: To examine the quality of care for patients with hypertension and associated outcome of hypertension control in Health Centres. Methods: A cross-sectional study design was used, and data were collected from a convenience sample of 202 patients. A self-reported questionnaire and blood pressure measurement were taken. Data were analysed using descriptive, bivariate, and hierarchical logistic regression analyses. Results: A total of 166 (82.2%) patients participated in the study. Of these, 130 (78.3%) were females. Mean age was 57.8 (SD =14.0). The quality of hypertension care process was high with mean score of 5.86 over 7 (SD = 1.4). However, only 30.1% (n = 50/166) had well-controlled hypertension. Comorbidity (OR = 2.3; 95% CI:1.0-5.1, p =.039) and the quality of care (OR = 1.6; 95% CI: 1.1- 2.4, p = .024) were associated with higher odds of having hypertension control. Conclusion: Tailored patient-centred primary care interventions that consider comorbidity could contribute to hypertension control in primary HCs in Rwanda.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".