Prevention of Hypertension in Bangladesh: A Review
Bibliographic record
Abstract
Background: Hypertension is a major public health problem in both the developed and developing countries and the leading cause of morbidity and mortality globally. The risk factors for hypertension, which can largely be prevented through simple health promotion and preventive measures, are mostly known. However, evidence on strategies for prevention of hypertension in Bangladesh is not available. The aim of this review study was to identify and discuss different approaches to prevent hypertension in Bangladesh. Methods: We performed a systematic search using electronic as well as manual methods for published and unpublished reports of prevention of hypertension. We then identified and discussed prevention strategies for hypertension suitable for Bangladesh context. Results: Several methods have shown to prevent hypertension. However, the challenge remains in implementing these methods in resource poor settings. Integrated action based on comprehensive policy and stepwise implementation should be adopted taking into consideration of local needs. Hypertension prevention should focus on awareness generation, health promotion and reduction of common risk factors using a combination of population based approach and targeted individual interventions. Conclusion: Consorted actions should be taken as a priority to prevent hypertension through intersectoral, multidisciplinary and multilevel approach by the Government and stakeholders for creating greater awareness and healthy life-style. DOI: http://dx.doi.org/10.3329/cardio.v7i2.22262 Cardiovasc. j. 2015; 7(2): 137-144
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".