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Record W2535461571 · doi:10.3329/jom.v17i1.30056

Prevention of Hypertension in Bangladesh: A Review

2016· review· en· W2535461571 on OpenAlexaff
Tuhin Biswas, Sheikh Mohammed Shariful Islam, Anwar Islam

Bibliographic record

VenueJournal of Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsYork University
Fundersnot available
KeywordsMedicinePsychological interventionContext (archaeology)Health promotionGovernment (linguistics)Public healthPopulationPromotion (chess)Multidisciplinary approachStroke (engine)Intensive care medicineEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Hypertension is a major public health problemglobally in both the developed and developing countries. Hypertension leads to cardiovascular diseases, stroke, kidney failure and is the leading cause of mortality and morbidity globally. The risk factors for hypertension, which can mostly be prevented through simple health promotion and preventive measures are mostly known. However, papers documenting the 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. We performed a systematic search using electronic as well as manual method for published and unpublished reports of prevention of hypertension. We then identified and discussed prevention strategies for hypertension suitable for Bangladesh context. Although several methods have shown to prevent hypertension, 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. Consorted actions should be taken as a priority to prevent hypertension through intersectoral, multidisciplinary and multilevel approach by the Government, Non-Government Organizations (NGOs), civil societies and create greater awareness among the population for a healthy life-style.J MEDICINE January 2016; 17 (1) : 30-35

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.824
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.385
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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