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Record W4390075841 · doi:10.35508/jhbs.v4i1.4799

Determinants of Hypertensive People Aged 20 to 44 Years Old

2022· article· en· W4390075841 on OpenAlexaff
Rahayu Chandranita Rini, Honey Ivone Ndoen, Deviarbi Sakke Tira

Bibliographic record

VenueJournal of Health and Behavioral Science · 2022
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsObesityBivariate analysisIncidence (geometry)MedicineConsumption (sociology)DemographyEnvironmental healthFamily historyUnivariate analysisMultivariate analysisGerontologyInternal medicineStatistics

Abstract

fetched live from OpenAlex

Hypertension is a multifactor disease that can be present in any age group or social-economic group and is due to the interaction of many factors. This study aims to analyze the relationship between family history, consumption of fruits and vegetables, consumption of sodium, consumption of fat, obesity, physical activity, and stress level with the incidence of 20-44 years old hypertension in Oesapa Community Health Center of Kupang City year 2020. This research is a case-control study. The sample consisted of 110 people case samples dan 110 people control samples. Sample selection of the case sample is by simple random sampling and control is by individual matching. The data analysis used was univariate analysis and bivariate analysis with chi-square. The result of the study showed that there is a relationship between family history (p=0,000), fruits and vegetable consumption (p=0,004), fat consumption (p=0,000), obesity (p=0,000), physical activity (p=0,000), and stress level (p=0,000) with the incidence of hypertension aged 20-44 years, while sodium consumption (p=1,000) variable has no relationship with the incidence of hypertension aged 20-44 years.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.394
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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