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Record W2955950576 · doi:10.33088/jmk.v9i2.307

HUBUNGAN AKTIVITAS FISIK DAN KUANTITAS TIDUR DENGAN KEJADIAN HIPERTENSI

2018· article· en· W2955950576 on OpenAlexaff
Gusnilawati Gusnilawati

Bibliographic record

VenueJURNAL MEDIA KESEHATAN · 2018
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)WiLAN (Canada)
Fundersnot available
KeywordsMedicineIncidence (geometry)Environmental healthUnivariate analysisObesityDyslipidemiaEmergency medicineInternal medicineMultivariate analysis

Abstract

fetched live from OpenAlex

Hypertension is not a contagious disease that is a cause of death in the world .Approximately 17.5 million people worldwide die from hypertension . An estimated 2025cases of hypertension will be 1.6 billion cases of hypertension . The cause of hypertension isdivided into two factors that can be controlled and uncontrolled . Controlled factors , amongothers, excessive salt intake , cholesterol , smoking , alcohol , physical activity , quantity ofsleep , lifestyle , stress , and obesity . This study was to determine the relationship of physicalactivity and quantity of sleep with the incidence of hypertension in Puskesmas SukamerinduBengkulu .This type of research is the use of cross -sectional and sampling methodsperformed with accidental sampling technique . The number of samples of this study were 97respondents and data collection was done by questionnaire interview . This study analyzesusing univariate and bivariate analysis with the Chi - Square test statistic where the significantlevel p = 0.05The results of the study demonstrate a significant association between physicalactivity with incidence of hypertension with p = 0:00 and a significant correlation with theincidence of sleep quantity with hypertension incidence with p = 0:00 .PHC nurses expectedfurther improve the quality of health services, especially in patients at risk for hypertensionand preventive improvement can be done by preventing the increase in hypertension .

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.309
Teacher spread0.274 · 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.

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
Published2018
Admission routes1
Has abstractyes

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