HUBUNGAN AKTIVITAS FISIK DAN KUANTITAS TIDUR DENGAN KEJADIAN HIPERTENSI
Bibliographic record
Abstract
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 .
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".