Effect Foot Soak Using Warm Water Mixed with Salt and Lemongrass to Decrease Pressure in Hypertension Patients in the Podorejo Ngaliyan
Bibliographic record
Abstract
The incidence of hypertension in the region Podorejo RW 8 Ngalliyan in 2014 as many as 85 people, increase in 2015 as many as 110 people from stage I – III, the researchers are interested to doing therapy using warm water with a mixture of salt and lemon grass to lower blood pressure. The purpose of this study was to determine the effect between soaking feet using warm water with a mixture of salt and cymbopogon nardus l. rendle with reduction of blood pressure in patients with hypertension in region Podorejo RW 8 Ngaliyan. Quantitative research manifold quasi experiment with design Time Series Design Without Control. Sampling technique used Purposive Sampling counted 80 people. Data obtained by statistical test using Wilcoxon test. Based on Wilcoxon test showed Z = -8,127 (sistolik) Z = -5,587 (diastolik) and ρ value = 0,000 with α = 0,05. Where ρ value 0,000 < 0,05, so that H0 rejected Ha be accepted. There is any any effect of soaking feet using warm water with a mixture of salt and lemon grass toward reduction of blood pressure in patients with hypertension in region RW 8 Podorejo Ngaliyan. Nurses are expected to be used as a therapy to help people with hypertension to lower blood pressure.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".