Bibliographic record
Abstract
Based on integrated surveillance data of Desa Teluk Health Center in 2019, people with hypertension in the working area of The Teluk Village Health Center are most common in elderly communities.Consumption of foods containing proteins, vitamins, and minerals for the elderly needs to be improved.This research is an analytical study with cross sectional approach conducted in three villages, namely Teluk village, Telaga Jernih village and Suka Mulia village which is the working area of Teluk Village Health Center in March 2021.The sample of this study was elderly people who suffered from hypertension as many as 45 respondents were collected using purposive sampling techniques using data collection tools in the form of STEPWise WHO questionnaire instruments consisting of questions about the characteristics of the respondents and the consumption of fruit and vegetables of the respondents.The majority of respondents were women (75.6%), the most age group were the late and senior (37.8%),une schools (44.4%) and the most occupations were housewives (46.7%).There was no correlation fruit consumption with hypertension with p = 1,000 (p>) and no correlation vegetable consumption and hypertension with a value of p = 0.567 (p>).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".