Gambaran Tingkat Pengetahuan Masyarakat Dalam Pengobatan Gastritis Di Dusun Pringgolayan Banguntapan Bantul
Bibliographic record
Abstract
According to the World Health Organization (WHO) in 2020, the largest gastritis sufferers occurred in Canada as much as 35%, China 31%, France 29.5%, Japan 14.5% while Indonesia reached 40.8%. Patients with gastritis in Indonesia reached 274,396 patients out of 238,452,952 population. The increase in incidence is influenced by a lifestyle that pays less attention to health. The majority of public knowledge about gastritis treatment is still in the poor category. This study aims to determine the description of the level of public knowledge in the treatment of gastritis in Pringgolayan Hamlet Banguntapan Bantul. The research method used descriptive observational with sampling using purposive sampling with a population of 2,931 people and a sample of 100 respondents. The data observed included the level of knowledge in the treatment of gastritis in Pringgolayan Hamlet. The data were then analyzed descriptively with the level of knowledge of good, sufficient, and less. The results of the study on the description of the level of knowledge of the community in the treatment of gastritis showed that the level of knowledge of respondents in the good category 51 respondents (51%), sufficient knowledge 44 respondents (44%), and less knowledge 5 respondents (5%). Based on the research that has been carried out, it can be concluded that the majority of people's knowledge level in the treatment of gastritis is good.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".