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Record W4414337508 · doi:10.30737/jafi.v6i2.6523

Gambaran  Tingkat Pengetahuan Masyarakat Dalam Pengobatan Gastritis Di Dusun Pringgolayan Banguntapan Bantul

2025· article· en· W4414337508 on OpenAlexaboutno aff
Yustina Esha Maunia Chantika, Danang Yulianto

Bibliographic record

VenueJurnal Inovasi Farmasi Indonesia (JAFI) · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Satisfaction
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)Nonprobability samplingObservational studyGastritisPopulationPublic health

Abstract

fetched live from OpenAlex

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.430
Teacher spread0.371 · 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
Published2025
Admission routes1
Has abstractyes

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