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Record W4411592739 · doi:10.24246/jms.v5i22024p192-201

Jus “Mesem” (Mentimun, Seledri, Lemon, Madu) Sebagai Pencegahan Hipertensi

2025· article· id· W4411592739 on OpenAlexaff
Elisa Esa Naftalina, Rosiana Eva Rayanti, Indah Setyawati, Bethania Clara Marpaung, Jonathan Sandy Pratama, Matan Mirin, Simeon Apintamon

Bibliographic record

VenueMagistrorum et Scholarium Jurnal Pengabdian Masyarakat · 2025
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsMedicineTraditional medicineHorticultureBiology

Abstract

fetched live from OpenAlex

Hipertensi merupakan kondisi ketika tekanan darah sistolik mencapai atau lebih dari 140 mmHg dan/atau tekanan darah diastolik mencapai atau lebih dari 90 mmHg saat diukur di fasilitas pelayanan kesehatan. Pada tahun 2021, tercatat sebanyak 69.247 namun, kasus hipertensi di Kota Salatiga hanya 20.310 kasus yang mendapat pelayanan kesehatan. Pengabdian kepada masyarakat ini bertujuan untuk memberikan promosi kesehatan kepada masyarakat terkait hipertensi dan cara mencegah hipertensi dengan jus Mesem. Jus Mesem dibuat dari 4 bahan yaitu mentimun, seledri, lemon dan madu. Melalui kuesioner yang dibagikan kepada peserta promosi kesehatan, ditemukan bahwa 39 dari 40 (97.5%) responden menyetujui bahwa kegiatan promosi kesehatan hipertensi dan demonstrasi pembuatan jus Mesem ini meningkatkan pengetahuan mereka terkait hipertensi dan meningkatkan minat mereka untuk mencegah hipertensi dengan membuat jus Mesem di rumah. Dengan adanya promosi kesehatan ini, masyarakat akan mampu untuk mencegah hipertensi dengan cara yang mudah dan dapat dimulai dari keluarga masing-masing.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.003

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.

Opus teacher head0.016
GPT teacher head0.322
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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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