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Record W4389225548 · doi:10.36973/jkih.v10i1.339

PENGEMBANGAN ASUHAN KOMUNITAS PADA LANSIA DENGAN PENDEKATAN KOMPLEMENTER PENGGUNAAN REBUSAN DAUN ALPUKAT DALAM PENURUNAN HIPERTENSI

2022· article· id· W4389225548 on OpenAlexaff
Meda Yuliani, Iceu Mulyati, Yuni Mauliany, Susilawati Susilawati, Nabila Berliana, Amirah Salwa, Lika Nurfiati

Bibliographic record

VenueJURNAL KESEHATAN INDRA HUSADA · 2022
Typearticle
Languageid
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsEncana (Canada)WiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Tekanan darah tinggi kronis (hipertensi) adalah penyakit yang umum di kalangan lansia. Salah satu penyebab kematian yang paling umum adalah hipertensi. Hipertensi merupakan salah satu kesulitan pada lansia karena menimbulkan dan merupakan faktor gagal jantung dan penyakit jantung koroner. Baik metode farmakologis dan non-farmakologis dapat digunakan untuk mengobati hipertensi. Rebusan daun alpukat non-farmakologis telah digunakan dalam penelitian selama bertahun-tahun dan sudah tersedia. Dalam asuhan ini, rebusan daun alpukat digunakan sebagai pengobatan tambahan untuk lansia dengan hipertensi.
 Untuk memulai, proses evaluasi, pemeriksaan, dan pemberian perawatan komplementer digunakan bersama dengan pendekatan studi kasus dengan desain deskriptif.
 Terdapat penurunan tekanan darah pada lansia setelah intervensi rebusan daun alpukat dibandingkan dengan rata-rata sebelum intervensi 171/101mmHg dan rata-rata pasca intervensi 148/89mmHg. Penurunan rata-rata adalah 23/12 mmHg. Rebusan daun alpukat membantu menurunkan tekanan darah.
 Rebusan daun alpukat ini diharapkan dapat digunakan sebagai antihipertensi dan sebagai pengobatan sendiri untuk menurunkan tekanan darah melalui penelitian selanjutnya pada rebusan daun alpukat ini di bidang medis.
 

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0990.002

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 head0.085
GPT teacher head0.401
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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