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Record W4391390810 · doi:10.56467/jptk.v7i1.130

Pengaruh Kombinasi Terapi Musik dan Slow Deep Breathing terhadap Perubahan Tekanan Darah Pasien Hipertensi

2024· article· id· W4391390810 on OpenAlexaff
Maryam Suaib, Dewiyanti Dewiyanti

Bibliographic record

VenueJurnal Pendidikan dan Teknologi Kesehatan · 2024
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Hipertensi adalah peningkatan tekanan darah yang tidak menimbulkan gejala selama bertahun-tahun hingga terjadi kerusakan organ yang tidak berarti dan menjadi penyebab meningkatnya morbiditas dan mortalitas di Indonesia. Kombinasi terapi musik dan slow deep breathing membantu mengrontrol tekanan darah secara bertahap. Kondisi ini akan menjaga pasien agar tidak mengalami komplikasi. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh terapi musik yang dikombinasikan dengan slow deep breathing terhadap perubahan tekanan darah pada penderita hipertensi. Jenis penelitian ini adalah quasy-experiment dengan rancangan pretest-posttest one group. Sampel dalam penelitian ini adalah 37 responden yang merupakan penderita hipertensi di ruang Interna RSUD Palemmai Tandi dengan cara Purosive Sampling. Penelitian ini menggunakan handphone dan aplikasi Mp3 dalam pelaksanaan intervensi dan menggunakan lembar observasi untuk pengukuran data primer. Uji Analisa data yang digunakan adalah Uji Wilcoxon Signed Ranks Test. Hasil uji Analisa ditemukan adanya pengaruh kombinasi terapi musik dan slow deep breathing terhadap perubahan tekanan darah pada pasien hipertensi yang dilakukan di Ruang Interna RSUD Palemmai Tandi (p value 0,004). Disarankan kepada perawat di RS untuk memasukkan kombinasi terapi musik dan slow deep breathing sebagai intervensi keperawatan untuk menurunkan tekanan darah yang dialami oleh pasien hipertensi.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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