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Record W4417278100 · doi:10.33024/hjk.v19i9.1747

Hubungan tekanan darah dengan depresi

2025· article· W4417278100 on OpenAlexaff
Wiwik Widiyawati, Deah Dwi Musfara, Anik Nur Kholifah, Tomi Indarto, Nilam Enggarsasi Setyoningrum

Bibliographic record

VenueHOLISTIK JURNAL KESEHATAN · 2025
Typearticle
Language
FieldPsychology
TopicStudent Stress and Coping
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBlood pressureDepression (economics)MoodFeelingPopulationIncidence (geometry)

Abstract

fetched live from OpenAlex

Background: Blood pressure is the heart's ability to pump blood throughout the body, which is divided into systolic and diastolic pressures. Elevated blood pressure can also be caused by psychological disorders such as stress, anxiety, and even depression. Depression is an emotional disorder or low mood characterized by prolonged or persistent sadness, hopelessness, guilt, and feelings of worthlessness. These factors disrupt all mental processes (thinking, feeling, and behaving) and decrease motivation for daily activities and interpersonal relationships. A common characteristic of depressed patients is when patients express doubts about self-worth or coping abilities. Purpose: To determine the relationship between blood pressure and depression. Method: Quantitative research using a cross-sectional approach. Data collection used the DASS-42 questionnaire to assess levels of depression. The population in this study was 1,580 people from the industrial area of ​​Driyorejo District, Gresik Regency. The sampling technique used was total sampling. Results: Three (37.5%) respondents with high blood pressure experienced severe depression, 97 (78.2%) respondents with normal blood pressure experienced moderate depression, and five (40%) respondents with low blood pressure experienced mild depression. The test showed a p-value of 0.07 (>0.05), indicating a very weak relationship. Conclusion: There is a very weak relationship between blood pressure and the incidence of depression, with a p-value of 0.07>0.05. Keywords: Blood Pressure; Depression; Hypertension. Pendahuluan: Tekanan darah memiliki kemampuan jantung memompa darah ke seluruh tubuh, yang dimana jantung memompa atas dinding arteri dan dibagi menjadi sistolik serta diastolik. Naiknya tekanan darah juga bisa karena gangguan psikis seperti stress, anxietas, bahkan depresi. Depresi adalah suatu gangguan emosional atau suasana hati yang buruk dengan ditandai timbulnya kesedihan yang berkepanjangan atau terus menerus, putus harapan, perasaan bersalah serta keadaan merasa tidak berarti. Faktor tersebut menyebabkan seluruh proses mental (berpikir, berperasaan, dan berperilaku) terganggu dan membuat menurunnya motivasi untuk beraktivitas dalam kehidupan sehari-hari serta pada hubungan interpersonal. Karakteristik umum pada pasien depresi ialah disaat pasien menyatakan atas keraguan tentang harga diri atau kemampuan koping. Tujuan: Untuk mengetahui hubungan antara tekanan darah dengan depresi. Metode: Penelitian kuantitatif dengan pendekatan cross sectional. Pengumpulan data menggunakan kuesioner DASS-42 untuk melihat tingkat depresi. Populasi pada penelitian ini adalah Masyarakat wilayah industri Kecamatan Driyorejo, Kabupaten Gresik sebanyak 1,580 orang. Teknik sampling pada penelitian ini adalah total sampling. Hasil: Sebanyak 3 (37.5%) responden dengan tekanan darah tinggi mengalami depresi sangat parah, sebanyak 97 (78.2%) responden dengan tekanan darah normal mengalami depresi sedang, dan sebanyak 5 (40.0%) masyarakat dengan tekanan darah rendah mengalami depresi ringan. Berdasarkan uji analisis didapatkan p value =0.07 (>0.05), yang menunjukkan hubungan sangat lemah. Simpulan: Terdapat hubungan yang sangat lemah antara tekanan darah dan kejadian depresi dengan p value (0.07>0.05). Kata Kunci: Depresi; Hipertensi; Tekanan Darah.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.002

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 head0.022
GPT teacher head0.370
Teacher spread0.348 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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