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Record W4389410902 · doi:10.36568/nersbaya.v17i1.37

Hubungan Kecemasan Dengan Kualitas Tidur Klien Asma Di Wilayah Kerja Puskesmas Tulangan Kabupaten Sidoarjo

2023· article· id· W4389410902 on OpenAlexaff
Putriari Riskiani, Padoli Padoli, Kiaonarni Ongko W

Bibliographic record

VenueJurnal Keperawatan · 2023
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicineGynecologyHumanities

Abstract

fetched live from OpenAlex

ABSTRAK Serangan asma menjadi salah satu faktor pencetus terjadinya stress. Selain itu juga bisa memperberat serangan asma yang sudah ada. Kecemasan mengakibatkan penurunan fungsi dari Suprachiasmatic Nukleus (SCN) di hypothalamus yang mengakibatkan gangguan pada ritme sirkadia membuat klien asma mengalami kualitas tidur buruk. Tujuan dari penelitian ini adalah untuk mengetahui Hubungan Kecemasan Dengan Kualitas Tidur Klien Asma Di Wilayah Kerja Puskesmas Tulangan Kabupaten Sidoarjo. Penelitian ini menggunakan jenis penelitian kuantitatif dengan metode penelitian desain deskriptif korelasional menggunakan pendekatan cross sectional. Populasi pada penelitian ini adalah seluruh klien asma yang melakukan pemeriksaan di Puskesmas Tulangan Kabupaten Sidoarjo berjumlah 78 klien dengan jumlah sampel 65 klien. Teknik sampling yang digunakan adalah Non Probalility Sampling dengan metode Accidental Sampling. Instrumen pengumpulan data menggunakan kuesioner kecemasan Zung Self Rating Anxiety Scale dan kuesioner kualitas tidur Pittsburgh Sleep Quality Index. Data yang didapatkan kemudian dianalisis menggunakan uji hipotesis Chi-Square. Hasil penelitian ini menunjukkan bahwa hampir setengahnya klien asma (40,0%) mengalami kecemasan sedang dan sebagian besar klien asma (55,4%) mengalami kualitas tidur buruk. Ada hubungan antara kecemasan dengan kualitas tidur klien asma. (P value=0,002) dimana ketika klien semakin cemas maka kualitas tidur semakin buruk. Klien asma hendaknya tetap menjaga dan mengelola mood dengan baik, menciptakan suasana tidur yang nyaman, minum obat asma sesuai anjuran dokter, sehingga tidak terjadi kecemasan dan kualitas tidur tidak buruk. Kata Kunci : Asma, Kecemasan, Kualitas Tidur ABSTRACT Asthma attacks are one of the trigger factors for stress. It can also exacerbate existing asthma attacks. Anxiety causes a decrease in the function of the Suprachiasmatic Nucleus (SCN) in the hypothalamus which results in disturbances in circadian rhythms making asthmatic clients experience poor sleep quality. The purpose of this research is to find out Relationship Of Anxiety With Sleep Quality Of Asthma Clients In The Working Area Of Puskesmas Tulangan Kabupaten Sidoarjo. This research uses quantitative research with correlational descriptive design research method using cross sectional approach. The population in this study were all asthmatic clients who did examinations at the Puskesmas Tulangan Kabupaten Sidoarjo totaling 78 clients with a sample of 65 clients. The sampling technique used is Non-Probability Sampling with the Accidental Sampling method. The data collection instrument used the Zung Self Rating Anxiety Scale anxiety questionnaire and the Pittsburgh Sleep Quality Index sleep quality questionnaire. The data obtained were then analyzed using the Chi-Square hypothesis test. The results of this study showed that almost half of asthmatic clients (40.0%) experienced moderate anxiety and most asthmatic clients (55.4%) experienced poor sleep quality. There is a relationship between anxiety and sleep quality of asthmatic clients. (P value = 0.002) where when the client gets more anxious, the sleep quality gets worse. Asthma clients should maintain and manage mood well, create a comfortable sleeping atmosphere, take asthma medication as recommended by the doctor, so that anxiety does not occur and sleep quality is not bad. Keywords : Asthma, Anxiety, Sleep Quality

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.005

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 head0.037
GPT teacher head0.325
Teacher spread0.288 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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