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Record W4312759618 · doi:10.30737/nsj.v5i2.2302

Hubungan Kekeringan dengan Praktik Personal Hygiene

2021· article· id· W4312759618 on OpenAlexaff
Saddan Sari Safitri, Tri Susilowati, Eska Dwi Prajayanti

Bibliographic record

VenueNursing Sciences Journal · 2021
Typearticle
Languageid
FieldSocial Sciences
TopicCOVID-19 Prevention and Impact
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHygieneGynecologyPersonal hygieneMedicineFamily medicinePathology

Abstract

fetched live from OpenAlex

Latar Belakang; Pada tahun 2018 Indonesia terdapat 13 kejadian dengan 5 provinsi yang terdampak. Salah satu kabupaten di Jawa Tengah, Boyolali juga memiliki tingkat kejadian kekeringan yang tinggi. Pada tahun 2018 menurut data dari BPBD Boyolali, terdapat 7 kecamatan dan 42 kelurahan terdampak bencana kekeringan, dan Desa yang paling parah terdampak kekeringan ialah Desa Ngaren. Kekeringan menyebabkan ketersediaan air bersih terbatas sehingga menyebabkan gangguan aktivitas rumah tangga dan kebersihan diri (personal hygiene). Metode; penelitian survei analitik yang menggunakan metode cross sectional dengan pendekatan waktu retrospective. Pengambilan sampel menggunakan teknik purposive sampling, dengan jumlah sampel 79 responden, sedangkan instrumen penelitian menggunakan kuesioner. Analisa bivariat menggunakan uji chi square.Tujuan; Mengetahui hubungan antara kekeringan dengan praktik personal hygiene di Desa Ngaren, Kec.Juwangi, Kab. Boyolali. Hasil; Hasil penelitian didapatkan sebagian besar responden mengalami kekeringan dengan kategori kering kritis sebanyak 40 responden (51%), sebagian besar responden melakukan praktik personal hygiene yang kurang baik sebanyak 64 responden (81%), sedangkan pada uji univariat membuktikan bahwa kekeringan di Desa Ngaren berhubungan dengan praktik personal hygiene (p value = 0,002). Kesimpulan; Terdapat hubungan kekeringan dengan praktik personal hygiene di Desa Ngaren Kecamatan Juwangi Kabupaten Boyolali.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.398
Teacher spread0.338 · 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 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
Published2021
Admission routes1
Has abstractyes

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