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Record W4366085236 · doi:10.29244/agro-maritim.050102

Strategi Pengendalian Stres pada Suami Pekerja Migran Indonesia

2023· article· id· W4366085236 on OpenAlexaff
Vivi Irzalinda, Nia Reviani, Mirdat Silitonga, Herien Puspitawati

Bibliographic record

VenuePolicy Brief Pertanian Kelautan dan Biosains Tropika · 2023
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesGynecologyArtMedicine

Abstract

fetched live from OpenAlex

Stres dikategorikan ketika tuntutan dan beban tugas yang berat dialami seseorang namun tidak dapat menyelesaikannya, sehingga akan berdampak negatif pada tubuh seseorang. Respons tubuh tersebut disebut respons fisiologis dan psikologis. Suami akan mengalami stres saat istri memutuskan menjadi TKI karena merasa pekerjaan yang biasanya dikerjakan oleh istri sekarang menjadi tanggung jawab suami. Perginya istri menjadi masalah yang berat bagi suami. Banyaknya beban yang ditanggung suami dapat mengakibatkan suami mengalami gejala stres. Pentingnya pengendalian stres suami TKI sebagai masukan program relevan kerjasama kementerian yaitu integrasi pendidikan komunitas pembangunan keluarga (community parenting), meningkatkan akses kesehatan mental dan terapi keluarga bagi suami TKI.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.030
GPT teacher head0.318
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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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