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Record W3025812140 · doi:10.46366/ijkmi.1.1.17-24

ANALISIS DUKUNGAN INSTITUSI TERHADAP PENYELENGGARAAN KANTIN SEHAT PADA MAHASISWA DI UNIVERSITAS NEGERI DAN SWASTA DI JAKARTA

2020· article· id· W3025812140 on OpenAlexaboutno aff
Tria Astika Endah Permatasari, Nurkamalia Nurkamalia, Nia Mailan Astin

Bibliographic record

VenueIAKMI Jurnal Kesehatan Masyarakat Indonesia · 2020
Typearticle
Languageid
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Masalah gizi ganda terus meningkat di berbagai negara termasuk di Indonesia. Salah satu kelompok yang berkontribusi dalam kondisi ini adalah mahasiswa. Ketersediaan kantin sehat di kampus menjadi faktor penyebab utama rendahnya status gizi mahasiswa. Penelitian ini bertujuan untuk mengetahui dukungan institusi terhadap peyelenggaraan kantin sehat pada mahasiswa di Universitas negeri dan swasta di Jakarta. Penelitian ini menggunakan desain studi analitik observasional dengan menggunakan metode cross sectional. Subjek penelitian ini adalah mahasiwa Negeri dan mahasiswa Swasta di Jakarta. Teknik pengambilan sampel yang digunakan adalah accidental sampling dengan jumlah responden sebanyak 114 responden yang terdiri dari 57 mahasiswa Perguruan Tinggi Negeri dan 57 mahasiswa Perguruan Tinggi Swasta. Data diambil pada bulan Oktober-November 2017 dengan menggunakan kuesioner yang diadaptasi dari Ontario Society of Nutrition Professionals in Public Health (OSNPPH). Data ini dianalisis dengan menggunakan analisis multivariat logistic ganda. Penelitian ini menunjukkan bahwa didapatkan nilai OR pada dukungan institusi sebesar 4,246 (95% CI: 1,933 – 9,326). Berdasarkan persepsi mahasiswa, dukungan institusi berpeluang 4 x lipat terhadap penyelenggaraan kantin sehat dibandingkan dengan yang tidak mendapatkan dukungan ististusi. Oleh karenanya, perlu adanya dukungan kebijakan penyelenggaraan kantin sehat dan penyediaan fasilitas di kampus dalam menunjang pemenuhan gizi mahasiswa.

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), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.263
Teacher spread0.236 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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