RELATIONSHIP OF BODY INDEX (BMI) WITH PRAMENSTRUATED SYNDROME IN NURSING STUDENT PROGRAMS AT THE UNIVERSITY OF BHAKTI KENCANA TASIKMALAYA
Bibliographic record
Abstract
Sindrom pramenstruasi merupakan kumpulan gejala fisik, psikologis yang terkait dan emosi dalam siklus menstruasi. Sekitar 80 sampai 95 persen wanita mengalami gejala-gejala pramenstruasi yang dapat mengganggu aktifitasnya. Beberapa faktor yang dapat menyebabkan sindrom pramenstruasi adalah peningkatan kadar hormon estrogen. Bahan dasar esterogen adalah lemak, untuk bisa memperdiksi lemak dalam tubuh deng cara mengukur indeks masa tubuh. Rancangan penelitian ini menggunakan observasional analitik. Tujuan dari penelitian ini menganalisis hubungan indeks masa tubuh dengan sindrom pramenstruasi pada mahasiswi prodi sarjana keperawatan. Sampel pada penelitian ini menggunakan random sampling sebanyak 63 responden. Hasil menunjukan indeks tertinggi masa tubuh dengan kategori kurus yaitu 24 orang (38,1%) dan kejadian sindroma pramesntruasi menunjukan frekuensi tertinggi adalah tidak mengalami sindroma pramenstruasi yaitu 37 orang (56,7%), sedangkan analisis bivariat menunjukan ada hubungan yang signifikan antara indeks masa tubuh dengan sindrom pramenstruasi pada mahasiswi prodi sarjana keperawatan dengan nilai p-value = 0,031. Responden diharapakan dapat melakukan pencegahan dan melakukan rutinitas sehari-harinya lebih baik lagi untuk menjalankan sindrom pramenstruasi.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".