<b>HUBUNGAN KEBIASAAN OLAHRAGA DENGAN KEKUATAN OTOT LANSIA</b> <b>DI WILAYAH KERJA PUSKESMAS PERAMPUAN</b> <b>KABUPATEN LOMBOK BARAT</b>
Bibliographic record
Abstract
Pendahuluan: Peningkatan jumlah lansia di Indonesia berdampak pada meningkatnya masalah kesehatan akibat proses penuaan, salah satunya penurunan kekuatan otot (sarkopenia). Kebiasaan olahraga merupakan salah satu faktor yang dapat memperlambat penurunan kekuatan otot pada lansia. Terdapat 65% Lansia di wilayah Kerja Puskesmas Perampuan Kabupaten Lombok Barat belum memiliki kebiasaan olahraga teratur sehingga rentan mengalami kelemahan otot. Tujuan: Mengetahui hubungan antara kebiasaan olahraga dengan kekuatan otot pada lansia perempuan di Desa Perampuan, Kecamatan Labuapi, Kabupaten Lombok Barat. Metode: Penelitian ini dilakukan pada bulan Maret sampai September 2025 dengan menggunakan desain analitik dengan pendekatan cross sectional. Teknik pengambilan sampel menggunakan purposive sampling dengan jumlah sampel 60 responden lansia perempuan. Instrumen penelitian meliputi kuesioner kebiasaan olahraga dan pemeriksaan kekuatan otot menggunakan Manual Muscle Testing (MMT). Analisis data dilakukan dengan uji korelasi Spearman Rank (α = 0,10). Hasil: Responden memiliki kebiasaan olahraga dalam kategori cukup (38,3%), baik (51,7%), dan kekuatan otot berada pada kategori sedang hingga kuat. Uji Spearman rank menunjukkan (p = 0,001; r = 0,631. Kesimpulan: Terdapat hubungan signifikan antara kebiasaan olahraga dengan kekuatan otot pada lansia perempuan di Desa Perampuan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.017 |
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".