PERSPEKTIF MASYARAKAT, PELAJAR, DAN ATLET MENGENAI PENTINGNYA PEREGANGAN
Bibliographic record
Abstract
Penelitian ini berisi tentang berbagai perspektif dari para partisipan mengenai pentingnya peregangan sebelum berolahraga. Tujuan dari penelitian ini untuk mengetahui seberapa pentingnya peregangan bagi para partisipan dan apa saja yang dialami partisipan ketika berolahraga tanpa melakukan peregangan. Penelitian ini menggunkan Metode Penelitian Kuantitatif (Metode Survey) berupa Google Form/angket yang disebarkan melalui sosial media (Whatsapp, Instagram ataupun media sosial lainnya). Masyarakat, pelajar dan atlet yang menjadi objek dalam penelitian ini dengan latar belakang cabang olahraga yang berbeda dan rentan usia partisipan dari 16-35 tahun. Hasilnya mayoritas partisipan dengan persentase 95,2% menganggap bahwa peregangan itu sangat penting. Namun meskipun dianggap sangat penting, tak sedikit dari partisipan yang jarang dan bahkan tidak sama sekali melakukan peregangan sebelum berolahraga. Hasilnya cedera ringan sampai parah tak bisa dihindarkan oleh para partisipan. Selain cedera, para partisipan yang tidak melakukan peregangan juga mengalami ketidaknyamanan saat berolahraga dari mulai otot mudah lelah, mudah mengalami nyeri, otot kaku, detak jantung meningkat dan pernapasan tak teratur. Mayoritas partisipan melakukan peregangan dalam jangka waktu 5-10 menit.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.015 |
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".