Pengaruh Pemberian Kompres Lidah Buaya (Aloe Vera) terhadap Penurunan Suhu Bayi Pasca Imunisasi DPT-HB
Bibliographic record
Abstract
Imunisasi DPT-HB (difteri, pertusisi, tetanus dan hepatitis B) dapat menimbulkan reaksi lokal yang mungkin timbul seperti rasa nyeri, merah dan bengkak. Umumnya pasca imunisasi ini anak sedikit rewel dan demam. Terapi aloe vera dipilih karena 95% kandungannya adalah air yang dapat dimanfaatkan untuk menurunkan demam melalui mekanisme penyerapan panas tubuh. Tujuan penelitian ini adalah untuk mengetahui pengaruh kompres lidah buaya (aloe vera) terhadap penurunan suhu tubuh bayi pasca imunisasi DPT-HB di Wilayah Kerja Puskesmas Pasar Baru Kabupaten Merangin. Desain penelitian yang digunakan adalah pra eksperimen dengan rancangan one group pretest-posttest design. Sampel penelitian ini sebanyak 20 bayi yang diambil menggunakan metode accidental sampling. Pengumpulan data primer menggunakan termometer untuk pengukuran suhu tubuh bayi, terapi lidah buaya yang sudah di bungkus dengan kasa steril, dan lembar observasi untuk mencatat hasil pengukuran suhu. Analisis data mengunakan t-test. Hasil penelitian memperoleh rerata penurunan suhu tubuh bayi sesudah diberikan kompres lidah buaya sebesar 0,64°C dan 85% suhu tubuh bayi menjadi normal. Hasil uji statistik menunjukkan terdapat pengaruh kompres lidah buaya terhadap penurunan suhu tubuh bayi pasca imunisasi DPT-HB (p 0,000). Pihak puskesmas diharapkan dapat merekomendasikan kepada ibu bayi terapi lidah buaya sebagai alternatif penanganan demam pada bayi pasca imunisasi DPT-HB.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".