RELATIONSHIP OF THERAPEUTIC COMMUNICATION WITH ANXIETY OF FIRST ACTIVE PHASE LABOR PATIENTS IN DR. SOEKARDJO TASIKMALAYA’S HOSPITAL
Bibliographic record
Abstract
Kecemasan menjelang persalinan akan mengakibatkan peningkatan kecemasan ke level yang lebih tinggi dan meningkatkan resiko cedera dan akan mempengaruhi kontraksi menjadi hypotonic. Salah satu faktor yang mempengaruhi kecemasan persalinan yaitu kurangnya komunikasi terapeutik. Tujuan penelitian adalah untuk mengetahui hubungan komunikasi terapeutik dengan tingkat kecemasan pasien persalinan kala 1 fase aktif di RSUD dr. Soekardjo Tasikmalaya. Metoda penelitian yang digunakan deskriptif korelasional. Populasinya adalah pasien bersalin kala 1 fase aktif, di ruang bersalin. Sampel 30 orang secara accidental sampling selama 3 minggu. Instrumen penelitian menggunakan kuesioner komunikasi terapeutik dan kecemasan yang sudah baku. Uji hipotesis menggunakan uji Rank Spearman. Hasil penelitian menunjukan persentasi komunikasi terapeutik paling tinggi pada kategori “kurang” sebanyak 13 orang (43.33%), untuk tingkat kecemasan paling tinggi kategori “berat” sebanyak 20 orang (66.66%). Hasil uji hipotesis menunjukkan bahwa ada hubungan komunikasi terapeutik dengan tingkat kecemasan pasien persalinan kala 1 fase aktif dengan P-value = 0,026, nilai koefisien korelasi 0.463 artinya kekuatan hubungannya cukup. Berdasarkan hasil penelitian, disarankan agar bidan dapat memberikan pelayanana yang terbaik terhadap pasien dengan meningkatkan komunikasi terapeutik untuk mengurangi kecemasan pada ibu bersalin.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".