Analisis Kepuasan Mutu Pelayanan Imunisasi DPT di Wilayah Puskesmas Punti Kayu Palembang
Bibliographic record
Abstract
World Health Organization dan United Nations International Children's Emergency Fund menyebutkan setiap tahunnya 2,6 juta bayi diseluruh dunia tidak mampu bertahan hidup selama lebih dari satu bulan. Satu juta di antaranya meninggal saat lahir. Indonesia setiap usia 0-11 bulan diwajibkan mendapatkan imunisasi dasar lengkap salah satunya DPT. Penelitian ini bertujuan diketahuinya analisis kepuasan mutu pelayanan imunisasi DPT di wilayah Puskesmas Punti Kayu Palembang tahun 2022. Dilaksanakan pada Mei - Juni 2022. Penelitian ini kuantitatif dengan desain cross sectional, populasi penelitian ini adalah jumlah kunjungan imunisasi di wilayah Puskesmas Punti Kayu Kota Palembang sebanyak 88 sampel. Pengumpulan dan pengambilan data menggunakan kuesioner. Hasil uji statistik mengunakan uji Chi-Square dan regresi logistik berganda dimana hasilnya menunjukkan ada hubungan bermakna (p value < 0,05) untuk variabel umur (0,040), pekerjaan (0,022), pengetahuan (0,003), sikap (0,041), dan peran petugas kesehatan (0,001). Tidak ada hubungan variabel jenis kelamin (0,302), pendidikan (0,302), dan penghasilan (0,104). Dari hasil uji statistik multivariat diperoleh faktor dominan adalah peran petugas kesehatan (p= 0,001; OR= 42.243). Diharapakan kepada tenaga kesehatan di wilayah Puskesmas Punti Kayu Palembang untuk selalu memberikan promosi kesehatan bagi ibu pasca melahirkan tentang imunisasi dasar lengkap khususnya DPT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".