Gambaran Pelaksanaan Imunisasi Baduta (Booster) Sebagai Upaya Pencegahan Penyakit Menular Di Puskesmas Peterongan Dan Puskesmas Mayangan Di Kabupaten Jombang
Bibliographic record
Abstract
Masih banyak puskesmas di Kabupaten Jombang yang cakupan imunisasinya belum mencapai target, beberapa diantaranya Puskesmas Peterongan dan Puskesmas Mayangan. Sehingga masih perlu dilakukan evaluasi yang mendalam terkait permasalahan tersebut, dengan demikian dapat dilakukan tindakan intervensi yang tepat. \n \nPelaksanaan program imunisasi baduta (booster) di Puskesmas Peterongan dan Puskesmas Mayangan telah sesuai dengan pedoman yang ada. Namun masih ada yang perlu di evaluasi terkait kendala dan hambatan dalam pelaksanaannya sehingga dapat diperoleh solusi yang tepat untuk memecahkan permasalahan yang ada. Berdasarkan hasil yang diperoleh, permasalahan yang ditemukan adalah tingginya kekhawatiran ibu terhadap efek samping imunisasi, anggapan masyarakat bahwa imunisasi dasar saja sudah cukup, rendahnya komitmen bidan desa terhadap pelaksanaan program imunisasi, masih rendahnya dukungan masyarakat (tokoh agama, tokoh masyarakat, dll) dalam pelaksanaan program imunisasi \n \nDinas Kesehatan Kabupaten Jombang telah melaksanakan strategi promosi kesehatan. Diharapkan strategi yang dilakukan mampu berkembang dan inovatif untuk pelaksanaan imunisasi yang lebih baik. \nRekomendasi promosi kesehatan yang diberikan dianalisis berdasarkan Piagam Ottawa Charter
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".