Gambaran Cakupan Imunisasi Dasar Lengkap (IDL) di Puskesmas Kadungora Garut Tahun 2024
Bibliographic record
Abstract
Complete Basic Immunization (CBI) coverage is a crucial indicator in the effort to prevent vaccine-preventable diseases and reflects the performance of primary healthcare services. In 2024, Garut Regency recorded a CBI coverage rate of 78.29%, while Kadungora Public Health Center reported the lowest rate at 65.07%. This study aims to describe the CBI coverage at Kadungora Public Health Center during the year 2024. The research used a quantitative descriptive method with a secondary data analysis approach. The population and the sample consisted of 813 infants who received immunizations and were recorded at Kadungora PHC between January and December 2024. The results showed coverage for each type of immunization as follows: HB-0 (71,7%), BCG (90%), DPT-HB-Hib 1–3 (91%,92,3%,91,9%), OPV 1–4 (90.3%,91,5%,92,1%,91%), IPV 1–2 (81,4%,70,6%), MR (91,1), PCV I–II (81,1%,82,2%), and Rotavirus I–III (58.18%–69.13%). The lowest coverage was found in HB 0, Rotavirus and IPV immunizations. These findings highlight the need for targeted interventions to improve CBI coverage, particularly for vaccines with the lowest achievement rates, in order to meet the national immunization coverage targets more evenly.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".