PERCEPATAN PEMENUHAN TENAGA KESEHATAN DI PUSKESMAS UNTUK DAYA UNGKIT PEMBANGUNAN KESEHATAN
Bibliographic record
Abstract
Keberadaan tenaga kesehatan, dalam Sistem Informasi SDM Kesehatan per Februari 2024 diketahui bahwa masih terdapat 4.699 (46%) Puskesmas yang belum memiliki 9 (sembilan) jenis tenaga kesehatan sesuai standar. Rendahnya ketersediaan tenaga kesehatan di Puskesmas, khususnya di wilayah timur, tentunya membutuhkan langkah-langkah percepatan dalam pemenuhan tenaga kesehatan di Puskesmas, yaitu dengan pemenuhan tenaga kesehatan melalui skema Pegawai Pemerintah dengan Perjanjian Kerja (P3K), redistribusi tenaga kesehatan, pengaturan Surat Izin Praktik (SIP), pemberian insentif tenaga kesehatan, pengembangan karir tenaga kesehatan, kolaborasi lintas program dan lintas sektor (Pemerintah Daerah, perguruan tinggi, Kementerian/Lembaga, dll), pemenuhan fasilitas kesehatan satu paket dengan pemenuhan tenaga kesehatan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.020 | 0.003 |
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".