Modul Sosialisasi Interprofession Education
Bibliographic record
Abstract
Tantangan di dunia kesehatan dewasa ini semakin tinggi seiring meningkatnya kebutuhan masyarakat akan pelayanan kesehatan yang berkualitas. Dalam upaya menyelenggarakan pelayanan kesehatan yang berkualitas, tentunya tidak bisa hanya dilakukan hanya oleh satu profesi kesehatan. Beragamnya masalah kesehatan yang ada di masyarakat menuntut para profesi kesehatan untuk berkolaborasi dalam memberikan pelayanan kesehatan. Seperti halnya yang dinyatakan dalam Alma Ata Declaration In International Conference on Primary Health Care (1978), bahwa praktik kolaborasi yang efektif merupakan prinsip kunci dalam pelayanan kesehatan. Peningkatan mutu pelayanan kesehatan akan terjadi jika profesi kesehatan interdisiplin saling bekerjasama/berkolaborasi dalam tim (Hind, 2003). Canadian Interprofessional Health Collaborative (CIHC) menyebukan bahwa praktik kolaborasi yang berpusat pada pasien juga memperbaiki kualitas pelayanan pasien dan meningkatkan luaran kesehatan pasien. Praktik kolaborasi menurunkan kejadian komplikasi, lama rawat inap, jumlah kunjungan ke rumah sakit, kejadian malpraktik, dan angka kematian. Praktik kolaborasi juga mengurangi konflik di antara tenaga kesehatan, mengurangi biaya kesehatan, dan meningkatkan kepuasan pasien serta tenaga kesehatan (Goldman J, 2011).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".