APLIKASI MEDCO DALAM MENINGKATKAN PENGETAHUAN KADER TERKAIT KOMPLIKASI KEHAMILAN
Bibliographic record
Abstract
Setiap hari diperkirakan 800 orang perempuan yang meninggal disebabkan kasus komplikasi kehamilan dan persalinan. Hampir semua kematian pada ibu ini disebabkan oleh penyebab yang seharusnya dapat dicegah jika tenaga kesehatan bisa mendeteksi dan melakukan penatalaksanaan komplikasi dengan tepat. Penelitian ini bertujuan untuk menilai efekrtivitas peningkatan pengetahuan kader kesehatan ibu dan anak tentang deteksi dini komplikasi kehamilan yang dilakukan melalui aplikasi Midwfery Earty Detection Of Complication For Pregnant Women di Puskesmas di Wilayah Kerja Dinas Kesehahatan Koto padang. Desain penelitian ini adalah pra eksperiment pre test dan post test menggunakan rancangan one group pre test post test. Sampel yang diambil dalam penelitian ini adalah 17 orang Kader kesehatan yang didapatkan melalui teknik cluster random sampling. Pengumpulan data dengan menggunakan kusioner, waktu penelitian dilakukan pada tanggal 20 agustus 2019 s/d 8 september 2019, kemudian analisis dengan menggunakan t-test dependent (paired sample t-test). Pada uji T-Test didapatkan hasil bahwa p=0,000, dimana p0,05. Sehingga dapat disimpulkan bahwa adanya pengaruh aplikasi Midwifery Early Detection of Complication for Pregnant Women (MEDCO) terhadap pengetahuan kader kesehatan di Puskesmas-Puskesmas Wilayah Kerja Dinas Kesehatan Kota Padang. Aplikasi Midwifery Early Detection of Complication for Pregnant Women Merupakan salah satu tools yang digunakan untuk meningkatkan pengetahuan kader sehingga diharapkan meningkatkan peran kader. Diharapkan tenaga kesehatan dapat mensosialisasikan pemenafaatan MEDCO
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".