Faktor-Faktor Kecemasan Tenaga Kesehatan Selama Pandemi COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Salah satu dampak dari pandemi COVID-19 adalah meningkatnya prevalensi kecemasan tenaga kesehatan. Penelitian ini bersifat analisis kuantitatif dengan desain studi cross-sectional. Sebanyak 167 tenaga kesehatan yang bekerja di RSUD Pasar Rebo direkrut sebagai sampel menggunakan teknik convenience sampling. Pengumpulan data dilakukan menggunakan kuesioner Zung Self-Rating Anxiety Scale yang disebarkan secara daring. Data dianalisis dengan menggunakan uji Chi Square. Hasil analisis menunjukkan bahwa terdapat hubungan yang bermakna antara jenis kelamin, usia, status pernikahan, status memiliki anak, bekerja sebagai perawat, dan beban kerja berat dengan tingkat kecemasan pada tenaga kesehatan. Diharapkan rumah sakit dapat mengambil upaya khusus untuk menjaga kesehatan mental tenaga kesehatan seperti memberikan konsultasi, mensosialisasikan mekanisme coping yang efektif pada tenaga kesehatan
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 it