Pengaruh Profesionalisme, Iklim Organisasi Dan Kompetensi Terhadap Kinerja Pegawai Bagian Umum Dan Keuangan RSUD Dr. Mohamad Soewandie – Surabaya
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis pengaruh profesionalisme, iklim organisasi, dan kompetensi terhadap kinerja pegawai Bagian Umum dan Keuangan RSUD Dr. Mohamad Soewandhie – Surabaya. Pendekatan yang digunakan dalam penelitian ini adalah metode kuantitatif dengan teknik analisis regresi linier berganda. Populasi dalam penelitian ini adalah seluruh pegawai Bagian Umum dan Keuangan RSUD Dr. Mohamad Soewandhie yang berjumlah 40 orang, dan sampel penelitian menggunakan teknik sampel jenuh, sehingga seluruh populasi diambil sebagai sampel. Hasil penelitian menunjukkan bahwa secara parsial, profesionalisme, iklim organisasi, dan kompetensi berpengaruh signifikan terhadap kinerja pegawai. Selain itu, secara simultan, ketiga variabel tersebut juga memiliki pengaruh yang signifikan terhadap kinerja pegawai. Di antara ketiga variabel tersebut, kompetensi memiliki pengaruh dominan terhadap kinerja pegawai. Temuan ini mengindikasikan bahwa peningkatan kompetensi pegawai, didukung dengan profesionalisme dan iklim organisasi yang baik, dapat meningkatkan kinerja pegawai secara optimal. Berdasarkan hasil penelitian, disarankan agar manajemen rumah sakit lebih fokus pada pengembangan program pelatihan dan peningkatan kompetensi pegawai, memperkuat budaya profesionalisme, serta menciptakan iklim organisasi yang kondusif guna meningkatkan kinerja pegawai secara berkelanjutan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".