Pengaruh Knowledge Sharing, Komitmen Organisasi Dan Kepemimpinan Terhadap Kinerja Pegawai Dinas Kesehatan Surabaya, Bidang Pencegahan Dan Pengendalian Penyakit
Bibliographic record
Abstract
Penelitian ini meneliti pengaruh knowledge sharing, komitmen organisasi, dan kepemimpinan terhadap kinerja pegawai di Dinas Kesehatan Surabaya, khususnya di Bidang Pencegahan dan Pengendalian Penyakit. Penelitian ini dilatarbelakangi oleh pentingnya kinerja pegawai dalam pelayanan kesehatan dan adanya inkonsistensi hasil penelitian sebelumnya terkait faktor-faktor yang mempengaruhinya. Tujuan penelitian ini adalah menganalisis pengaruh parsial dan simultan dari knowledge sharing, komitmen organisasi, dan kepemimpinan terhadap kinerja pegawai, serta menentukan variabel yang berpengaruh dominan. Penelitian ini menggunakan metode kuantitatif dengan pendekatan survei. Data dikumpulkan melalui kuesioner yang disebarkan kepada sampel pegawai di Bidang Pencegahan dan Pengendalian Penyakit Dinas Kesehatan Surabaya. Analisis data dilakukan dengan menggunakan teknik regresi linier berganda. Hasil penelitian menunjukkan bahwa knowledge sharing dan komitmen organisasi berpengaruh positif dan signifikan terhadap kinerja pegawai, sedangkan kepemimpinan tidak berpengaruh signifikan. Secara simultan, knowledge sharing, komitmen organisasi, dan kepemimpinan berpengaruh signifikan terhadap kinerja pegawai. Variabel komitmen organisasi memiliki pengaruh dominan terhadap kinerja pegawai. Penelitian ini diharapkan dapat memberikan wawasan bagi praktisi dalam meningkatkan kinerja pegawai, menjadi referensi ilmiah di bidang manajemen sumber daya manusia, dan menjadi acuan bagi penelitian selanjutnya.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".