PENGARUH PENGEMBANGAN KARIR DAN MANAJEMEN KONFLIK TERHADAP KINERJA BPJS KESEHATAN CABANG PALU
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui dan menganalisis: (1) Pengembangan Karir dan Manajemen Konflik berpengaruh serempak terhadap Kinerja Pegawai pada BPJS Kesehatan Cabang Palu. (2) Pengembangan Karir berpengaruh secara parsial terhadap Kinerja Pegawai BPJS Kesehatan Cabang Palu. (3) Manajemen Konflik berpengaruh secara parsial terhadap Kinerja Pegawai BPJS Kesehatan Cabang Palu. Penelitian ini menggunakan penelitian kuantitatif yang merupakan penelitian yang bertujuan untuk menjelaskan hubungan antara dua variabel atau lebih dengan sampel 36 pegawai dan teknik analisis data yang digunakan adalah regresi linear berganda. Hasil analisis dan pengujian hipotesis dapat disimpulkan bahwa: (1) Berdasarkan hasil uji regresi diperoleh sig. F sebesar 0,00 < 0,05, yang dapat diartikan bahwa variabel Pengembangan Karir dan Manajemen Konflik berpengaruh secara serempak terhadap Kinerja Pegawai. (2) Berdasarkan hasil uji regresi diperoleh variabel Pengembangan Karir memiliki tingkat signifikansi t sig. 0,000 < α 0,05, yang dapat diartikan bahwa variabel Pengembangan Karir secara parsial berpengaruh signifikan terhadap Kinerja Pegawai. (3) Berdasarkan hasil uji regresi diperoleh variabel Manajemen Konflik memiliki tingkat signifikansi t sig. 0,000 < α 0,05 yang dapat diartikan bahwa variabel Manajemen Konflik secara parsial berpengaruh signifikan terhadap Kinerja Pegawai.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".