Kecelakaan Kerja Berdasarkan Loss Causation Model Pada Industri Informal Pengelasan
Bibliographic record
Abstract
Angka kecelakaan kerja masih tinggi, tidak hanya pada sektor formal tapi juga pada sektor informal, salah satunya pada industri informal pengelasan. Penyebab kecelakaan berupa faktor lack of control, basic cause, dan immediate cause. Penelitian ini bertujuan untuk mengetahui faktor yang berhubungan dengan kecelakaan kerja berdasarkan Loss Causation Model. Penelitian ini menggunakan pendekatan kuantitatif, jenis observasional dengan rancang bangun cross sectional. Penilitian dilakukan di 15 tempat pengelasan di Bandung Raya. Populasi pada penelitian ini adalah pekerja sektor informal pengelasan (juru las) di wilayah Bandung Raya. Didapatkan 75 sampel dengan teknik total sampling Pengumpulan data dilakukan dengan kuesioner penelitian, dan pedoman wawancara. Analsis data dengan uji chi-square, regresi logistik sederhana dan uji regresi logistik ganda. Hasil penelitian menunjukkan terdapat hubungan antara pogram K3, peran dan tanggung jawab, pengetahun, motivasi, pelatihan pengelasan, standar kerja, penggunaan APD, dan kepatuhan terhadap IK dengan kecelakaan kerja. Variabel paling berpengaruh terhadap kecelakaan kerja adalah motivasi keselamatan (B = 4,605). Penelitian ini menyimpulkan bahwa faktor lack of control, basic cause, dan immediate cause berhubungan dengan kecelakaan kerja. Pemilik kios pengelasan perlu bekerjasama dengan Pos UKK setempat untuk mengelola K3 di tempat kerja.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".