Analysis Factors Associated with Work Accidents Among House Construction Workers in Karang Ayu
Bibliographic record
Abstract
Globally, work-related injuries are a significant public health and economic issue, with approximately 5-7% of all deaths in industrialized countries caused by work-related injuries. Each year, work accidents cause 350,000 deaths and 270 million non-fatal serious injuries. Due to its high burden of occupational hazards, the construction industry is more at risk than other industries. Compared with workers in different occupations, building construction workers are three to four times more likely to die and twice as likely to be injured than workers in other occupations. This study aims to analyze the factors associated with the incidence of work accidents among house construction workers in Karangayu, Semarang Barat. This study used descriptive qualitative methods, including observation, interviews, and questionnaires. The result of this study is that factors can cause work accidents, such as age, unsafe actions such as not using PPE properly, and worker knowledge, which leads to unsafe actions. Still, in this observation, there are driving factors behind workers' unsafe actions, one of which is the non-compliance of house construction workers in Karangayu, Semarang.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".