Systematic review: Lost-time injuries in the US mining industry
Bibliographic record
Abstract
BACKGROUND: The mining industry is associated with high levels of accidents, injuries and illnesses. Lost-time injuries are useful measures of health and safety in mines, and the effectiveness of its safety programmes. AIMS: To identify the type of lost-time injuries in the US mining workforce and to examine predictors of these occupational injuries. METHODS: Primary papers on lost-time injuries in the US mining sector were identified through a literature search in eight health, geology and mining databases, using a systematic review protocol tailored to each database. The Critical Appraisal Skills Programme (CASP), Framework of Quality Assurance for Administrative Data Source and the Cochrane Collaboration 'Risk of bias' assessment tools were used to assess study quality. RESULTS: A total of 1736 articles were retrieved before duplicates were removed. Fifteen articles were ultimately included with a CASP mean score of 6.33 (SD 0.62) out of 10. Predictors of lost-time injuries included slips and falls, electric injuries, use of mining equipment, working in underground mining, worker's age and occupational experience. CONCLUSIONS: This is the first systematic review of lost-time injuries in the US mining sector. The results support the need for further research on factors that contribute to workplace lost-time injuries as there is limited literature on the topic. Safety analytics should also be applied to uncover new trends and predict the likelihood of future incidents before they occur. New insights will allow employers to prevent injuries and foster a safer workplace environment by implementing successful occupational health and safety programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".