Occupational injury in the United Arab Emirates: epidemiology and prevention
Bibliographic record
Abstract
BACKGROUND: The United Arab Emirates (UAE) is developing rapidly, with many foreign construction, farm and industrial workers. AIMS: To assess the epidemiology of occupational injury hospitalizations using a trauma registry. METHODS: Surgical admissions from March 2003 to April 2005 were recorded in the registry at the main trauma hospital in Al Ain city (population 348,000). Prevention-related variables were analysed using SPSS and severity was quantified by injury severity scores (ISS). RESULTS: There were 614 occupational injury hospitalizations, an incidence of approximately 136/100,000 workers/year. Males accounted for 98% of injuries, the 25-44 age group for 69% and non-nationals for 96%. External causes included falls 51%, falling objects 15%, powered machines 11%, animal-related 7% and burns 6%. Median ISS was 4 for all six main external causes. Extremities were most frequently injured, followed by chest, head and neck, abdomen and face. Mean hospitalization duration was 9.4 days, with 36% hospitalized for >1 week. CONCLUSIONS: The main external causes were proportionately much more frequent than in industrialized countries. Effective countermeasures are needed to reduce the incidence and severity of occupational injury among vulnerable migrant workers in the UAE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".