A descriptive epidemiological study on the patterns of occupational injuries in a coastal area and a mountain area in Southern China
Bibliographic record
Abstract
OBJECTIVES: This study compared patterns of occupational injuries in two different areas, coastal (industrial) and mountain (agricultural), in Southern China to provide information for development of occupational injury prevention measures in China. DESIGN: Descriptive epidemiological study. SETTING: Data were obtained from the Hospital Injury Surveillance System based on hospital data collected from 1 April 2006 to 31 March 2008. PARTICIPANTS: Cases of occupational injury, defined as injury that occurred when the activity indicated was work. OUTCOME MEASURES: Distribution and differences of patterns of occupational injuries between the two areas. RESULTS: Men were more likely than women to experience occupational injuries, and there was no difference in the two areas (p=0.112). In the coastal area, occupational injury occurred more in the 21-30-year age group, but in the mountain area, it was the 41-50-year age group (p<0.001). Occupational injuries in the two areas differed by location of hometown, education and occupation (all p<0.001). Occupational injuries peaked differently in the month of the year in the two areas (p<0.001). Industrial and construction areas were the most frequent locations where occupational injuries occurred (p<0.001). Most occupational injuries were unintentional and not serious, and patients could go home after treatment. The two areas also differed in external causes and consequences of occupational injuries. CONCLUSIONS: The differing patterns of occupational injuries in the coastal and mountain areas in Southern China suggest that different preventive measures should be developed. Results are relevant to other developing countries that have industrial and agricultural areas.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".