Age related differences in work injuries and permanent impairment: a comparison of workers’ compensation claims among adolescents, young adults, and adults
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that adolescent workers are at greater risk for work injury. AIMS: To investigate the severity of work injuries across age groups. METHODS: Workers' compensation records were used to examine work related injuries among adolescents (15-19 years old), young adults (20-24 years old), and adults (25+ years old) between 1993 and 2000. The incidence of compensated injuries was calculated for each age group and compared by gender, industry, and type of injury. The presence and degree of permanent impairment in each age group was also examined. RESULTS: For males, adolescents and young adults had higher claim rates than adults. For females, adults had the highest claim rates and young adults the lowest. Rates of permanent impairment indicated that age was positively associated with severity of injury. CONCLUSIONS: Indicators of health consequences, in particular presence of permanent impairment, provide preliminary evidence that compensated work injuries sustained by youth are not as serious as injuries sustained by adults. Nevertheless, there was evidence that some young workers sustain injuries that have long term consequences. Documenting the consequences of the injuries that young workers sustain has implications for secondary prevention efforts and health services policy.
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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 | 0.003 |
| 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".