Disparities in occupational injury hospitalization rates in five states (2003–2009)
Bibliographic record
Abstract
BACKGROUND: Achievement of health equity and elimination of disparities are overarching goals of Healthy People 2020, yet there is a paucity of population-based data regarding race/ethnicity-based disparities in occupational injuries. METHODS: Hospital discharge data for five states (Arizona, California, Florida, New Jersey, and New York) were obtained from the Healthcare Cost & Utilization Project (HCUP) for 2003-2009. Age-adjusted rates and trends for work-related injury hospitalizations were calculated using negative binomial regression (reference category: non-Latino white). RESULTS: Latinos were significantly more likely to have a work-related traumatic injury hospitalization. The disparity for Latinos was greatest for machinery-related hospitalizations. Latinos were also more likely to have a fall-related hospitalization. African-Americans were more likely to have an occupational assault-related hospitalization, but less likely to have a fall-related hospitalization. CONCLUSIONS: We found evidence of substantial multistate disparities in occupational injury-related hospitalizations. Enhanced surveillance and further research are needed to identify and address underlying causes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".