Same‐level fall injuries in US workplaces by age group, gender, and industry
Bibliographic record
Abstract
BACKGROUND: As the workforce ages, occupational injuries from falls on the same level will increase. Some industries may be more affected than others. METHODS: We conducted a cross-sectional study using data from the Bureau of Labor Statistics to estimate same-level fall injury incidence rates by age group, gender, and industry for four sectors: 1) healthcare and social assistance; 2) manufacturing; 3) retail; and 4) transportation and warehousing. We calculated rate ratios and rate differences by age group and gender. RESULTS: Same-level fall injury incidence rates increase with age in all four sectors. However, patterns of rate ratios and rate differences vary by age group, gender, and industry. Younger workers, men, and manufacturing workers generally have lower rates. CONCLUSIONS: Variation in incidence rates suggests there are unrealized opportunities to prevent same-level fall injuries. Interventions should be evaluated for their effectiveness at reducing injuries, avoiding gender- or age-discrimination and improving work ability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".