Work-Time Exposure and Acute Injuries in Inshore Lobstermen of the Northeast United States
Bibliographic record
Abstract
The objective of this study was to inform efforts to reduce risk for musculoskeletal disorders among commercial lobstermen by characterizing and quantifying injuries that occur to people while harvesting lobsters commercially in the Northeast United States. This study aimed to estimate a denominator of exposure to lobstering in full-time equivalents (FTE), to estimate a fatality rate, and to calculate incidence rates for acute injuries within the sample population. Captains were randomly selected from those licensed to fish in Maine and Massachusetts. Data on work exposure and injuries with rapid onset that occurred on the boat ("acute injuries") were collected using a survey, which was administered quarterly via phone or face-to-face interview with the captain. The quarterly survey assessed the number of weeks worked during the quarter, average crew size, number of trips per week, and average trip length in hours. In addition, this survey captured relevant information (body segment affected, type of injury, and whether treatment was received) on all acute injuries occurring during the quarter. FTE were estimated using fishermen days and fishermen hours. The annual FTE estimated using days was 2,557 and using hours was 2,855. As expected, the summer months (3rd quarter) had the highest FTE and the winter (1st quarter) the lowest FTE. Fall (4th quarter) and spring (2nd quarter) ranked second and third, respectively. The incidence rates for all injuries (49.7/100 FTE) and injuries requiring treatment (15.0/100 FTE) were much higher than those reported in other studies of fishing that used Coast Guard data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".