Injury and Return to Work Among Maritime Workers in British Columbia, Canada
Bibliographic record
Abstract
Maritime occupations encompass seafaring, fishing, marine aquaculture, and longshore work. These non-standard occupations tend to be hazardous with high injury rates. They are associated with varying levels of seasonality, shift work, geographic mobility, and different types of remuneration, posing unique challenges when recovering from work-related injury and illness. Occupational health and safety is under-researched in these sectors. Furthermore, little research exists on return to work (RTW) after injury among maritime workers. This paper presents findings from a mixed methods research program designed to provide insight into injury, compensation and RTW experiences among maritime workers in the Canadian province of British Columbia (BC). Research methods include the analysis of provincial workers' compensation data, data from an anonymous online survey of injured/ill BC maritime workers and from semi-structured interviews with injured workers and key informants. Analysis of workers' compensation data shows high rates of serious injuries, longer disability duration, and high rates of deemed RTW, particularly in fishing. Survey findings suggest a relatively low percentage of workers file claims for workers' compensation to WorkSafeBC. Interview data highlight some of the challenges that may explain under-reporting, longer disability duration, and relatively poor RTW outcomes. Policy relevant concerns and areas for future research relevant to understanding and addressing some of the identified RTW challenges associated with these sectors are presented.
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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 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".