How much did cold shock and swimming failure contribute to drowning deaths in the fishing industry in British Columbia 1976–2002?
Bibliographic record
Abstract
BACKGROUND: The Workers' Compensation Board of British Columbia requested a retrospective analysis of all fishermen's deaths from immersion in water in British Columbia. AIMS: To identify the underlying cause of drowning and make recommendations to improve safety in the fishing industry. METHOD: Eighty-nine inshore and offshore fishing accidents were analysed. Where possible, deaths were classified into the four stages of cold-water immersion: cold shock, swimming failure, hypothermia and post-rescue collapse. Other factors that led up to the drowning were also identified. RESULTS: One hundred and thirty fishermen died from immersion between 1976 and 2002. One hundred and twenty-eight drownings were certified by the coroner as drowning or drowning/hypothermia and two were certified as cardiac event after immersion. The underlying causes of drownings were reclassified as: cold shock (5.4%), swimming failure (5.4%), hypothermia (5.4%), post-rescue collapse (0.8%), cardiac event (0.8%) and drowning/other (10%). In the remaining 72.2% of deaths, there was insufficient information to determine an underlying cause. All deaths occurred in water below 17.5 degrees C but 95% were in water less than 15 degrees C. CONCLUSIONS: Immersion in water below 15 degrees C is dangerous and this should be emphasized on marine survival courses. Accident investigators, coroners and pathologists need a common checklist to record vital data. A recommended format is included as Supplementary data available at Occupational Medicine Online. Fishermen should be educated about the dangers of sudden, unexpected immersion in cold water. Consideration should be given to making marine survival courses mandatory for fishermen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".