Injuries and Health Issues Occurring During Offshore Transoceanic Sailing: A Survey of Recreational Sailors and Cruisers
Bibliographic record
Abstract
Introduction Most of the literature on sailing injuries is centered on competitive sailing, often involving a single regatta. The aims of this study were to provide a description of the types of injuries and illnesses sustained during amateur offshore cruising events, estimate their incidence, and investigate potential risk factors for injuries. Methods We conducted a cross-sectional survey of self-reported sailing-related injuries and health issues during 4 different events organized by the World Cruising Club between 2014 and 2015. Prior to departure, sailors received an injury or health issue report form to complete during their sailing event. Questionnaires were then collected at the end of each event. Bivariable (Student’s t tests and χ 2 tests) and mutilvariable logistic regression were used to study the associations among injuries, health issues, and the characteristics of sailors or sailboats. Results The incidence of injuries and health issues among the respondents was 1.08 and 1.01 per 10,000 nautical miles, respectively. Smaller boats ( P<0.001) and crews with less experience with the current boat ( P<0.001) were associated with reporting of more injuries. Most of the injuries were reported during favorable weather conditions. Health issues were more frequent on smaller boats and with women ( P=0.008), who reported significantly more seasickness ( P<0.001), anxiety ( P=0.037), and skin rash/fungal infection ( P=0.021). Conclusions Injuries and health issues are relatively common among amateur offshore recreational sailors, but severe injuries are rare. Smaller boats and having less experience in sailing with the current boat were associated with more injuries. Preventive strategies should include a sailing experience requirement on the boat being sailed for all crew members, increasing the minimum boat size requirement for sailing events, and mandatory first-aid training prior to a cruising event for all crew members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".