PW 1296 Changing awareness, attitudes, and behaviours in an evidence-informed social marketing campaign to reduce recreational boating injuries in british columbia: the evidence
Bibliographic record
Abstract
Background Recreational boating-related injuries are a preventable cause of morbidity and mortality in Canada. Implementing a targeted social marketing campaign will encourage meaningful behaviour change and result in increased compliance with safe boating practices, and reduced incidence of injury. Objective To provide a foundation to guide the development of a targeted boating safety social marketing campaign, this phase of the project aimed to: (1) summarize the existing evidence related to the risk factors and interventions for recreational boating-related injury; and (2) describe the burden of recreational boating-related injury in British Columbia (BC). Methods: We conducted (1) a review of the English language literature related to recreational boating injury; and (2) a retrospective analysis of fatalities, hospitalizations, and emergency department visits caused by recreational boating-related injuries in BC from April 1, 2009 – March 31, 2014. Findings Alcohol consumption and low PFD use are widely established risk factors for recreational boating-related injury. Existing studies suggest that legislation and targeted educational initiatives may improve recreational boating safety practices and reduce injury. In BC, recreational boating-related injuries primarily occurred among adult males during the warmer months. Most injuries occurred during powerboat use. One third of individuals who were fatally injured in a recreational boating incident had consumed alcohol and the majority were not wearing a PFD at the time of the incident. Recreational boating-related deaths most frequently occurred in a lake. Conclusion A targeted social marketing campaign aimed at improving safe boating behaviors is warranted. The campaign should include messaging related to avoiding alcohol use and wearing a PFD while boating. High-risk groups for targeted messaging include males, teenagers, young adults, and middle-aged adults. The campaign should be developed with emphasis on the flat water recreational boating community, and delivery should occur during the warmer months of the year, especially July and August.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".