PW 2267 Survey of public knowledge regarding vehicle submersion: sinking characteristics, escape strategies and public advice
Bibliographic record
Abstract
<h3>Purpose</h3> Vehicle submersion has one of the highest fatality rates for any type of single-vehicle incidents, accounting for up to 10% of drownings in industrialized nations. A survey was conducted to determine current public Knowledge, Attitudes and Practice regarding vehicle submersion accidents, escape strategies and protocols. <h3>Method</h3> Eighty-two respondents were surveyed at a public event in Winnipeg, Canada. <h3>Results</h3> Although most respondents (87%) had knowledge of vehicle submersion events, most (86.6%) felt there was minimal risk of being involved in a vehicle submersion personally, and most (82%) thought that they would likely survive such an event. Most respondents (90%) selected a ‘successful’ initial action (e.g., SEATBELTS off, or WINDOWS open) that could lead to escape and survival during vehicle submersion. However, other responses indicated the chances of completing an entire self-rescue sequence (SEATBELTS off, WINDOWS open, OUT, CHILDREN first; SWOC) is less likely. In the event that a window needs to be broken, the chances of success decrease. Only 70% of respondents appropriately chose a side window to break if necessary (front and rear windshields are laminated and cannot be broken). Even less (57%) identified objects to use, that could likely break a side window. Only 13 (16%) respondents had a proper window breaking tool in their vehicle, and none had their tool installed in the best location that is visible and reachable (hanging from the rearview mirror). <h3>Conclusions</h3> Most people were aware of vehicle submersions. Few respondents felt this scenario posed a significant risk to themselves, and there was limited knowledge related to vehicle sinking characteristics and the SWOC escape strategy. Thus, more work is required for further research and public education initiatives to prevent vehicle submersion incidents, and to teach the ‘SEATBELTS off, WINDOWS open, OUT, CHILDREN first’ protocol, to prevent vehicle submersion deaths. <h3>Funding</h3> NSERC, Canada.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".