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PW 2267 Survey of public knowledge regarding vehicle submersion: sinking characteristics, escape strategies and public advice

2018· article· en· W2893499266 on OpenAlexaffabout
Gordon G. Giesbrecht, Gerren K. McDonald, Cheryl Moser, Kartik Kulkarni

Bibliographic record

VenueAbstracts · 2018
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsSubmersion (mathematics)Poison controlForensic engineeringEngineeringComputer securityPsychologyMedical emergencyComputer scienceMedicineMathematics

Abstract

fetched live from OpenAlex

Purpose 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. Method Eighty-two respondents were surveyed at a public event in Winnipeg, Canada. Results 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). Conclusions 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. Funding 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.007

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.

Opus teacher head0.070
GPT teacher head0.331
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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