Public knowledge, attitudes and practices of vehicle submersion incidents: a pilot study
Bibliographic record
Abstract
Vehicle submersions account for up to 10% of all drownings in high-income countries. Reports indicate that occupants may be conscious and functional, but possibly making incorrect decisions for self-rescue leading to drowning. This study investigated current public knowledge, attitudes and practices regarding vehicle submersion incidents and to determine if individuals, who are aware of educational efforts regarding vehicle submersions, indicated better responses. A knowledge, attitude and practice (KAP) survey was developed based on previous findings and guidelines from Operation ALIVE (Automobile submersion: Lessons In Vehicle Escape) for vehicle submersion incidents. The majority of respondents (87%) had knowledge of vehicle submersions from the media, but they were not aware (94%) of an effective self-rescue protocol. Respondents felt they had low risk of involvement in a vehicle submersion, and that the chance of survival was likely. Most respondents selected a “successful” initial action for escape; however, other responses indicate the chances of completing a successful self-rescue sequence was less likely. Only 45% of respondents were “aware” of Operation ALIVE educational initiatives, and this awareness did not generally produce better responses. Public understanding of vehicle submersion incidents is low and current public education efforts have not increased awareness in the severity or the urgency for performing self-rescue in this scenario. Simply increasing public knowledge of “SWOC” (“SEATBELTS” off, “WINDOWS” open, “OUT” immediately, “CHILDREN” first) would help to decrease the high fatality rate associated with this type of road traffic accident.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".