Noncompliance with seat-belt use in patients involved in motor vehicle collisions.
Bibliographic record
Abstract
BACKGROUND: Seat-belt compliance in trauma patients involved in motor vehicle collisions (MVCs) appears low when compared with compliance of the general public. In this study we wished to define the relative frequency of seat-belt use in injured Canadian drivers and passengers and to determine if there are risk factors particular to seat-belt noncompliance in this cohort. METHODS: We identified trauma patients who were involved in MVCs over a 24-month period and contacted them 2-4 years after the injury by telephone to administer a standardized survey. Potential determinants of seat-belt noncompliance were compared with the occurrence of an MVC by multiple logistic regression. RESULTS: Seat-belt noncompliance in 386 MVC patients was associated with drinking and driving, youth, speeding, male sex, being a passenger, smoking, secondary roads, rural residence, low level of education, overnight driving, having no dependents, licence demerit points, previous collisions, unemployment and short journeys. There was an increase in seat-belt awareness and a decrease in self-rated driving ability after the MVC. CONCLUSIONS: Factors that indicate poor driving habits (alcohol, speeding, previous MVCs and driving offences) also predict seat-belt noncompliance. Injury prevention programs should selectively target these high-risk drivers to improve seat-belt compliance and limit associated injury and consumption of health care resources.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".