Predictors of Motor Vehicle Collision Injuries Among a Nationally Representative Sample of Canadians
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine predictors of subsequent motor vehicle collision injuries, with a particular focus on health-related variables, using the longitudinal dataset from the Canadian National Population Health Survey (NPHS) for the years 1994-2002. METHODS: Multiple logistic regression analysis was used to determine the relations between motor vehicle collision injury and four risk factors: binge drinking, health status, distress, and medication use. Age and sex were included as control variables. The total sample size was 14,529. RESULTS: A higher percentage of females and younger persons reported a motor vehicle collision injury. Binge drinkers, respondents with poor health, respondents with distress, and respondents reported using two or more medications reported a higher percentage of subsequent injuries. Logistic regression analysis found that persons with poorer health status and persons who used more medications had higher odds of motor vehicle injuries. Only one statistically significant interaction effect was found: alcohol bingeing and medication use. CONCLUSIONS: Among a nationally representative sample of Canadians, various demographic and risk factors predict subsequent injuries. Given that this number represents a considerable economic burden, this study underscores the need for continued research and countermeasures on alcohol, drugs, and driving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".