What Factors Predict Subsequent Motor Vehicle Injuries? Analysis of the longitudinal Canadian National Population Health Survey
Bibliographic record
Abstract
Objective: The purpose of this study was to examine predictors of subsequent motor vehicle collision injuries (MVCs), 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: Path analysis technique was used to determine the relations between MVC injury and four risk factors: binge drinking; health status; distress; and medication use. The three demographic variables, age at ‘baseline’, sex, and immigration status, were added into the model as control variables. Three age groups were examined: young = 12-29.9; middle-aged = 30-59.9 and old = 60-85 years of age. The total sample size was 16 093. Results: A higher percentage of males, younger and Canadian born persons reported a MVC injury. Binge drinkers, respondents with poor health, respondents with distress and who reported using pain relievers, tranquillizers, antidepressants, codeine, Demerol, morphine, sleeping pills, and two or more medications reported a higher percentage of subsequent injuries. Path analysis found that among younger individuals, the variable binge drinking, was the only significant
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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".