Bibliographic record
Abstract
Objective: The objective of this study was to analyze conditions relating to road traffic safety in Canada from 2003 to 2017. We investigated the RTAs resulting in fatal or serious injuries by comparing three five-year time clusters, 2003 to 2007, 2008 to 2012, and 2013 to 2017, in accordance with Canadian national road safety strategies. Subjects and Methods: This was a retrospective study. Data was collected by using the Canadian Motor Vehicle Traffic Collision Statistics reports from 2003 to 2017. Information regarding impaired driving in Canada was obtained from the Canadian Centre on Substance Use and Addiction, while seatbelt usage was obtained from the Canadian Council of Motor Transport Administrator’s NORP reports. The research focuses on three consecutive five-year periods: 2003-2007, 2008-2012, and 2013-2017. Analysis done to compare the difference among the three time-period clusters for the major RTA outcomes: fatal collisions, fatalities, injuries and serious injuries. Descriptive data such as age, location and driving impairment, helped illustrate RTA demographics to aid in future study methodologies. Results: During 2003 to 2007, the median number of fatalities was 2,768 (2,755.8-2,877.0). This number made a significant decrease going into the second time cluster with a median of 2,216 (2,062.8-2,286.3). When the final time period is taken into account, with a median of 1889 (1846.3-1909.8), there is a decrease of 879 fatalities. The initial five-year time period showed a median of 15,605 (14,930.5-15,870.0) serious injuries. There was a decrease to 11,796 (11,072.0-12,179.0) in the second data cluster from 2008 to 2013. The third time period showed a median of 10,662 (10206.5-10783.8) serious injuries. When comparing the three five-year periods in terms of fatal collisions, fatalities, injuries and serious injuries with the Kruskal-Wallis test, each was statistically significant (P=0.002). Conclusion: There was a significant decrease in the amount of injuries, serious injuries, fatal collision and fatalities when compared as three consecutive sets of five-year clusters. The average age of fatalities due to motor vehicle collisions is the 65 and over age group, where more injuries were sustained mostly by the 24 to 34 age group. Impaired driving due to alcohol has seen a decrease, while drug-induced road fatalities unfortunately made an increase.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".