The Number Needed to Enjoin As a Novel Metric to Evaluate the Degree of Infringement of Civil Rights: A Case Study Using Seat Belt Laws
Bibliographic record
Abstract
Background: Road traffic accidents are the leading cause of accidental death in the United States and are projected to be the fourth leading cause of death worldwide by 2030. However, many jurisdictions face resistance to seat belt laws on principle and legislators have been hesitant to tighten existing laws or strengthen enforcement. Using seat belt laws in Canada and the US as a case study, I introduce the number needed to enjoin to help illustrate the degree to which a law infringes on a civil right, such as primary and secondary seat belt law. I discuss how the metric can help illuminate how different jurisdictions value freedom from a law, and further show how the metric can be used to justify certain policy decisions from state to state. Methods and Findings: I utilized Transport Canada’s National Collision Database to conduct a retrospective cohort analysis on 714,239 motor vehicle occupants over age 10 involved in a police-reported collision between 1999-2012. A multinomial logistic regression model was used to determine the effectiveness of seat belts in preventing a fatality or hospital admission within 30 days of the collision. A sensitivity analysis was performed to examine the impact of air bags on our findings. I also developed a number needed to enjoin as a metric to assist legislators in assessing the impact of strengthened laws on individual liberties. After adjusting for various person and collision-level characteristics, non-belted occupants were 17.7 (99% CI 15.3-20.5) times more likely to be killed and 6.3 (99% CI 5.8-6.8) times more likely to be hospitalized than belted occupants involved in a collision. The sensitivity analysis examined the effect of air bags on our findings found belted. I found the number needed to enjoin for a jurisdiction such as New Hampshire with no seat belt law to be 113. Conclusions: The benefits of seat belts in preventing fatalities and injuries requiring hospitalization have been underestimated. Legislators ought to consider the significant benefits compared to the tradeoffs against personal liberties to improve road safety. The number needed to enjoin may be a useful aid to assist policymakers in assessing the tradeoffs.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".