Bans on Cellphone Use While Driving and Traffic Fatalities in the United States
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: As of January 2020, 18 of 50 US states comprehensively banned almost all handheld cellphone use while driving, 3 states and the District of Columbia banned calling and texting, 27 states banned texting on a handheld cellphone, and 2 states had no general cellphone ban for all drivers. However, it remains unknown whether these bans were associated with fewer traffic deaths and whether comprehensive handheld bans are more effective than isolated calling or texting bans. We evaluated whether cellphone bans were associated with fewer driver, non-driver, and total fatalities nationally. METHODS: We conducted a longitudinal panel analysis of traffic fatality rates by state, year, and quarter. Population-based rate ratios and 95% CIs were estimated comparing state-quarters with and without cellphone bans. RESULTS: From 1999 through 2016, 616,289 persons including 344,003 drivers died in passenger vehicle crashes in the United States. Relative to no ban, comprehensive handheld bans were associated with lower driver fatality rates (adjusted rate ratio aRR = 0.93, 95% CI = 0.90, 0.97) but not for non-driver fatalities (aRR = 1.01, 95% CI = 0.95, 1.07) or total fatalities (aRR = 0.98, 95% CI = 0.94, 1.01). We found no differences in driver fatalities for calling-only bans (aRR = 1.00, 95% CI = 0.97, 1.03), texting-only bans (aRR = 1.02, 95% CI = 0.99, 1.05), texting plus phone-manipulating bans (aRR = 0.99, 95% CI = 0.93, 1.04), or calling and texting bans (aRR = 0.98, 95% CI = 0.88, 1.09). CONCLUSIONS: Comprehensive handheld bans were associated with fewer driver fatalities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 it