The cost‐effectiveness of a law banning the use of cellular phones by drivers
Bibliographic record
Abstract
OBJECTIVE: To assess the cost-effectiveness of a law banning the use of cellular phones by drivers in the Canadian province of Alberta. METHOD: Cost-effectiveness analysis using a probabilistic decision-analytic model and publicly available data. We adopted a societal perspective. Health gains were measured in terms of quality-adjusted life-years. Costs include those associated with awareness raising, enforcement and the welfare loss associated with the reduction in cellular phone use, less savings in health care and other costs associated with automobile accidents. RESULTS: A ban promotes health and releases resources worth more than the costs. There is an 80% chance that a ban will be 'cost saving', and a 94% chance that a ban will cost less than Can$50,000/QALY. The results are sensitive to the additional risk posed by cellular phone use while driving, and the rate and pattern with which drivers comply with a ban. CONCLUSION: Under our base line assumptions a cellular phone ban is likely to be cost saving from a societal perspective. The results are sensitive to parameters for which there is very little information or for which the available information is contradictory.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".