Aging Baby Boomers—A Blessing or Challenge for Driver Licensing Authorities
Bibliographic record
Abstract
INTRODUCTION: In less than 5 years, the first wave of baby boomers will begin turning 65, with the last wave of boomers entering their senior years in January 2029. Currently, boomers make up a significant percentage of the population in Canada, the United States, and other developed countries. The baby boom generation has had a profound impact on our society over the last six decades, and this large cohort will continue to exert its influence for several decades to come. Central to this article is the rapid growth in the number of persons 65 years of age and older, beginning in 2011, with a corresponding increase in the number of older drivers. The demographic shift has important implications for licensing authorities, the regulatory bodies charged with licensing and 'fitness to drive' decisions. OBJECTIVES: The objectives of this paper are to summarize the published scientific literature on licensing policies and procedures currently in use for older drivers, discuss their limitations, and provide recommendations for meeting the upcoming challenges of an aging baby boomer population of drivers. METHOD: Online searches were conducted using the following databases: PsycINFO, MEDLINE, Scopus, and TRIS. Google and Google Scholar also were searched for scientific articles. References identified from database and online searches were examined for relevant articles. RESULTS: A number of studies have investigated the utility of different licensing policies and procedures for identifying older drivers who may be at risk for impaired driving performance. Overall, results suggest that current policies and procedures are ineffective in identifying high-risk older drivers. The results also emphasize the need for a different approach for the identification of high risk older drivers by licensing agencies. Recommendations to assist with that goal are provided. CONCLUSIONS: The aging of the baby boomer population, combined with the projected high crash rates for this cohort of drivers as it moves through the senior years, underscores the need for cost-effective, accurate, and efficient methods for identifying and assessing the subgroup of older drivers whose driving has declined to an unsafe level. That subgroup consists of individuals with medical conditions (and treatments) affecting driving performance. The demographic shift has been a blessing for licensing authorities in that it has created awareness of the need for a reexamination of licensing policies and procedures designed to identify those older drivers who may no longer be safe to drive. If that awareness becomes translated into effective policies and procedures that appropriately target the medically at-risk/impaired older driver rather than the older driver per se, the result will be an increase in the safety and mobility of the older driving population and increased public safety overall. However, a continued focus on older drivers rather than medically at-risk drivers will result in a costly, ineffective, and overburdened system.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".