Aging Baby Boomers—A Blessing or Challenge for Driver Licensing Authorities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».