Physician’s Role in Managing Driver’s Licenses for Older Adults: Implications for Korea
Bibliographic record
Abstract
The increasing proportion of older adult drivers presents a growing road safety challenge in Korea, highlighted by a significant rise in traffic accidents involving individuals aged 65 years and older. In response, Korean authorities have implemented mandatory cognitive and physical assessments for license renewal, complemented by community-based educational programs. However, international comparisons reveal that the involvement of physicians as gatekeepers in assessing driver fitness is more robust in the United States and Canada, where legal frameworks either permit or require healthcare professionals to report medically at-risk drivers to licensing authorities. These systems balance public safety with patient confidentiality by providing statutory protection for reporting physicians, though barriers such as legal ambiguity, concerns over liability, and inconsistent practices persist. Evidence suggests that mandatory reporting laws increase physician engagement and reporting rates, yet emotional and ethical dilemmas may hinder compliance. In Korea, strengthening the physician's role in the driver license management system-supported by legal immunity and clear guidelines-could enhance early identification of at-risk drivers and reduce accident rates among older adults. A multidisciplinary approach, involving secondary assessments by occupational therapists and licensing authorities, is recommended to ensure objective evaluation of driving competence. Adopting a reporting model may further clarify responsibilities and improve outcomes. Ultimately, integrating physicians more actively into the licensing process is essential for safeguarding both older adult drivers' autonomy and public safety in an aging society.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".