Ethics of Clinical Decision-Making for Older Drivers: Reporting Health-Related Driving Risk
Bibliographic record
Abstract
The number of older drivers will continue to increase as the population ages. Health care professionals have the responsibility of providing care and maintaining confidentiality for their patients while ensuring public safety. This article discusses the ethics of clinical decision-making pertaining to reporting health-related driving risk of older drivers to licensing authorities. Ethical considerations inherent in reporting driving risk, including autonomy, confidentiality, therapeutic relationships, and the uncertainty about determining individual driving safety and risk, are discussed. We also address the moral agency of reporting health-related driving risk and raise the question of whose responsibility it is to report. Issues of uncertainty surrounding clinical reasoning and concepts related to risk assessment are also discussed. Finally, we present two case studies to illustrate some of the issues and challenges faced by health care professionals as they seek to balance their responsibilities for their patients while ensuring road safety for all citizens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.169 | 0.286 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".