Road safety of older drivers and the nursing profession: A scoping review
Bibliographic record
Abstract
BACKGROUND: Population ageing will lead to an increase in the number of older drivers. The ageing process can affect older adults' driving ability. Nurses can play a major role in identifying potentially at-risk drivers and providing guidance about driving safety and cessation, but their role remains somewhat unknown. OBJECTIVE: This scoping review aims to present the scientific literature in relation to the nursing profession in the field of road safety. DESIGN: CINAHL and PubMed databases were used to identify quantitative, qualitative, mixed methods or clinical practice guideline articles that referred to the role of nurses in older people's road safety and were published in English or French languages between January 1990 and August 2020. Ten (10) articles met the inclusion criteria and were analysed. RESULTS: Analysis of included articles revealed one main theme: Nurses' and NPs' roles in the mobility continuum. Results showed that nurses and nurse practitioners (NPs) often see older drivers in their clinical practice and that they have the competencies to screen and assess their fitness to drive. They are well positioned to discuss age-related changes, fitness to drive and a driving retirement plan with their older patients, but they are not confident when addressing this issue. Few older adults have discussed their driving abilities with healthcare professionals, but they would be willing to discuss this if the subject were brought up. CONCLUSIONS: This scoping review highlighted the paucity of research addressing the role of the nurse in road safety. More research is needed to adequately document this role.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".