The healthcare professionals’ role in road safety in primary care settings in Canada: A qualitative study
Bibliographic record
Abstract
Abstract Background: During the aging process, many seniors will see their ability to drive affected, particularly due to the high prevalence of health problems. To this end, healthcare professionals working in primary care are in frequent contact with older drivers at risk and for whom interventions have to be undertaken. However, little is known about their role, despite the major implications with legal authorities in assessing fitness to drive and in reporting unsafe drivers, especially in a Quebec setting. The purpose of this study was to describe the role of primary healthcare professionals in road safety. Methods: Within a descriptive qualitative design, semi-structured interviews were undertaken with 27 primary healthcare professionals (registered nurses (n=16), occupational therapists (n=7), and family physicians (n=4)). Using an inductive approach, qualitative data was analyzed with the content analysis method. Results: The road safety role of the primary healthcare professionals interviewed varied between professions, depending on their respective expertise. Furthermore, they work in close collaboration and interdisciplinarity. Their role is divided into two main components, namely screening and assessment of older drivers’ driving abilities, as well as of the support and accompaniment offered to seniors following the loss of their driving license. Their role also differs in terms of legally declaring older drivers fit or not to drive. Some professionals integrate preventive interventions into their practice, aimed either to keep seniors driving as long as possible, or to prepare seniors to stop driving. Several healthcare professionals reported experiencing challenges, especially during the process of assessing their elderly patients’ fitness to drive. Conclusions: This study’s results will help organizations to develop training programs allowing primary healthcare professionals to improve their practice with elderly drivers and their loved ones.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".