Rethinking Driving Against Medical Advice: The Situated Nature of Driving After Stroke
Bibliographic record
Abstract
Background: As stroke can result in functional impairments that impact driving ability, many jurisdictions mandate a 30-day period of driving restriction post-stroke. However, between 26% and 38% of clients drive against medical advice during this period. Purpose: Informed by critical reflexivity of the literature and the first author's practice, this critical analysis paper (1) explicates and critiques how adherence to guidelines regarding driving after stroke in the first 30 days is conceptualized in individualistic, biomedically centred research and (2) argues for expanded understandings of driving based on a transactional occupational perspective. Key Issues: Individualistic, biomedical perspectives view driving against medical advice as an individually located phenomenon, generating partial understandings and individually focused solutions. Re-conceptualizing driving after stroke as a transactional occupational choice provides a productive basis for understanding and addressing driving within practice and research. Implications: Concepts from occupational science can generate new insights for research and client-centred practice regarding driving following stroke.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".