Driving Difficulties and Adaptive Strategies: The Perception of Individuals Having Sustained a Mild Traumatic Brain Injury
Bibliographic record
Abstract
Introduction. After a mild traumatic brain injury (mTBI), individuals quickly resume driving. However, relatively little is known about the impact of mTBI on driving ability and, notably, on the perceived influence of postconcussive symptoms on driving. Hence, the objective of this study was to document the perception of driving abilities in individuals with mTBI. Method. Twenty-seven drivers with mTBI were interviewed to document their perception regarding their driving abilities. Both driving-related difficulties and compensatory strategies used to increase driving safety were documented. A mixed quantitative and qualitative analysis of the data was completed. Results. 93% of participants reported at least one difficulty perceived as having an impact on everyday activities. Most frequently named problems affecting driving were fatigue and reduced concentration. In addition, 74% of participants had adapted their driving or developed strategies to compensate for driving difficulties. Discussion/Conclusion. Postconcussive symptoms have repercussions on driving ability. However, people with mTBI tend to be aware of their difficulties and develop, over time, adaptive strategies. Preventive measures are thus warranted to increase health care professionals' awareness of the potential consequences of mTBI on driving ability and to promote guidelines for the safe resumption of driving after injury.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".