Shifting gears versus sudden stops: qualitative study of consultations about driving in patients with cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: General practitioners (GPs) report finding consultations on fitness to drive (FtD) in people with cognitive impairment difficult and potentially damaging to the physician-patient relationship. We aimed to explore GP and patient experiences to understand how the negative impacts associated with FtD consultations may be mitigated. METHODS: Individual qualitative interviews were conducted with GPs (n=12) and patients/carers (n=6) in Ireland. We recruited a maximum variation sample of GPs using criteria of length of time qualified, practice location and practice size. Patients with cognitive impairment were recruited via driving assessment services and participating general practices. Interviews were audio-recorded, transcribed and analysed thematically by the multidisciplinary research team using an approach informed by the framework method. RESULTS: The issue of FtD arose in consultations in two ways: introduced by GPs to proactively prepare patients for future driving cessation or by patients who urgently needed a medical report for an expiring driving license. The former strategy, implementable by GPs who had strong relational continuity with their patients, helped prevent crisis consultations from arising. The latter scenario became acrimonious if cognition had not been openly discussed with patients previously and was now potentially impacting on their right to drive. Patients called for greater clarity and empathy for the threat of driving cessation from their GPs. CONCLUSION: GPs used their longitudinal relationship with cognitively impaired patients to reduce the potential for conflict in consultations on FtD. These efforts could be augmented by explicit discussion of cognitive impairment at an earlier stage for all affected patients. Patients would benefit from greater input into planning driving cessation and acknowledgement from their GPs of the impact this may have on their quality of life.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".