The Dangers of Distracted Driving: A Substudy of Patient Perception Data From the DRIVSAFE Observational Study
Bibliographic record
Abstract
OBJECTIVE: To determine how fracture clinic patients perceive the dangers of distracted driving. DESIGN: Analysis of patient perception subset data from the original DRIVSAFE study; a large, multicenter cross-sectional study, surveying fracture clinic patients about distracted driving. SETTING: Four Level 1 Canadian trauma center fracture clinics. PATIENT SELECTION CRITERIA: English-speaking patients with a valid Canadian driver's license and a traumatic musculoskeletal injury sustained in the past 6 months. OUTCOME MEASURES AND COMPARISONS: Primary outcome was patients' safety ratings of driving distractions. As per the original DRIVSAFE study, patients were categorized as distraction-prone or distraction-averse using their questionnaire responses and published crash-risk odds ratios (ORs). A regression analysis was performed to identify associations with unsafe driving perceptions. RESULTS: The study included 1378 patients, 749 (54.3%) male and 614 (44.6%) female. The average age was 45.8 ± 17.0 years (range 16-87). Sending electronic messages was perceived as unsafe by 92.9% (1242/1337) of patients, while reading them was seen as unsafe by 81.2% (1086/1337). Approximately three-quarters of patients viewed making (78.9%, 1061/1344) and accepting (74.8%, 998/1335) calls on handheld mobile phones as unsafe. However, 31.0% (421/1356) of patients believed that they had no differences in their driving ability when talking on the phone while 13.1% (175/1340) reported no driving differences when texting. Younger age (OR, 0.93 [95% confidence interval (CI) 0.90-0.96], P < 0.001), driving experience (OR, 1.06 [95% CI 1.02-1.09], P < 0.001), and distraction-prone drivers (OR, 3.79 [95% CI 2.91-4.94], P < 0.001) were associated with unsafe driving perceptions. CONCLUSIONS: There is a clear association between being prone to distractions and unsafe driving perceptions, with distraction-prone drivers being 3.8 times more likely to perceive driving distractions as safe. This information could potentially influence the appropriate delivery and content of future educational efforts to change the perception of driving distractions and thereby reduce distracted driving. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".