Do Hands-Free Cell Phone Conversation While Driving Increase the Risk of Cognitive Distraction Among Drivers? A Cross-Over Quasi-Experimental Study
Bibliographic record
Abstract
Background: Background: Conversation over the cell phone while driving is a known risk factor for road traffic crash. Using handsfree to talk on the cell phone may remove visual and manual distraction yet not the cognitive distraction. Objectives: The purpose of this study was to better understand the mechanisms of cognitive distraction due to hands-free cell phone conversation (HFPC) while driving. Methods: Twelve male and 12 female University students in Tehran, Iran, were selected via the consecutive convenient sampling method, and randomly assigned to one of the following administrations of cross-over quasi-experimental study design, during year 2016: Administration 1, participants performed the tests while involved in HFPC, took rest for 60 minute, and then performed the tests another time without HFPC; Administration 2, participants performed the tests without HFPC, took rest, and then performed the tests another time with HFPC. Each participant moved to the other administration after 7 to 10 days. The Vienna test system (VTS) was used to measure simple and choice reaction time, selective attention, visual orientation, and visual memory. Linear regression analysis was used to study the change in test scores due to HFPC. Results: The mean age of participants was 27.1±5.3 years. A history of road traffic crash (regardless of severity) was reported among 9 (37.5) participants in the previous year. Hands-free cell phone conversation while driving was directly associated with mean time correct rejection score (P < 0.01) (selective attention), omitted response (P < 0.01) and median reaction time (P < 0.01) (choice reaction time), and mean reaction time (P < 0.01) (Simple reaction time). Moreover, HPFC was inversely associated with sum hits (P = 0.05) (selective attention), incorrect (P < 0.02) and correct response (P < 0.01) (choice reaction time), score based on viewing and working time (P < 0. 01) (visual orientation), and visual memory performance (P < 0.01). Conclusions: Using hands-free devices to converse during driving, impaired reaction time, selective attention, visual orientation and visual memory, which are essential for safe driving. Thus, the use of these devices does not preclude cognitive distraction and should be restricted. © 2018, Iranian Red Crescent Medical Journal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".