Plight of the distracted pedestrian: a research synthesis and meta-analysis of mobile phone use on crossing behaviour
Bibliographic record
Abstract
Background Pedestrians are commonly involved in vehicle collisions that result in injuries and fatalities. Pedestrian distraction has become an emerging safety issue as more pedestrians use their mobile phones while walking and crossing the street. Objectives The purpose of this research synthesis and meta-analysis is to determine the extent to which cell phone conversation, text messaging or browsing, and listening to music affect a number of common pedestrian behavioural measures. Methods A keyword search was developed with a subject librarian that used MeSH terms from selected databases including PsycINFO, SPORTDiscus, Medline and TRID. Supplemental searches were also conducted with Google Scholar and Mendeley. Effect size coding Thirty-three studies met inclusion criteria and were subjected to data extraction. Statistical information (ie, M, SD, SE, 95% CI, OR, F, t ) was extracted to generate standardised mean difference effect sizes (ie, Cohen’s d) and r effect sizes. Results Fourteen experimental studies were ultimately included in an N-weighted meta-analysis ( k =81 effect sizes), and eight observational studies were included in a qualitative overview. Both mobile phone conversation and text messaging increased rates of hits and close calls. Texting decreased rates of looking left and right prior to and/or during street crossing. As might be expected, text messaging was generally found to have the most detrimental effect on multiple behavioural measures. Limitations A variety of study quality issues limit the interpretation and generalisation of the results, which are described, as are future study measurement and methods improvements.
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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 | 0.037 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".