Quality and Accuracy of Information Available on Websites for Distracted Driving: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Distracted driving has become alarmingly widespread, and its prevalence continues to increase despite efforts by government and nongovernment organizations to educate the public about this pervasive problem. Every year, 1.35 million people die, and nearly 80 million people get injured in road traffic incidents. Motor vehicle crashes are the leading cause of death among young people, and distracted driving plays a huge role in road traffic fatalities and injuries. Considering that most people now use the internet as an information source and Google is the most visited website and number one online search engine in the world, we performed a qualitative analysis of information available through Google on distracted driving and its outcomes. OBJECTIVE: The goal of this study was to analyze the quality and accuracy of the information on distracted driving and its consequences available to the general public when using Google as a search engine for distracted driving. METHODS: In November 2018, a nonregional Google search on distracted driving was conducted. The first two pages of the Google search results were selected for analysis. Data were collected on the type of website, type of distraction, consequences of distracted driving described, presence and referencing of statistics, and orthopedic and nonorthopedic injuries described, with their acute and chronic sequelae. RESULTS: In total, we analyzed 25 websites: 12 websites (48%) were from government bodies, which were the most common type of websites; 19 (76%) of the sites provided statistics; and 15 (60%) referenced the source of the statistic. Mobile phones were the most frequently cited type of distraction, with 17 (68%) sites discussing it, while death was the most commonly mentioned consequence of distracted driving, quoted in 15 (60%) of the websites. Additionally, 52% of the sites provided tips on how to avoid distracted driving. Only one website mentioned orthopedic injuries. CONCLUSIONS: The prevalence of distracted driving is increasing, and so are the consequences associated with it. Nevertheless, the information available online does not accurately describe the current circumstances regarding this issue. The National Highway Traffic Safety Administration attributed 391,000 injuries and 3477 deaths to distracted driving in 2015, which are 5000 more injuries and almost 150 more fatalities compared to 2011. However, despite these figures, most of the websites discussed death as a consequence of distracted driving and often overlooked injuries, even though injuries are over 100 times more likely to occur in distraction-affected crashes. The websites also largely fail to address other forms of driving distractions, like daydreaming or talking to a passenger, and mostly focus on mobile phone-related activities as distractions. More specific information on the dangers of distracted driving and nonlethal trauma may support an overall cultural shift to curb this behavior.
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How this classification was reachedexpand
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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 teacher head, 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".