Role of Habits in Cell Phone-Related Driver Distractions
Bibliographic record
Abstract
Despite increased media attention and legislation banning some forms of cell phone use while driving, drivers continue to engage in illegal cell phone distractions. Several studies have used the theory of planned behavior (TPB) to explain why drivers voluntarily engage in cell phone distractions, and found that TPB constructs (attitudes, social norms, perceived behavioral control) predict intentions to engage in cell phone distractions while driving. Given that cell phone use is ubiquitous, habits that have formed around their general use may lead to automatic engagement in cell phone distractions while driving. This differs from voluntary engagement, in that habits are carried out automatically, with little thought given to the action or its consequences. Thus, in addition to the TPB constructs that explain intentions, habitual factors should also be considered in understanding why drivers use cell phones. A few studies have examined the role of habits in this context, but they only focused on texting behaviors. An online survey was conducted with 227 respondents to investigate the role of habitual cell phone use in driver engagement in a variety of illegal cell phone tasks (e.g., social media, email). Habitual cell phone use was found to explain unique variance in self-reported engagement after controlling for TPB constructs. Overall, the findings indicate that cell-phone-related distractions may not be entirely voluntary; instead, cell phone habits developed outside of the driving context appear to have a significant effect, suggesting that cell phone use while driving may have become automatic to a certain extent.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".