Theory of Planned Behavior as a model of limit mobile phone use while driving
Bibliographic record
Abstract
Theory of Planned Behavior (TPB) is one of the most widely used psychological models when it comes to explaining road safety behaviors. Recently, studies have also been conducted from the perspective of dual-process models. However, the present is the first study on road safety behaviors that integrates both perspectives. The study evaluates the roles of both implicit attitudes and TPB constructs in the prediction of mobile phone use while driving. Method a sample of 100 drivers completed: (1) a self-reporting instrument on Mobile phone use while driving, (2) a questionnaire addressing TPB constructs, (3) an indirect measure of attitudes (Implicit Association Test), and (4) a social desirability scale. Results suggest that both types of attitudes make a significant and quite similar contribution to the explanation of Mobile phone use while driving. Interestingly, implicit attitudes were a better predictor than explicit attitudes among participants reporting inconsistent Mobile phone use Mobile Phone while driving. In addition, path analysis models suggested that implicit attitudes appear to be relatively independent of TPB constructs and have a direct effect on Mobile phone use. Conclusion the findings advance the idea of adding implicit attitudes to variables from the TPB model in order to increase the explanatory power of models used to predict road safety behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".