A Pilot Study on Impact of Mood State on Emergency Response Capacity for Young Novice Drivers
Bibliographic record
Abstract
As young novice drivers are inclined to getting involved in traffic accidents due to their improper emergency response under sorts of gender affective state, namely, mood, widely generated in fast-paced urban life, it is of great necessary to study the impact of mood state on responsive capacity for young novice drivers. Fourteen college students were recruited to take part in complex reaction experiments for this pilot study. Each subject’s mood was collected through a simplified POMS scale, while their complex reaction time (CRT) and response error rate (RER) were acquired during the experiments. The study results showed that young novice drivers’ RER was significantly positively correlated (pc=0.323 ∗ ∗ , “pc” omitted next) with their score of total mood disturbance (TMD), and a logarithmic regression model was feasible to describe the correlations with a good fitting effect. Further, their RER was also significantly positively correlated with score of negative mood state components such as nervousness (0.290 ∗ ∗ ), anger (0.300 ∗ ∗ ), fatigue (0.278 ∗ ∗ ), depression (0.287 ∗ ∗ ), and fluster (0.261 ∗ ), and a quadratic or cubic regression model was suitable to describe the correlations. Additionally, the young novice drivers’ CRT was significantly positively correlated with score of nervousness (0.222 ∗ ∗ ), vigorousness (0.227 ∗ ), and fluster (0.273 ∗ ), and a quadratic or exponential regression model was suitable to describe the correlations. The results can provide theoretical support for developing targeted intervention to improve young novice drivers’ emergency response capacity for driving training or traffic management authorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".