Is There Any Relationship between Mental Health and Driving Behavior of Taxi Drivers in Kerman?
Bibliographic record
Abstract
Traffic accidents are the main reason of disability and the second reason of mortality in Iran. Therefore finding out the effective factors is vital. The aim of this study is to examine the relationship between mental health and taxi drivers’ behavior in Kerman. This is a cross-sectional descriptive research in which Manchester driving behavior questionnaire (MDBQ) and “general health questionnaire (GHQ)” were used. The questionnaires were distributed between 186 taxi drivers during February and March 2015. Our study was conducted in the province of Kerman in the east south of Iran. We used descriptive methods as well as t-tests, chi-square tests, and logit models for data analysis. The data analysis showed that the driving behavior of Kermanian taxi drivers is good (0.481±4.13) and the mental health situation of them is partly good (0.662±3.61). The Pearson’s correlation test showed overall driving behavior score is correlated positively with mental health score (r=0.83, P=0.000). Also there were positive correlations between all driving behavior dimensions and mental health dimensions at a level of significance of 0.005. The result of Chi-Square Test showed that there the younger drivers and who had less driving experience had higher mental health score. Single drivers and who had less education, which had faced with financial loss in their previous accidents, which had lose their driving license for a while, higher driving behavior score compared to the others (P<0.05). By some improvement actions in driver’s mental health, we can effect on their behavior. And by proper driving behaviors, we can avoid from some mortalities, disabilities and heavy costs on society.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".