A Cross “Ethnical” Comparison of the Driver Behaviour Questionnaire (DBQ) in an Economically Fast Developing Country
Bibliographic record
Abstract
AIM: The aim of this study was to compare the driving behaviours of four ethnic groups and to investigate the relationship between violations, errors and lapses of DBQ and accident involvement in Qatar. SUBJECTS AND METHODS: The Driver Behaviour Questionnaire (DBQ) was used to measure the aberrant driving behaviours leading to accidents. Of 2400 drivers approached, 1824 drivers agreed to participate (76%) and completed the driver behaviour questionnaire and background information. RESULTS: The study revealed that the majority of the Qatari (35.9%) and Jordanian drivers (37.5%) were below 30 years of age, whereas Filipino (42.3%) and Indian subcontinent (34.1%) drivers were in the age group of 30-39 years. Qatari drivers (52%) were involved in most accidents, followed by Jordanians (48.3%). The most common type of collision was a head on collision, which was similar in all four ethnic groups. The Qatari drivers scored higher on almost all items of violations, errors and lapses compared to other ethnic groups, while Filipino drivers were lower on all the items. The most common violation was the same in all four ethnic groups "Disregard the speed limits on a motorway". The most common error item observed was "Queing to turn right/left on to a main road". "Forget where you left your car" and "Hit something when reversing" were the two lapses identified in factor analysis. CONCLUSION: The present study identified that Qatari drivers scored higher on most of the items of violations, errors and lapses of DBQ compared to other countries, whereas Filipino drivers scored lower in DBQ items.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".