The Influences of Drivers/Riders in Road Traffic Crashes in Ghana between 2001 and 2011
Bibliographic record
Abstract
The road traffic accident (RTA) is a global misfortune and the leading cause of death among young drivers. In safeguarding and developing innovative safety strategies to curtail the situation, the factors causing this menace needs proper attention and investigation. The objective of this study is to identify the potential factors responsible for causing a traffic accident in Ghana. In studying these factors extensively, a descriptive study with quantitative technique was employed. Analyses used data between 2001 and 2011 obtained from the Building and Road Research Institute (BRRI) with specific focus on the age, drinking, vehicle defect, driver/rider error, injury, road surface type and weather. A total of 200,528 cases of drivers/riders were analysed and discovered that, people with younger age (21-40) contribute 62.97% of total crashes. Crashes reduce steadily as drivers/riders age increases. Also, the vehicle defect analysis shows that 87.46% of accidents cannot be linked to the fault of the vehicle before incidence, while the majority (75.38%) of drivers/riders had no injury during a traffic accident. Higher number of fatalities are recorded on tar good roads (81.57%) and clear weather (91.75%). The fight against this canker by the authorities must consider periodic refresher courses for younger drivers/riders on traffic law to bring to bear the adherence of good driving/riding principles and attitudes to ensure that safety is guaranteed for all road users in the country.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".