Pattern and socio-economic implications of road crashes in southwestern Nigeria
Bibliographic record
Abstract
Road traffic accident is a major health problem in Nigeria. Death and injuries resulting from road crashes in the country are on the increase. For instance, fatalities rose from 1,083 in 1960 to 8,012 in 2001. While people who suffered from various degrees of injuries also increased from 10.216 in 1960 to 23,249 in 2001. Most of the victims are young, vibrant and highly productive people. The situation in Southwestern Nigeria is particularly alarming. The purpose of this paper is to assess the socio-economic implications of road crashes on road accident victims in Southwestern Nigeria. The study relied on the administration of 438 questionnaires to road accident victims in one public and one private hospital in each of the six states in the region. Information was also collected from records department in some of the hospitals. The data were presented using descriptive statistics. Findings from the study indicated that more than 70 per cent of the accident victims were within the productive age group of between 15-45 years. While, 80 per cent of them were male headed households, artisans and civil servants constituted more than 50 per cent of the accident victims. Further analysis of the data showed that, 60 per cent of the victims were very poor. And on the average each victim spent a minimum of N2000 per day as medical expenses with at least one family member attached to him/her throughout the period of admission in the hospital. This has grave consequences on family incomes, the future career of the victims as well as the human capital development. In addition, there are psychological implications such as sleeplessness, anxiety and travel phobia as well as emotional trauma suffered by the victims and members of their families. The paper calls for the adoption of preventive methods as well as standardized post- crash management initiatives in order to reduce the magnitude and burden of road crashes in Southwestern Nigeria.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".