Determinants of drink-driving and association between drink-driving and road traffic fatalities in Ghana
Bibliographic record
Abstract
Damsere-Derry, J., Afukaar, F., Palk, G., & King, M. (2014). Determinants of drink-driving and association between drink-driving and road traffic fatalities in Ghana. The International Journal Of Alcohol And Drug Research, 3(2), 135-141. doi:http://dx.doi.org/10.7895/ijadr.v3i2.135Aims: The objective is to establish determinants of drink-driving and its association with traffic crashes in Ghana.Methods: A multivariable logistic regression was used to establish significant determinants of drink-driving, and a bivariate logistic regression to establish the association between drink-driving and road traffic crashes in Ghana.Results: In total, 2,736 motorists were randomly stopped for breath testing, of whom 8.7% tested positive for alcohol. Among the total participants, 5.5% exceeded the legal BAC limit of 0.08%. Formal education is associated with a reduced likelihood of drink-driving compared with drivers without formal education. The propensity to drink-drive is 1.8 times higher among illiterate drivers than it is among drivers with basic education. Young adult drivers also recorded elevated likelihoods for driving under alcohol impairment, compared with adult drivers. The odds of drink-driving among truck drivers is OR = 1.81 (95% CI = 1.16 to 2.82) and two-wheeler riders is OR = 1.41 (95% CI = 0.47 to 4.28) compared with car drivers. Contrary to general perception, commercial car drivers have a significant reduced likelihood of 41%, OR = 0.59 (95% CI = 0.38 to 0.92) compared with private car drivers. Bivariate analysis conducted showed a significant association between the proportion of drivers exceeding the legal BAC limit and road traffic fatalities (p < 0.001). The model predicts that a 1% increase in the proportion of drivers exceeding the legal BAC will be associated with a 4% increase in road traffic fatalities (95% CI = 3% to 5%) and vice versa.Conclusions: A positive and significant association between roadside alcohol prevalence and road traffic fatality has been established. Scaling up roadside breath tests, determining standard drink (e.g., any drink which contains about 10 grams of absolute alcohol) and disseminating this information to the populace, and formulating policies targeting youth (such as increasing minimum legal drinking age and reducing the legal BAC limit for youth and novice drivers) might reduce drink-driving related crashes in Ghana.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".