Psycho-spatial predictors of alcohol use among motor drivers in Ibadan, Nigeria: Implications for preventing vehicular accidents
Bibliographic record
Abstract
Abikoye, G. E. (2012). Psycho-spatial predictors of alcohol use among motor drivers in Ibadan, Nigeria: Implications for preventing vehicular accidents. International Journal of Alcohol and Drug Research, 1(1), 17-26. doi:10.7895/ijadr.v1i1.32 (http://dx.doi.org/10.7895/ijadr.v1i1.32)Aims: The study examined the roles of selected psychological, demographic and environmental variables in predicting hazardous drinking for both commercial drivers and private drivers.Design: The study was a cross-sectional survey.Setting: Data were collected at motor parks, auto workshops and car wash centers across the metropolis of Ibadan, Nigeria.Participants: A convenience sample of 566 drivers was recruited (241 commercial and 325 private drivers). Most drivers were male, and the mean age of the total sample was 35.70 years (±8.62 years).Measurements: The Alcohol Use Disorders Identification Test (AUDIT) was used to measure hazardous drinking. Psychological variables included standardized measures of perceived drinking norms and optimistic bias. Demographic variables included age, sex, education, marital status, employment status and income. Environmental variables included proximity to alcohol vendors or selling points and neighborhood density.Findings: About 45% of commercial drivers and 25% of private drivers reported hazardous levels of alcohol consumption. Hazardous drinking was predicted by age, education, income, proximity to alcohol outlets, neighborhood density, optimistic bias and perceived drinking norms even when all variables were included in the regression model. These variables accounted for a substantial proportion of variance in predicting hazardous drinking.Conclusions: Psychological, environmental and demographic variables are important in understanding hazardous drinking among drivers and should be incorporated into intervention for reducing drivers’ hazardous drinking, including drinking and driving.
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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".