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Record W2000192688 · doi:10.7895/ijadr.v1i1.32

Psycho-spatial predictors of alcohol use among motor drivers in Ibadan, Nigeria: Implications for preventing vehicular accidents

2013· article· en· W2000192688 on OpenAlexvenueno aff
Gboyega E. Abikoye

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2013
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsAlcohol Use Disorders Identification TestMarital statusEnvironmental healthSample (material)Hazardous wastePsychologyHuman factors and ergonomicsDemographyInjury preventionPoison controlGeographyMedicineEngineeringPopulationSociology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.326
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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