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Journal Article

Citation

Ajayi AP, Mazinyo SP. Open Transp. J. 2020; 14(1): 78-85.

Copyright

(Copyright © 2020, Bentham Science Publishers)

DOI

10.2174/1874447802014010078

PMID

unavailable

Abstract

BACKGROUND:

The study examined the nexus between Social Economic Situation (SES) and Substance Use Disorder (SUD) and the influence the two exerted on willingness to comply with Health Safety and Environment (HSE) guiding the conveyance of petroleum products among truckers in Ibadan metropolis, Nigeria.

Methods:

Multistage and simple random sampling techniques were utilized to obtain the 265 respondents who are professional haulers and necessary information on the study objectives were obtained through self-reporting designed questionnaire. Bivariate statistical analysis and Logistic Regression Model were also used in ascertaining the existence of relationships among the dependent and independent variables.

Results:

The result from the bivariate analysis showed the existence of an association between two SES variables of age [X2 (9) 17.05, p=0.04] and literacy level[X2 (9) 41.36 p=0.00] and SUD. While the marital status of the respondents did not significantly influence the incidences of SUD [X2 (9) 9.841 p=0.36].Furthermore, a Logistic Regression Model (LRM) examined the relationship between dichotomous dependent variable (compliance or non-compliance to HSE stipulations guiding the conveyance of petroleum products) and the different independent explanatory variables (Age, Marital Status, Educational Status and SUD). The Wald criterion demonstrated that marital status (p=.000), educational status (p=.000) and attitude to SUD (p=.000) all made significant contributions to the prediction (compliance) while age (p=.861) was not a significant predictor.

Conclusion:

The study recommends the importance of non-economic factors (like literacy and marital status) in the achievement of sustainable HSE stipulations guiding the conveyance of petroleum products.
Keywords: Socio-economic-situation, Substance use disorder, Truckers, Health safety environment, Road safety, Logistic regression model.


Language: en

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