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ObjectiveThis study was conducted to estimate the prevalence of disability and associated factors and further quantify the associated sex differential among Ghana’s workforce aged 15+ years.DesignA nationally stratified cross-sectional study.SettingGhana.ParticipantsIndividuals aged 15 years and above.Outcome measureDisability that limits full participation in life activities.MethodsThree predictive models involving Poisson, logistic and probit regression were performed to assess the association between disability and covariates. Modified Poisson multivariate decomposition analysis method was employed to assess sex differential and associated factors using Stata V.16.ResultsThe prevalence of disability was 2.1% (95% CI 1.2 to 2.4), and the risk of disability among males was approximately twice compared with females (Poisson estimate: adjusted prevalence ratio (95% CI)=1.94 (1.46 to 2.57); logistic estimate: aOR (95% CI)=2.32 (1.73 to 3.12)). Male sex increased the log odds of disability by 0.37 (probit estimate, aβ (95% CI)=0.37 (0.23 to 0.50)). The variability in age group, marital status, household (HH) size, region, place of residence, relationship to HH head, hours of work per week and asset-based wealth were significantly associated with disability-based sex differential. (Significant increased endowment: β×10−3 (95% CI×10−3)=−37.48 (−56.81 to −18.16) and significant decreased coefficient: β×10−3 (95% CI×10−3)=42.31 (21.11 to 63.49).) All disability participants were challenged with activities of daily living, limiting them in full participation in life activities such as mobility, work and social life.ConclusionThe magnitude of experiencing disability among working males was nearly twice that of females. Sex differentials were significantly associated with age groups, marital status, HH size, region of residence, relationship to HH head, hours of work per week and wealth. Our findings amass the provisional needs of persons living with a disability that are indicators to consider to achieve the United Nations Convention on the Rights of Persons with Disabilities Article 10. In addition, formulation of workplace policies should adopt a gender-sensitive approach to reduce disparities and eliminate disability in the target population.

The study was conducted using 20 cafeterias in Greater Accra Region on the effect of service quality dimensions on customer satisfaction in the hospitality industry. Purposive sampling technique was employed for the collection of the study data. Two hundred questionnaires were distributed to the customers of the selected cafeterias. The data gathered was analyzed by employing structural equation modelling (SEM) supported by AMOS 23.0 with maximum likelihood estimation in order to test the proposed hypothesis for the study. From the analysis of the data, tangibility was statistically having significant relationship with customer satisfaction. The result indicates that responsiveness, empathy, and assurance have insignificant relationships with customer satisfaction of the selected cafeterias.

This study assessed the factors that affects the amount of rainfall in Ghana. Knowing the factors that influence the amount of rainfall in a given geographical area is very important for planning and decision-making purposes. In this study, temperature, relative humidity, locality and the seasons that these factors occur were considered in determining the amount of rainfall received on land. Multilevel and panel data analysis techniques were used to analyze the data gathered from 2001-2015. The study reveals that temperature has effect on rainfall whiles relative humidity has no significant effect on the amount of rainfall experienced in the selected areas used for the study. The result shows that rainfall is maximized when temperature and relative humidity are at high levels and few rainfalls is expected when both parameters are at low levels. The study also highlighted on the importance of the study variables on food production in Ghana.

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.