PO 8313 Predictors of health institutional readiness for ebola epidemic containment in nigeria: a structural equation modelling (SEM) approach. (24th April 2019)

• Record Type: Journal Article
• Authors: Adekeye, Joshua; Musenge, Eustasius; Arulogun, Oyedunni
• Is Part Of: BMJ global health. Volume 4(2019)Supplement 3
• Page Start: A28
• Page End: A29
• Publication Date: 2019-04-24
• Subjects: World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2019-EDC.73
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : Background: There is paucity of literature on organisational readiness in the health space. Previous studies focus on epidemic preparedness and often depict readiness as a minor element in the implementation space. This study investigated the predictors of health institutional readiness to implement innovations for combating an Ebola epidemic outbreak in Nigeria. Methods: The cross-sectional, mixed methods design was employed. The study population was 785 health care workers and non-health workers working at University of Port-Harcourt Teaching Hospital (Nigeria). The purposive sampling was used for qualitative study while the stratified random sampling technique was utilised for the quantitative study. Qualitative data were collected from fifteen respondents while a total of 511 questionnaires were administered at the study site. The qualitative data was analysed using inductive thematic analysis. The quantitative data was analysed using structural equation modelling (SEM). Results: The qualitative study suggested that quality improvement was perceived as most useful in influencing all the tree sub-components of readiness. Training is perceived as most useful in building readiness while it is perceived to be moderately useful in influencing the sub-component of readiness. The OLS estimates indicates that QI/QA exert a positive and significant effect on motivation (β=0.004, p<0.05) and general capacity score (β=0.28 p<0.05) while it inversely but significantly exerts influence on innovation specific capacity (β=−0.21×10–3, p<0.05). The SEM/pathway analysis shows the direct and indirect routes of interactions among predictors of readiness after adjusting for confounders. All the explanatory variables have significant effect on readiness except gender which was dropped from the final model. Conclusion: The strength of evidence of how an evidence-based system for innovation support can influence readiness was established. Though readiness is a rate-determining step in ensuring robust and effective implementation outcomes for epidemic containment, exploring innovation outcomes and their amplification through explicitly target readiness dynamics requires further investigation.