The U.S. public health system is continually challenged by unexpected epidemiological events that pose significant risks to the health of the community and require a commensurate surge in the public health system capacity to stem the spread of the disease. The complexity and even changing nature of funding and surge events drives agencies to innovate in order to maintain and support a competent workforce as well as update, or evolve the knowledge, skills and abilities (KSA) necessary to prevent, mitigate, or even eliminate the health crisis arising from a disease. This research investigates the capability of an agent-based, online personalized (AOP) intelligent tutoring system (ITS) that adaptively uses aptitude treatment interaction (ATI) to deliver public health training and assure competency. Also, presented is a conceptual model that combines Davis' Technology Acceptance Model (TAM) and the Public Health Service's Health Behavior Model (HBM) concepts to understand actual use of new technology in the public health sector. TAM is used to evaluate the effectiveness and the behavioral intent to use the system. HBM is used to explain and predict the preventative health behavior of actual use of the ITS. Our findings indicate the use of the ITS increases participant performance while providing a high level of acceptance, ease of use, and competency assurance. Without the determination of casual sequence, the TAM/HBM conceptual model demonstrated the best fit for predicting actual use of an ITS with the constructs of attitude, cues to action, and perceived ease of use showing the most influence. However, discussion of our findings indicates limited potential for an ITS to make a major contribution to adding workforce surge capacity unless members are directed to utilize it and technology barriers in the current public health IT infrastructure overcome.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1380 |
| Date | 01 January 2020 |
| Creators | Matthews, Sarah |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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