The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach
Abstract Background Sustaining evidence-based interventions (EBIs) is an ongoing challenge for dissemination and implementation science in public health and social services. Characterizing the relationship among human resource capacity within an agency and subsequent population outcomes is an import...
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| Format: | Article |
| Language: | English |
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BMC
2018-06-01
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| Series: | Implementation Science |
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| Online Access: | http://link.springer.com/article/10.1186/s13012-018-0767-0 |
| _version_ | 1818599587649159168 |
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| author | Virginia R. McKay Lee D. Hoffer Todd B. Combs M. Margaret Dolcini |
| author_facet | Virginia R. McKay Lee D. Hoffer Todd B. Combs M. Margaret Dolcini |
| author_sort | Virginia R. McKay |
| collection | DOAJ |
| description | Abstract Background Sustaining evidence-based interventions (EBIs) is an ongoing challenge for dissemination and implementation science in public health and social services. Characterizing the relationship among human resource capacity within an agency and subsequent population outcomes is an important step to improving our understanding of how EBIs are sustained. Although human resource capacity and population outcomes are theoretically related, examining them over time within real-world experiments is difficult. Simulation approaches, especially agent-based models, offer advantages that complement existing methods. Methods We used an agent-based model to examine the relationships among human resources, EBI delivery, and population outcomes by simulating provision of an EBI through a hypothetical agency and its staff. We used data from existing studies examining a widely implemented HIV prevention intervention to inform simulation design, calibration, and validity. Once we developed a baseline model, we used the model as a simulated laboratory by systematically varying three human resource variables: the number of staff positions, the staff turnover rate, and timing in training. We tracked the subsequent influence on EBI delivery and the level of population risk over time to describe the overall and dynamic relationships among these variables. Results Higher overall levels of human resource capacity at an agency (more positions) led to more extensive EBI delivery over time and lowered population risk earlier in time. In simulations representing the typical human resource investments, substantial influences on population risk were visible after approximately 2 years and peaked around 4 years. Conclusions Human resources, especially staff positions, have an important impact on EBI sustainability and ultimately population health. A minimum level of human resources based on the context (e.g., size of the initial population and characteristics of the EBI) is likely needed for an EBI to have a meaningful impact on population outcomes. Furthermore, this model demonstrates how ABMs may be leveraged to inform research design and assess the impact of EBI sustainability in practice. |
| first_indexed | 2024-12-16T12:21:58Z |
| format | Article |
| id | doaj.art-0bba7676e3154541b494c93a89ca82cf |
| institution | Directory Open Access Journal |
| issn | 1748-5908 |
| language | English |
| last_indexed | 2024-12-16T12:21:58Z |
| publishDate | 2018-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Implementation Science |
| spelling | doaj.art-0bba7676e3154541b494c93a89ca82cf2022-12-21T22:31:56ZengBMCImplementation Science1748-59082018-06-0113111010.1186/s13012-018-0767-0The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approachVirginia R. McKay0Lee D. Hoffer1Todd B. Combs2M. Margaret Dolcini3Center for Public Health Systems Research in the Warren G. Brown School of Social Work, Washington University in St. LouisDepartment of Anthropology, Case Western Reserve UniversityCenter for Public Health Systems Research in the Warren G. Brown School of Social Work, Washington University in St. LouisSchool of Social and Behavioral Health Sciences, College of Public Health and Human Sciences, Oregon State UniversityAbstract Background Sustaining evidence-based interventions (EBIs) is an ongoing challenge for dissemination and implementation science in public health and social services. Characterizing the relationship among human resource capacity within an agency and subsequent population outcomes is an important step to improving our understanding of how EBIs are sustained. Although human resource capacity and population outcomes are theoretically related, examining them over time within real-world experiments is difficult. Simulation approaches, especially agent-based models, offer advantages that complement existing methods. Methods We used an agent-based model to examine the relationships among human resources, EBI delivery, and population outcomes by simulating provision of an EBI through a hypothetical agency and its staff. We used data from existing studies examining a widely implemented HIV prevention intervention to inform simulation design, calibration, and validity. Once we developed a baseline model, we used the model as a simulated laboratory by systematically varying three human resource variables: the number of staff positions, the staff turnover rate, and timing in training. We tracked the subsequent influence on EBI delivery and the level of population risk over time to describe the overall and dynamic relationships among these variables. Results Higher overall levels of human resource capacity at an agency (more positions) led to more extensive EBI delivery over time and lowered population risk earlier in time. In simulations representing the typical human resource investments, substantial influences on population risk were visible after approximately 2 years and peaked around 4 years. Conclusions Human resources, especially staff positions, have an important impact on EBI sustainability and ultimately population health. A minimum level of human resources based on the context (e.g., size of the initial population and characteristics of the EBI) is likely needed for an EBI to have a meaningful impact on population outcomes. Furthermore, this model demonstrates how ABMs may be leveraged to inform research design and assess the impact of EBI sustainability in practice.http://link.springer.com/article/10.1186/s13012-018-0767-0SustainabilityAgent-based modelingEvidence-based interventionHuman resourcesDissemination and implementation scienceOrganizational capacity |
| spellingShingle | Virginia R. McKay Lee D. Hoffer Todd B. Combs M. Margaret Dolcini The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach Implementation Science Sustainability Agent-based modeling Evidence-based intervention Human resources Dissemination and implementation science Organizational capacity |
| title | The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach |
| title_full | The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach |
| title_fullStr | The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach |
| title_full_unstemmed | The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach |
| title_short | The dynamic influence of human resources on evidence-based intervention sustainability and population outcomes: an agent-based modeling approach |
| title_sort | dynamic influence of human resources on evidence based intervention sustainability and population outcomes an agent based modeling approach |
| topic | Sustainability Agent-based modeling Evidence-based intervention Human resources Dissemination and implementation science Organizational capacity |
| url | http://link.springer.com/article/10.1186/s13012-018-0767-0 |
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