Knowledge hubs for DRR science for the implementation of the Sendai Framework.
Jim Yong Kim, President of the World Bank, recently made a case for "science of delivery", i.e. a science of implementation and execution [ 23].
Such inadequate description of implementation interventions hinders the development of a cumulative science of implementation.
(See companion article in this issue of Global Health: Science and Practice for implementation details about the Care Group approach.
Most importantly, refinements in resilience theory, prevention science, and strategies for implementation must be applied to a scalable dissemination of family-centered services to populations that are most exposed to trauma and loss.
Another important contribution of the present study regards the need to unite the health sciences and social sciences for the implementation of interdisciplinary actions that can contribute to the prevention of dental caries in the population.
It was initially designed for implementation science research focused on public sector services including mental health and child welfare, and similarly organized systems such as substance use disorder treatment (Aarons et al., 2011).
Together, the high rates of drug abuse, HIV, and related conditions among offender populations; the availability of evidence-based practices to address these needs; and the structural barriers to innovation make the US criminal justice system a ripe target for implementation science research.
Understanding the science behind implementation processes has also become an important concern for healthcare policy and practice.
Recommended best practices from implementation science for use during program delivery and current practices in pharmaceutical risk-minimization program implementation are summarized in Table 2.> Risk-minimization programs can be challenging to implement because implementation may need to occur at multiple levels and/or be conducted by multiple parties.
This collision of framings in relation to expectation of effects also speaks to the different kinds of data generated in mixed-method implementation science, for instance, between the data we have generated through modelling (oriented to HIV prevention impact) and that which we have generated through qualitative interviews (which have captured participant perspectives on recovery).
