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Applying Machine Learning Techniques to Implementation Science
The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, describe a few limited real-world applications of ML related to implementation science, and discuss challenges that implementation scientists and methodologists may face along the way when using ML as a strategy to monitor EBI adoption or to inform the need for interventions.
ML approaches can be applied across the continuum of EBI implementation.
Online J Public Health Inform 2024;16:e50201
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Similar considerations have prompted Knittle et al [35] to develop a compendium of self-enactable techniques. The compendium contains 123 techniques that individuals can carry out themselves in order to achieve behavioral change and maintenance.
Second, the level of specificity varies among BCTs. While some techniques are highly specific, such as “information about antecedents” or “prompts or cues,” other techniques are less specific.
JMIR Form Res 2023;7:e50573
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