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Generalizing about Public Health Interventions: A Mixed-Methods Approach to External Validity.

Laura C Leviton1

  • 1The Robert Wood Johnson Foundation, Princeton, New Jersey 08543-2316;

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PubMed
Summary

Public health aims for greater external validity to generalize evidence-based interventions (EBIs) for population health impact. This review explores methods to enhance EBI applicability and assess generalizability.

Keywords:
adaptationadoptionapplicabilitycomplexitycontextcore componentsdisseminationevidence-based interventionsexternal validitygeneralizationsampling

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Area of Science:

  • Public Health
  • Health Services Research
  • Implementation Science

Background:

  • Growing demand for external validity of evidence-based interventions (EBIs).
  • Need to generalize EBI findings beyond controlled effectiveness studies for population health impact.
  • Current limitations in translating research to real-world public health practice.

Purpose of the Study:

  • To review and synthesize methods for assessing the external validity of EBIs.
  • To propose strategies for enhancing the applicability of EBIs in public health.
  • To guide research towards greater population health impact.

Main Methods:

  • Review of existing methodologies for external validity assessment.
  • Identification of key components for enhancing generalizability.
  • Synthesis of statistical and logical inference techniques.
  • Emphasis on practitioner consultation and context description.

Main Results:

  • Four key methods proposed: improved EBI/context description, statistical/logical inference for samples, refined intervention theory/components, and practitioner consultation.
  • Focus on context features crucial for program theory and practitioner relevance.
  • Integration of mixed-methods research for broader causal generalizations.

Conclusions:

  • Enhancing external validity is critical for public health applicability and population impact.
  • A multi-faceted approach combining methodological improvements is necessary.
  • Future research should prioritize context-specific factors and mixed-methods designs to expand causal generalizations.