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Scaling up complex interventions: insights from a realist synthesis.

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Scaling up complex public health interventions requires understanding context-specific strategies. Key mechanisms like awareness, commitment, confidence, and trust are activated through actions such as building partnerships and securing political support.

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Area of Science:

  • Public Health
  • Health Services Research
  • Intervention Science

Background:

  • Preventing chronic diseases necessitates complex, multi-component interventions tailored to specific contexts.
  • Many complex public health interventions fail to scale up, limiting their population-level impact.
  • Understanding the conditions and mechanisms for successful scaling up is crucial for public health practice.

Purpose of the Study:

  • To enhance understanding of how and under what conditions complex public health interventions can be successfully scaled up.
  • To identify core mechanisms and strategies that facilitate the scaling up of public health interventions.
  • To provide practical implications for practitioners involved in scaling up interventions.

Main Methods:

  • A realist synthesis was conducted, focusing on three successful complex intervention scale-up cases.
  • Data were extracted from published and grey literature, classified using Context-Mechanism-Outcome (CMO) configurations.
  • Findings were interpreted with knowledge users and compared with diffusion theory.

Main Results:

  • Four core mechanisms for scaling up were identified: awareness, commitment, confidence, and trust.
  • Two broad scaling-up strategies were highlighted: renewing/regenerating and documenting success.
  • Contextual actions included building partnerships, conducting evaluations, engaging political support, and adapting funding models.

Conclusions:

  • Successful scaling up involves coordinated efforts, including strategic evaluations and mobilizing political support.
  • Building partnerships and fostering peer learning networks are vital for ongoing knowledge exchange.
  • Future research should focus on action research and knowledge translation for realist syntheses.