title: Supporting Better Evidence Generation and Use within Social Innovation in Health in Low- and Middle-Income Countries: A Qualitative Study.
abstract: BACKGROUND
  While several papers have highlighted a lack of evidence to scale social innovations in health, fewer have explored decision-maker understandings of the relative merit of different types of evidence, how such data are interpreted and applied, and what practical support is required to improve evidence generation. The objectives of this paper are to understand (1) beliefs and attitudes towards the value of and types of evidence in scaling social innovations for health, (2) approaches to evidence generation and evaluation used in systems and policy change, and (3) how better evidence-generation can be undertaken and supported within social innovation in health.
  
  
  METHODS
  Thirty-two one-on-one interviews were conducted between July and November 2015 with purposively selected practitioners, policymakers, and funders from low- and middle- income countries (LMICs). Data were analysed using a Framework Analysis Approach.
  
  
  RESULTS
  While practitioners, funders, and policymakers said they held outcome evidence in high regard, their practices only bear out this assertion to varying degrees. Few have given systematic consideration to potential unintended consequences, in particular harm, of the programs they implement, fund, or adopt. Stakeholders suggest that better evidence-generation can be undertaken and supported within social innovation in health by supporting the research efforts of emerging community organizations; creating links between practitioners and academia; altering the funding landscape for evidence-generation; providing responsive technical education; and creating accountability for funders, practitioners, and policymakers.
  
  
  CONCLUSIONS
  How better evidence-generation can be undertaken and supported within social innovation in health is a previously under-operationalised aspect of the policy-making process that remains essential in order to refrain from causing harm, enable the optimization of existing interventions, and ultimately, to scale and fund what works.
authors.affiliation: Centre for Evidence-Based Intervention, University of Oxford, Oxford, United Kingdom.
authors.forename: Madeleine
authors.identifier: http://orcid.org/0000-0003-0107-4047
authors.initials: M
authors.lastname: Ballard
authors.affiliation: The George Institute for Global Health in the Oxford Martin School, University of Oxford, Oxford, United Kingdom.
authors.forename: Jenny
authors.initials: J
authors.lastname: Tran
authors.affiliation: Saïd Business School, University of Oxford, Oxford, United Kingdom.
authors.forename: Fred
authors.initials: F
authors.lastname: Hersch
authors.affiliation: Global Health Sciences, University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Amy
authors.initials: A
authors.lastname: Lockwood
authors.affiliation: Saïd Business School, University of Oxford, Oxford, United Kingdom.
authors.forename: Pamela
authors.initials: P
authors.lastname: Hartigan
authors.affiliation: Centre for Evidence-Based Intervention, University of Oxford, Oxford, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Montgomery
mesh_terms: D003657:Decision Making; D003906:Developing Countries / Q000191:economics*; D006291:Health Policy / Q000191:economics*; D006302:Health Services Research / Q000191:economics*; D006801:Humans; D011050:Policy Making; D036301:Qualitative Research; D057170:Translational Research, Biomedical / Q000191:economics / Q000639:trends*