BACKGROUND: Coproduction is an inclusive approach for improving health and social care services, and coproduction research mostly focuses on participating stakeholders who are not researchers. Programme theories are important for designing, evaluating and disseminating change initiatives; however, few empirical studies on quality improvement initiatives or coproduction projects include explicit programme theories. This study addresses these knowledge gaps by describing new researchers' initial implicit programme theories of coproduction from three different system perspectives.
METHODS: This is a cross-sectional interview substudy that includes 12 respondents. The respondents are new researchers (doctoral students) in Samskapa, an international research programme. The respondents conduct their studies in their own national contexts: Western Europe and North America. The interviews focus on their thoughts and experiences of coproduction, and the data are analysed using directed content analysis based on central concepts of programme theory. Coded statements are additionally coded for the system perspective they refer to. To describe programme theories of coproduction from micro-, meso- and macrosystem perspectives, a latent interpretation of the data is carried out. The consolidated criteria for reporting qualitative research (COREQ) checklist for qualitative studies was used to assure quality standards.
RESULTS: A generic programme theory of coproduction is suggested: If microsystem actors collaborate, facilitated by mesosystem mediators and supported by macrosystem managements' feedback and engagement, then coproduction will occur and health and welfare systems will improve.
CONCLUSIONS: Coproduction projects are complex interventions that exhibit equifinality - a principle from open systems theory which posits that similar outcomes can be achieved through multiple, distinct pathways. Programme theories of coproduction from several system perspectives can be merged into a generic programme theory, which in turn can capture the interventions' complexity.