Back

The Dialogic Health Systems Research Framework (DHSRF): A tool for facilitating self-criticality, researcher interactions and knowledge management in Health Systems Research & Policy Studies

Priya, R.; Das, S.; Kuriakose, L. M.; Shukla, M.; Sarkar, A.; Dumka, N.; Hannah, E.; Basheer, N.; Kotwal, A.

2025-01-31 public and global health
10.1101/2025.01.29.25320166 medRxiv
Show abstract

Health Systems Research (HSR) has witnessed significant progress in theory, methodology and practice over the last two decades. The complexity of health systems has allowed for diverse lenses for HSR. However, given the absence of dialogue between the different streams of HSR, the diversity of this field, perhaps its greatest strength, is turning out to be quite the challenge. Without a common language that enables researcher interaction and critical examination of the field, diversity can easily turn into a din. To overcome this confusion evidence-based policy making requires tools that can assess the diverse evidence generated for designing systems coherent with the desired values and principles. Hence, we need a common research framework for HSR, to understand, describe and explain the systems structure and functioning, including observed and projected processes of change, across streams. It should be able to make sense of the formulation of HSR, allowing diverse research paradigms their appropriate place in HSR, and make them talk to each other rather than against each other. This study, therefore, presents the Dialogic Health Systems Research Framework (DHSRF), developed through an iterative and reflexive methodology involving a systematic narrative review and a national consultation. The DHSRF draws upon the comprehensive primary health care principles and a complexity lens to be context-sensitive, open to diverse perspectives, adaptable to different settings, value-critical and dialogic. It potentially contributes to knowledge management by a) allowing for a comprehensive review of HSR at the proposal and design stage to ensure a good fit for purpose, and b) assessing the strength of the outcomes of HSR in relation to the purpose and objective(s) of the research. By surfacing the inner workings of HSR, the framework can galvanise dialogue and debate in enriching the field and facilitating utilisation of its outcomes for policy, planning and implementation.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.