Governance and Policy Research

Governance and Quality of Care Quality failures are governance failures. The Lancet Commission on High-Quality Health Systems showed back in 2018 that countries rarely fail to improve quality because they don’t know what good care looks like. They fail because governance arrangements don’t support it: Misaligned incentives, fragmented accountability, and systems that don’t learn from harm. That much is settled. The real challenge is that governance often remains abstract. After working with countries for more than two decades, I’ve found the key question isn’t whether governance matters — it’s how to govern for quality, day to day: How do you assess current capacity? Where do you start? How do you build governance for quality over time? Two tools have been especially useful in making this practical. 1. WHO’s Six Governance Behaviours, which describe what governing for quality actually involves: Deliver strategy — quality protected in budget decisions, not just policy documents Build understanding — turning data on harm and outcomes into improvement, not just compliance Enable stakeholders — giving frontline teams time and authority, not just expectations Foster relations — building collaboration, not just hierarchy Align structures — making financing, regulation, and mandates pull in the same direction Nurture trust — creating conditions where problems can be reported honestly 2. The WHO Governance Progression Pathway, which recognises that capacity develops in stages: Ad hoc → Deliberate → Embedded → Mature Most countries are at different stages across different behaviours. You might be strong on strategy but weak on trust. Have sound data systems but misaligned incentives. That’s normal. The point isn’t perfection. It’s understanding: Where are you now Which governance behaviour is holding quality back What moving one stage forward would require Used together, these tools help teams: Diagnose their current state Identify real leverage points Sequence reforms realistically and track progress over time Quality doesn’t improve through strategies alone. It improves when governance creates the conditions for learning, coherence, and trust. If useful, I’ve written this up in more detail, with a one-page summary you can use as a reference. #HealthGovernance #QualityOfCare #HealthSystems #UHC I have also written an extended essay about this topic for my latest Substack post. Blerta Maliqi Margaret E. Kruk

"This working paper will argue that standardization is the ideal route for establishing robust and adaptive AI governance for the research sector internationally. This is mainly because of the ability for standardization to engage multiple stakeholders with different interests while ensuring accountability and robust requirements through conformity assessment and certification within a standard. The first section of the paper will begin with an evaluation of the primary global risks posed by AI use within research institutions. The second section will discuss how these global risks require global governance structures and what that might look like in the research context. The third section will address some of the primary concerns or critiques of standardization as a method of governance and potential paths to ensure those shortcomings will not affect efficacy and robustness of an international standard for AI use in research. The fourth section will review the existing landscape of international standards for AI, the process of international standard development through the International Organization for Standardization (ISO) and other key institutions and organizations that might play a role in an ISO standard for AI in research. The fifth section will detail the essential elements of any ISO standard for AI in research. The final section will provide conclusions and recommendations for institutions to develop the CAN/DGSI 128 standard into an international standard." Great work from Matthew da Mota, Ph.D. at the Centre for International Governance Innovation (CIGI) #AI #AIgovernance 

How can we successfully transform scientific research results into Government policy? This book chapter presents innovative processes that have been developed in University College Cork and used to bridge the interface between the research ecosystem and policy-making ecosystem. Available here https://lnkd.in/evFNv9Hu. While the insights can apply across many areas of policy, the specific example here focuses on how energy systems modelling has been used to inform energy and climate mitigation policies in Ireland. From our experience over a 15 year period, motivation is critically important in order to overcome the challenges and to take on the extra effort to move beyond the traditional research process towards any or all of: actively informing, influencing, underpinning and co-producing policy. Engagement is not about communicating research findings, but critically also about listening to the policy practitioners needs, and developing a clear understanding of the policy making process, which is significantly different from the research process. Building trust with policy practitioners can take a lot of time and effort, but is hugely important. This includes developing personal relations respecting their role, their position, and when conversations are confidential in nature (especially when this not explicitly stated). Based on this experience, coupled with the examples provided, our approach can be summarised in a seven step plan that other research teams may find useful, in particular those who wish to bridge between the research and policy eco-systems: 1. Undertake scientifically robust research, submit it for peer review, publish it in scientific journals and make methods and results openly and publicly available. 2. Frame research questions that respond to specific policy needs, and then submit the results and insights to policy practitioners to inform policy 3. Translate research results into policy insights—including through the use of ‘policy briefs’ 4. Improve communications of research findings through the development of infographics 5. Engage actively with policy practitioners and policy makers—this is critical to move beyond informing and towards influencing policy, mindful of the different roles and responsibilities of each. 6. Co-produce policy—challenging but can be very successful. 7. Build absorptive capacity in the policy system—the focus here is on equipping the policy makers to understand the strengths and limitations of the approaches used, and improved interpretation of the scenario results generated. Thanks to co-authors Paul Deane and Fionn Rogan, and to MaREI, Science Foundation Ireland, Environmental Protection Agency (EPA) Ireland, Sustainable Energy Authority of Ireland (SEAI), Minister Eamon Ryan, International Energy Agency (IEA), IEA-ETSAP | Energy Technology Systems Analysis Program

Why does so much “policy-relevant” research fail to shape policy when it matters most? This paper ⬇️ offers a grounded answer from international behavioural science units. 👉 Quality and relevance are not produced by methods alone. They emerge from #relationships, #timing, and #InstitutionalPositioning. 🔔 What stood out to me is how consistently policy influence depends on proximity to decision-making rather than to journals. Research that informs policy is co-produced early, aligned with real policy questions, and embedded in organzations that understand political constraints, trade-offs, and moments of opportunity. Rigor matters, but relevance is negotiated, not delivered. The paper quietly challenges a familiar myth: that better evidence automatically leads to better policy. In practice, evidence travels through trust, credibility, and interpretive work. Behavioural science units succeed not because they simplify politics away, but because they learn how to work within it. The implication is uncomfortable for academia. If we want research to matter, we must invest in #PoliticalLiteracy, institutional interfaces, and long-term engagement, not only in methodological excellence. 📢 Policy-relevant research is not a technical output. It is a governance achievement. #PolSci4Health

Policy Consulting White Paper by GGI Fellows under the Council on Sustainable Development - Quantum Computing and Artificial Intelligence: A Comparative Analysis of Growth, Governance, and #Policy Challenges Global Governance Initiative is delighted to submit this white paper to the Ministry of Electronics and Information Technology- This document focuses on AI’s contrasts with QC’s nascent but geopolitically strategic development. By collaborating with stakeholders and utilizing data-driven strategies, GGIians proposal including- 1. integrated regulation, 2. post-quantum cryptography, 3. workforce development, and a 4. global quantum governance body. Author: Sam Chalyanth, and Shreya Goyal Mentor: Karan Patel https://lnkd.in/fviffWw

I recently discovered a fascinating paper by a recent PhD graduate Alix Bonargent - now at IDInsight - that provides cool new evidence about the research to policy pipeline. (Link in comment) She constructs a dataset of over 500 research projects, all conducted in the context of International Growth Center rearch initiatives, and seeks to explore what characteristics of projects predict actual policy change. She finds that projects developed in partnership with policymakers are dramatically more likely to result in observed change (15 to 20 percentage points), even conditional on publication. But, she also finds that there is a pronounced political cycle to this form of collaboration: it's more likely to be effective when projects are launched earlier in the electoral cycle, when there is more time to act on the findings. Interestingly, though, researchers from elite institutions seem to be relatively insulated from political cycles. Her hypothesis is that they can command so much funding and such large project teams that those teams can "protect" projects from political fallout. Fascinating analysis - worth reflecting on as we all attempt to craft meaningful and policy-relevant projects, particularly when working within ever-tighter constraints on time and resources.