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Background
The Thinking Healthy Extended Program (THEP) is a nonspecialist-delivered mental health intervention targeting female caregivers (mothers and grandmothers) of children aged 6-36 months in rural China. It consists of 24 fortnightly group sessions, supplemented by individual home visits for caregivers who miss group sessions, and integrates cognitive reframing techniques, behavioral engagement, childrearing skills, and peer support. Embedded within a large-scale randomized controlled trial, this study aims to evaluate implementation fidelity of THEP, examine hypothesized mechanisms of change, and identify contextual factors, including enablers and barriers, that influenced intervention implementation.
 

Methods
In-depth interviews were conducted at 3 months and 10 months into the intervention implementation with 50 caregivers and 10 facilitators. The interview guides were developed based on the UK Medical Research Council’s process evaluation framework, with the facilitator interview guide focused on implementation fidelity, the caregiver interview guide focused on mechanisms of change, and contextual factors explored in both guides. All interviews were audio-recorded, transcribed, and analyzed using thematic analysis.
 

Results
THEP facilitators demonstrated competence in coordinating schedules, delivering sessions, and engaging caregivers. Caregivers reported improvements in peer support and childrearing skills, along with some uptake of behavioral engagement. However, both facilitators and caregivers demonstrated limited understanding of cognitive reframing techniques. Grandmothers (who accounted for 43% of the sample in the current study) experienced greater challenges than mothers with respect to applying the four program mechanisms of change. Several contextual factors, including a child-focused orientation, distractions related to the presence of children at sessions, and intergenerational differences between mother and grandmothers, influenced THEP implementation.
 

Conclusions
THEP was feasible to deliver through nonspecialist facilitators, but implementation quality was constrained by limited understanding of cognitive reframing strategies and variation in caregiver engagement. Future adaptation and scale-up should prioritize facilitator capacity and intervention approaches that are more feasible for nonspecialist providers and more accessible to diverse caregivers.

Journal Publisher
SSM - Mental Health
Authors
Yuyin Xiao
Scott Rozelle
Lia Fernald
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Population aging has increased the importance of understanding how long-term care interacts with healthcare. This paper examines the effects of Korea’s Long-Term Care Insurance (LTCI) reforms on healthcare utilization by exploiting successive eligibility expansions through changes in the Grade 3 eligibility cutoff in 2008, 2012, and 2013. Using the Korea Elderly Cohort Database and a regression discontinuity difference-in-differences design with individual fixed effects, the author estimates the causal effects of LTCI eligibility on overall and disease-specific healthcare utilization.

The results show that LTCI expansions significantly reduced inpatient healthcare utilization, with the effects following the 2008 introduction of LTCI substantially larger than those of subsequent eligibility expansions. The reductions were primarily driven by hospitalizations for diabetes and pneumonia, while outpatient visits for depression and dementia increased.

The findings suggest that LTCI both substitutes for avoidable inpatient care and complements outpatient care for conditions requiring continuous management. Rather than simply reducing healthcare utilization, LTCI reallocates healthcare use toward more appropriate patterns of care, highlighting the importance of better integrating long-term care and healthcare systems in rapidly aging societies.

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Evidence from Korea’s Long-Term Care Insurance Reforms

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Background: 
Early motherhood is a critical yet vulnerable life stage shaped by intersecting individual, familial, societal and structural challenges. Existing research often overlooks the multiple everyday stressors mothers experience across ecological domains. This study explores the lived experiences of mothers in urban China, using an ecological framework to examine the multi-level challenges they face during the first three years after childbirth.

Methods: 
In-depth interviews were conducted with 50 mothers of children aged 0–3 years recruited from nine community and township health centers and one childcare program for a migrant community in Shanghai, China, during well-child visits. Interviews were guided by an ecological framework and analyzed using thematic analysis.

Results: 
Challenges clustered across ecological levels, including postpartum physical health problems, caregiving burden, limited family and social support, work–childcare tension and constrained services, and information and financial constraints. These challenges manifested as common themes that negatively affected maternal wellbeing and the child nurturing environment.

Conclusions: 
These findings highlight that the challenges mothers experience extend beyond the individual level to family and institutional contexts, underscoring the need for coordinated interventions from individual support to family- and system-level services.

Journal Publisher
International Journal for Equity in Health
Authors
Yuyin Xiao
Scott Rozelle

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Visiting Student Researcher, Rural Education Action Program
Junling Zhao (2).png

Junling Zhao is a Visiting Student Researcher at the Stanford Center on China’s Economy and Institutions (2026–27). She is currently a Ph.D. candidate in Health Policy and Management at Peking University. She holds bachelor’s degrees in Sociology and Economics from Peking University. Her research interests include adolescent mental health, digital health, and health inequality.

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OBJECTIVES
This study examines whether and how caregiver mental health symptoms influence the impacts of a center-based parenting intervention on child development.
 
METHODS
We conducted a clustered randomized controlled trial across 100 rural villages in China between 2016 and 2019. Villages were randomly assigned to a treatment group that received weekly 1-on-1 parenting sessions through parenting centers or to a pure comparison group. A total of 2554 caregiver–child dyads with children aged 6 to 24 months were surveyed at baseline, and 2044 completed the endline survey after 1 year (follow-up rate: 80.0%). Child development was assessed using the Bayley Scales of Infant Development, Third Edition. Caregiver mental health was measured using the Depression Anxiety and Stress Scale-21. Ordinary least squares regressions with interaction terms were used for analysis.
 
RESULTS
The intervention significantly improved cognitive (β = 0.17 SD; 95% CI 0.045–0.29) and language development (β = 0.10 SD; 95% CI 0.00–0.21) among children whose caregivers did not exhibit depressive symptoms at baseline. Positive cognitive effects were also observed among children of caregivers with low anxiety (β = 0.13 SD; 95% CI 0.00–0.26) or low stress (β = 0.15 SD; 95% CI 0.03–0.27). In contrast, no significant effects were detected across developmental domains among children whose caregivers exhibited mental health symptoms. Heterogeneous impacts were driven by differences in stimulating parenting practices rather than program attendance or parenting beliefs.
 
CONCLUSIONS
The intervention primarily benefited children whose caregivers had no baseline mental health symptoms, suggesting caregiver mental health moderates intervention effects.
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Pediatrics
Authors
Scott Rozelle
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Maternal mental health issues are pervasive in rural China, yet community-based interventions are scarce. To address this gap, we developed the Thinking Healthy Extended Program, a nonspecialist-led intervention designed to promote mental health among female caregivers of children under the age of three, including mothers and grandmothers, in resource-constrained areas of China. Drawing on an adapted Stages of Implementation Science framework, this study describes the Exploration and Installation phases of a four-phase intervention development process. During the Exploration phase, we assessed caregivers’ mental health needs and preferences for intervention delivery through in-depth interviews. The Installation phase comprised three stages: developing the overall curriculum structure, identifying and validating curriculum topics, and integrating these topics into a comprehensive curriculum based on feedback from interviews, focus group discussions, and cognitive walkthroughs. The development process took place from 2022 to 2024 in a prefecture in southwestern China and involved 73 caregivers of children aged 6-36 months. The resulting 24-session curriculum integrates simplified cognitive reframing techniques, behavioral activation, caregiving skills, and peer support. This study demonstrates the feasibility of a systematic and participatory approach to developing a mental health intervention for low-resource populations.

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SSM - Mental Health
Authors
Hanwen Zhang
Scott Rozelle
Lia Fernald
Authors
Beth Duff-Brown
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In the inaugural episode of Health Policy Forum: In Conversation, host Paul Costello talks with Michelle Mello, JD, PhD, a professor of health policy and of the law, about where AI in health care actually stands—and why it looks so different from the AI dominating headlines.

Mello explains that adoption so far is concentrated in two seemingly unglamorous, but hugely helpful categories: ambient scribes that transcribe clinical visits, and administrative tools that handle billing and insurance approvals. The tools that generate the most excitement—such as clinical decision support and risk prediction—remain rare in practice. 

Meanwhile the hard policy problem isn't the technology but governance: whether every hospital, not just the best-resourced ones, can evaluate and monitor the flood of tools being marketed to them. Mello also describes how listening to patients changed her mind about disclosure, why clinicians are right to worry about liability, and what she'd tell Congress to do first.

 

We cannot justify an ecosystem in which hospitals can operate AI tools with very little vetting and no oversight and have no regulatory consequences.
Michelle Mello, JD, PhD
Professor of Health Policy and of Law

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TRANSCRIPT

Host Paul Costello

Welcome.  This is Health Policy Forum: In Conversation, a Stanford Health Policy podcast.  I'm your host, Paul Costello. We'll talk with an array of experts in health policy about what's in the news and emerging issues in health care that will impact all of us. We hope you'll follow us and check out our webpage that you can find in the show notes.

In this inaugural launch of Health Policy Forum: In Conversation, I'm pleased to welcome Stanford Professor of Health Policy and of the Law, Michelle Mello.  Dr. Mello is one of the nation's leading experts on health policy.  She has her eye on what's happening now and also what's around the corner. Thank you for being with me, Michelle. Let's get right into it. Thanks for having me. Michelle, if you were to paint a picture.

Where are we today in the world of the implementation of AI and healthcare?  What is proceeding aggressively and what's on the back burner right now?

Michelle Mello

I think we're very much in the early days. What we are seeing is wide, pretty widespread adoption among higher resource organizations of two kinds of AI and not as much adoption of other kinds. The two kinds that we see a lot of are AI scribes; these are ambient tools that listen to a conversation like in a clinic visit, create a full transcript, and then summarize that transcript for the doctor. And administrative tools that do things like handle insurance approvals, prepare and submit bills, and automate other kind of back office operations within hospitals. The kinds of AI tools that people get really excited about that might really improve quality of care like risk predictors or tools that help doctors make clinical decisions are not as widely implemented at this time.

Costello

How are doctors and patients feeling about the scribe potential? If a physician walks into a room or a nurse walks into the room, nurse practitioner, they say, can we record this? How are patients liking that? And what about physicians using it?

Mello

Well, of course, it's hard to know how all patients think about it without survey evidence, but I think in general, it feels a little weird to a lot of patients. More and more of us are becoming accustomed to seeing that in the examination room, but it's still, from a patient perspective, new and kind of invasive technology to encounter in the doctor's office, something that's listening to everything. And the patients that I've interviewed about these kinds of technologies have questions about where the information from that conversation goes, who keeps it, and how it's used at the same time that they recognize it has a lot of value for doctors who are trying to save time.

Costello

Does it change that sense of intimacy between the physician and the patient?

Mello

You know, I think there are mixed reports about that. In one study, about half of the patients who had had a scribe used in their care reported that they got more face-to-eye contact with the doctor. So that's a good thing. On the other hand, why is it only half? This is something that's taking notes instead of the doctor having to type into their computer and yet half of the patients didn't think they got a more intimate connection at all.  And as you I think you were suggesting it could have the opposite effect as well that patients feel like there's a robot listener in the room, they might have greater hesitation about what they say. 

Costello

What do you consider the most significant public policy issues about the use of AI in health care?

Mello

I think one major risk is cybersecurity.  that, you know, the good news is that healthcare organizations are really on top of that and they're getting a lot of help from private sector firms. The other issue that I worry about that is much less readily solved is how do we govern the huge amount of AI that's being pushed out to healthcare organizations in the market and make sure that all healthcare organizations, not just the best resourced ones, are in a position to be able to intelligently evaluate all these marketing pitches that they're getting, decide the tools that are best for them, and then monitor them to make sure that they're used safely. We're learning that that is expensive and tricky, and absent regulatory requirement, it's hard to imagine that all healthcare organizations will do it. 

Costello

And when you consider the cuts in Medicaid for rural hospitals, you would think that this is an added burden on them. How will they work around this, the financial aspects of this?

Mello

Yeah, I think that the way that a lot of AI developers would answer that question is to say, “Oh, we have so many tools that will save you money. And so yes, it might cost you money to license our product and to monitor it, but you're going to save hugely also.” And that is probably true for some kinds of tools, particularly the tools that interface with the process of creating and billing insurance claims. It's not true for a lot of other tools. And so, for those hospitals, really the best solution that we've hit upon so far is sharing information out. So, there's a lot of really good work going on at Stanford Health Care and other leading health systems that are large and academic oriented about what these tools do in healthcare, what do you need to be on the lookout for, what questions to ask. And the good news is that those organizations are very actively sharing out that information so that not everybody has to start at zero when they're trying to evaluate a new AI tool or figure out how to monitor it.

Costello

You mentioned regulation and I'm wondering, the federal government right now seems more focused today on innovation and less on regulation. And in that vacuum, who proposes the guidelines and the guardrails for AI in health care? 

Mello

Well, there are government agencies that are doing so. They're doing it on a voluntary basis. For example, NIST, which is a standard setting arm of the federal government, has developed a voluntary certification program that AI developers can participate in by meeting certain standards. They can get a gold star, so to speak, and be able to advertise in their marketing materials that they're compliant with these standards. And then there's an organization called the Joint Commission. They actually have an important role with the government. They inspect and certify hospitals for participation in the Medicare program. They have also developed a voluntary auxiliary certification that you can get for responsible AI governance. Again, don't have to, not a condition of Medicare participation, but you get the gold star. And that has been in partnership with a private organization called the Coalition for Health AI, which is a big umbrella organization for a couple thousand health systems, tech companies, insurance plans, and others that have spent a couple years now developing best practices for vetting and monitoring of AI tools.

So, there are these private initiatives, they're kind of interlacing a bit with the government, but right now most of what is happening is voluntary and anything that is voluntary and costly will have uneven uptake. 

Costello

You mentioned a few moments ago the work being done at Stanford and I know that you've called it landmark work. What is being done here and what is Stanford trying to do to make sure that AI is safe use responsibly and is fair to our clinical workforce. Can you explain that?

Mello

Yeah, well, when we talk about Stanford here, we mean our hospital system, Stanford Health Care. That's a separate organization from Stanford University, but there's a lot of cross pollination in faculty who have appointments in both. And what has happened is that hospital leaders, at a pretty early stage, recognize that number one, Stanford being Stanford, it's going to be a hub of innovation and experimentation with frontier technologies like AI. So that was going to mean tons of things being proposed and tried here.

And number two, it had to have a process for wrangling all of that activity into a manageable, governable space. And so pretty early on, in terms of what's going on nationally and even globally, it developed this quite robust governance process. It had the foresight to say, we’ve got a great lever here for making people submit their things to governance, which is that we will not let our digital services unit turn it on until you can certify that you've gone through this process. So, anybody who's got kind of a patient-facing or otherwise potentially risky tool has to go through this governance process. The tools get a review for their clinical utility, for their financial sustainability, and through my team's work, their ethical risks.

And the goal here is to try to proactively spot problems before the organization falls in love with the technology and puts it into use, and then to figure out in advance what is important to measure about the impact of this AI and how we're going to do that?

Costello

One of the things that I would think health-care providers around the country seem to have very different practices around what they should tell patients and when they should tell patients. What should patients be told about the use of AI?  And do you think that this should be uniform and who would make it uniform? What's the regulatory process?

Mello

Yeah, those are great questions. And as an ethicist, my priors coming into working in this area were, you know, that patients should be told about everything all the time so that they can be active participants in their care and make good choices. I've come to have a different view over the last couple of years, mostly as a result of talking with a lot of patients.

As part of the ethics governance work that we do here at Stanford, we have a patient partner panel of folks who have been trained in AI and who speak with us in a group format every month about AI tools that are being considered for adoption here. And their attitudes about being told or occasionally asked for permission to use tools in their care really depend on the tool. But in a much larger share of use cases than I would have guessed, they're not really focus on notification. They are much more focused on the question, what is the organization going to do to keep me safe? And, I’ve heard them say things like, you know, when I'm in the hospital, I got a lot going on, I'm sick, I have piles of paperwork pushed at me. The last thing that is helpful in this situation is another piece of paper asking me to sign on the dotted line. It feels like a waiver of responsibility, like they're just trying to cover their bases. More meaningful to me would be to know that there is a healthcare provider in the loop of this AI that's going to be reviewing it and that they have been kind of set up for success in that task by the organization, that they've created the time and information that the person needs to do that job. And that the organization itself has a good process for vetting these tools and is choosing and monitoring carefully.

But there are certainly exceptions to that general feeling like you know, if you've got a choice of surgical modalities, for example, one uses an AI guided robot and one doesn't, that's a clear case for informed consent. In California, an AI scribe is something you have to get permission for, not informed consent in the medical sense, but permission, and that's a wiretapping law, it doesn't have anything to do with healthcare, but there are exceptions. But the general rule I think should be, particularly because in a hospital like Stanford, you might have like a couple dozen different tools that touch your care in various ways. I think the presumption should be rather than using a notification to shunt responsibility onto the patient, we ought to be preserving that process for the most important, highest risk tools where they have a choice, you know, there's something they can do to opt out of the tool or take action in response to its use and otherwise really focus on our own safety and responsibility.

Costello

As you well know, there is a growing fear about AI outside of health care. Can it be contained? A recent column in The New York Times stated, “If there's ever a reason to doubt the dangers of unrestrained artificial intelligence, those reasons have gone out the window. What's next is a vital window of opportunity to slow down AI progress so we can better understand these models and avoid the calamity that may be on the horizon.”

And I'm wondering, how do you think that this writ large concern about AI is leeching into healthcare and the concerns that patients and physicians will have?

Mello

Well, the spaces that I hear about in healthcare have to do with cybersecurity. So that AI agents, whether it's something that is in use in healthcare, that's gone rogue or more likely some external agent, would be hacking into patient data and causing problems. That's one, and I think that's a problem in every sector in all settings for all of us all the time. And the other is that as we move increasingly from AI products that are human mediated, meaning there's a human being who reviews an AI suggestion and acts on it, accepts it or rejects it or modifies it, to systems that are agentic, meaning autonomous, they don't have a human in the loop, that those systems might get things wrong in ways that actually can physically hurt people very quickly and in a way that's very hard to intercept.

And that makes healthcare a little bit distinct from other contexts where we might worry about autonomous AI, like, I don't know, human resources or investments.  We're talking about physical injury happening very quickly here. And so, there is, I think, a heightened concern about being able to meet the challenge of vetting and monitoring those agentic tools as they come online. 

Costello

You testified before Congress quite a bit. If you were to advise a member of Congress on legislation to regulate AI, where would you start?

Mello

I think the most important thing that we could do at a federal level in the healthcare space would be to require healthcare organizations to have a governance process for AI as a condition of participating in federal health programs. We require hospitals and other facilities to have all kinds of other structures and processes in place to ensure that patients are safe and receive high quality care. And I just think we've reached the point where we cannot justify an ecosystem in which hospitals can operate AI tools with very little vetting and no oversight and have no regulatory consequences.

More broadly, I think the thing that is rightly occupying Congress is that much harder question of what you do about frontier models, general, you know, all-purpose models like chat GPT and the other modern LLMs that, as you've sort of alluded to already, have such far reaching potential impacts and harms from the kinds of cybersecurity risks that we've talked about already to, you know, more granular, but already present harms like mental health harms to kids. And I think that landscape is extremely difficult to figure out what the regulatory strategy should be because they do so many different things. 

Costello

Let me close by asking you, what is the one question that clinicians ask you about AI and what is the one question that patients ask?

Mello

By far the most common question I get from clinicians is, am I liable for mistakes that hurt patients if AI was involved? And that answer is not straight forward, but usually the answer is probably, yeah, you are, because developers of AI typically include provisions in their licensing agreements that immunize them from liability and disclaim ordinary consumer warranties that people usually make about their products. And so, liability would really rest on the physician, the nurse, and perhaps also the hospital and that makes physicians understandably leery about using AI tools, especially when they're not given a lot of information about how to be good overseers of the AI. 

I think patients just tend to be curious about what's going on. Like, what don't I know about how AI is being used in my care? They just have a curiosity about how these technologies that they hear so much about are actually being used on the ground. What are the benefits? And again, how is it that we're going about trying to keep them safe? But I think there's increasingly, at least around here in Silicon Valley, where we're all kind of AI natives, more of a sort of hopeful curiosity about AI and healthcare than there used to be, or that might be the case in other parts of the country.

Costello

You mentioned here in Silicon Valley, just the posterboards along 101 of AI are overwhelming.

Mello

Yeah, absolutely. You can't drive for more than 30 seconds on Highway 101 without seeing a billboard promising that agentic AI is going to change the world and make our lives better. 

Costello

Thanks for joining me today, Michelle. I really appreciate it. I want to again thank my guest, Dr. Michelle Mello. I hope you enjoyed the show.  If you liked it, tell a friend or a colleague.  This is Health Policy Forum: In Conversation. I'm Paul Costello. See you next time.

More Michelle Mello Commentary on AI

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AI Could Ease Looming Medicaid Work-Requirement Rollout—If Deployed Carefully

SHP’s Michelle Mello and Himaja Nagireddy, believe AI could help states manage looming Medicaid work requirements if deployed carefully—and not too broadly or quickly.
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Chip Kahn and Michelle Mello for KFF podcast
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AI is racing into everyday medicine, yet the rules of the road are still being written. SHP's Michelle Mello joins this KFF podcast to discuss.
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AI, Insurance Denials—and the Future of Health Policy

Michelle Mello, professor of law and health policy, joins The Future of Medicine for a conversation about one of the most frustrating and consequential parts of American healthcare: prior authorization.
AI, Insurance Denials—and the Future of Health Policy
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In the premiere of Stanford Health Policy's new podcast, Health Policy Forum: In Conversation, host Paul Costello and Stanford professor of health policy and of law, Michelle Mello, discuss the reality of AI in health care vs. the media hype over agentic AI.

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In Brief
  • Why AI scribes and billing tools led adoption, and clinical AI didn't
  • What patients actually want to know about AI in their care
  • Stanford Health Care's governance process and the lever that makes it work
  • Voluntary certification from NIST, the Joint Commission, and CHAI — and why voluntary means uneven
  • Rural hospitals, Medicaid cuts, and the cost of doing oversight well
  • Agentic AI and why physical injury makes health care different
  • Mello's one recommendation for federal legislation
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Background

The pandemic of antimicrobial resistance (AMR) will only be mitigated by policy action and innovation and importantly, supported by local and community action. With the United Nations General Assembly high level meeting on AMR taking place in September 2024, we decided to ascertain citizens’ understanding of the issues and prioritisation for action.

Methods

From June to August 2024, while intergovernmental negotiations on the outcome document were taking place, we used Deliberative Polling®, a methodology founded on deliberative democratic theory, in six middle income countries across three continents to explore people’s understanding and support for 45 policies that were likely to feature in the political declaration.

Results

In total 2419 participants were randomised to deliberation intervention (written and video information, facilitated online small group discussions, and expert plenary sessions) or control groups who only completed the pre- and post-deliberation surveys. Support increased significantly through deliberation for 3/4 of the proposals (>90% for 2/3), as well as on knowledge about AMR and internal political efficacy. Proposals relating to infection prevention were most heavily supported across all six countries. We found regional variation in support for proposals relating to informal antibiotic access and the use of antibiotics in food production, with less support for selected proposals from South America.

Conclusions

Deliberative polling is a powerful method of large scale community engagement and this is new for AMR helping us to understand the views of the public relating to policies that will require their support to enact.

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[version 2; peer review: 2 approved, 3 approved with reservations]

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Wellcome Open Res 2026
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Alice Siu
James S. Fishkin
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10:690
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For the second time in history, UN Member States adopted a Political Declaration on AMR in 2024. This declaration outlines ambitious commitments to tackle one of the most pressing health challenges of our time. These commitments will only be successful if the public understands, supports, and ultimately changes behavior to make them work.

Ahead of UNGA, we asked citizens in six countries what they thought about different policy proposals across six themes: Equity, Access and Stewardship; Infection Prevention; Food Security; Awareness and Education; Target setting; and Surveillance.

Working with the Trinity Challenge, we brought together 2,419 citizens from Brazil, Colombia, Nigeria, Tanzania, India, and Indonesia using Deliberative Polling®, a method that assesses what a representative and informed sample would think about policy proposals after extended, evidence-based discussions. Deliberations took place on the innovative Artificial Intelligence (AI)-assisted Stanford University Online Deliberation Platform across two days.

Citizens left the process not just more informed, but more engaged with the solutions required.

What is Deliberative Polling® ?


Deliberative Polling® is a research methodology developed by Professor James Fishkin at Stanford University's Deliberative Democracy Lab. Unlike standard opinion polling, it measures not just initial public opinion but informed public opinion - what people think after genuine engagement with the issues, trade-offs, and expert perspectives.

It has been applied in over 160 cases around the world, from national referendums to climate policy.

Learn more about the Deliberative Polling® methodology.

"Deliberative Polling® doesn't just ask what people think - it asks what they would think if they had the chance to really engage with the evidence."
Professor James Fishkin
Stanford Deliberative Democracy Lab

Key Findings


The results were striking - and they matter for policymakers, health leaders, and the public alike.

🌍 2,419 citizens across six middle-income countries participated
📈 43 out of 45 policy proposals saw increased support after deliberation
✅ The most popular proposals were those relating to infection prevention
🌐 Regional differences emerged — particularly around attitudes to restricting antibiotic use in food production.

Antimicrobial resistance is not only a scientific challenge but a societal one. This project shows that when citizens are given balanced information, diverse perspectives, and the opportunity to deliberate, they can engage meaningfully with the complexity of AMR and support the action it requires. Building public trust and community engagement is essential to reducing the global impact of AMR. We hope these findings and resources will help advance public engagement on AMR and strengthen deliberative approaches across global health.

Support increased for 43 out of 45 proposals following deliberation. Beyond the data, participants left better informed about AMR and more engaged with the solutions on the table. That is whole-of-society action in practice.
Dame Sally Davies
Master of Trinity College Cambridge and Executive Chair of the Trinity Challenge

From Understanding to Action


This study demonstrates something important: when citizens are given the chance to engage seriously with a complex global health challenge, they rise to it.

Across all six countries, deliberation moved public opinion towards greater support for ambitious AMR action — on prescription practices, agricultural use, access to medicines, and prevention. The 2024 UN Political Declaration set out what governments have committed to. This research shows that the public, when informed, is ready to support it.

The findings have direct relevance for:

  • Policymakers designing national AMR action plans, particularly in middle-income countries
  • Health communicators seeking evidence-based approaches to public engagement on AMR
  • Researchers building the evidence base for deliberative approaches in global health
  • Civil society organizations advocating for community-level AMR action
     

We are committed to sharing these findings as widely as possible — with governments, health bodies, and the communities that took part.

Thanks to our funders


With thanks to the Novo Nordisk Foundation and Wellcome.

Contact and Media


For media inquiries and further information about the research, please contact Louise Gough (Chief Operating Officer) at info@thetrinitychallenge.org.

Read our press release.

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Melissa Jeanty
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The Deliberative Democracy Lab and the Trinity Challenge asked 2419 people in six countries through the first-ever deliberative poll on AMR.

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