Papyrus Walk a miscellany of musings

Parsing the NIH Reform Debate

I was recently alerted to Martin Kulldorff’s Blueprint for NIH Reform — a document that’s stirred some intense reactions among my colleagues. A few view it as a needed critique of systemic inefficiencies. Most regard it as an ideological Trojan horse—an attack on science dressed as reform. So where does the truth lie?

The short answer is: it’s complicated—and the messenger matters.

Kulldorff, once a Harvard professor and biostatistician, became a polarising figure during the COVID-19 pandemic for promoting ideas widely dismissed by the mainstream scientific community, including opposition to lockdowns, masking, and even some aspects of vaccination policy. He was also a co-author of the controversial Great Barrington Declaration, which called for herd immunity through natural infection — a strategy many experts considered unscientific and dangerous at the time.

This background understandably colors how his recent proposals are received.

But here’s the nuance: the Blueprint itself raises a number of ideas that aren’t inherently fringe. Calls for reforming NIH grant structures, enhancing academic freedom, incentivising open science, and streamlining peer review are echoed by many researchers across disciplines — including those with no ties to politicised public health debates. Frustrations with bureaucratic inefficiencies and perverse incentives in scientific funding are real and shared.

Where it becomes tricky is in the framing. Kulldorff doesn’t just argue for reform — he implies that current structures are suppressing truth, and that controversial views (like his own during the pandemic) have been silenced not because they lack merit, but because of groupthink or institutional bias. That framing, for many, crosses the line from constructive critique into undermining the scientific process itself.

There’s also a risk that pushing for more “openness” in what research gets funded — while laudable in theory — could result in resources being diverted to low-evidence, high-noise pursuits. Or, as one colleague aptly put it, “sending the ferret down an empty warren.” Science thrives on curiosity, but it also requires discipline and evidence-based filters.

Venue choice also matters. If this proposal were intended as a serious intervention into science policy, it might have been published in a mainstream medical or policy journal where it could be openly debated across the full spectrum of scientific opinion. Instead, it was published in the Journal of the Academy of Public Health — a platform co-founded and edited by Kulldorff himself, with close ties to politically conservative and contrarian public health figures. That choice raises questions about whether the article is seeking reform through consensus, or carving out space for alternative narratives that have struggled to find support in mainstream science.

So how should we engage with this?

Acknowledge the valid points: There is room — and need — for reform in how science is funded, reviewed, and communicated.
Be vigilant about context: Not all calls for reform are neutral. Motivations and affiliations matter, especially when public trust is on the line.
Defend the integrity of science: We can advocate for better systems without abandoning the core principles of evidence, rigor, and accountability — including fair peer review and a balance of risk and reward.

In the end, this is not a binary question of “pro-science” vs “anti-science.” It’s about how science evolves, who gets to shape that evolution, and what values we prioritise along the way — openness, yes, but always in service of evidence and public good.

This is an independent submission, edited by D.D. Reidpath.

The Great Trade Experiment

Last month I wrote about The Great Foreign Aid Experiment of the Trump administration. Foreign aid has not been without its critics because it is inefficient, promotes corruption, or is a part of an insidious program of neo-colonialism. The decision, however, by the US Government to put foreign aid “through the wood chipper” sets up a natural experiment to test whether aid save lives—more precisely, whether the sudden removal of aid ends lives. Most people in global health believe that it will result in significant suffering, although some see a silver lining: deaths among the poor and vulnerable will mark the emergence of independent health systems in low-income countries that are more resilient and finally free of external interference.

Not content with one natural experiment at the expense of the global poor, on the 2^nd of April 2025, Donald Trump announced the imposition of the highest rate of tariffs on US imports in almost 100 years. In effect, the government is dismantling the free-trade mechanism that has been operating since the mid-1990s, and adopting a more isolationist market posture. Under this new theory of trade, wealth is not created, it is finite and accrued by one country to dominate another.

The evidence has been pretty clear about the effects of poverty on health. Poor people are more likely to die than rich ones. Infant, child, and maternal mortality rates are significantly higher among the poor. Preventable and treatable diseases such as HIV, tuberculosis, and malaria also disproportionately infect and kill the poor. These poverty effects occur both within and between countries. Furthermore, they are not just biological outcomes—they are deeply social, economic, and political in nature. The conditions of poverty limit access to healthcare, nutrition, education, and safe living environments.

Over the last 75 years, in parallel with increasing life expectancy across the globe, wealth has also increased. The proportion of people living in extreme poverty today is much lower than it was 50, 20, or even 10 years ago. In fact, historically the sharpest global decline in extreme poverty occurred between 1995 and 2019—2020 was, of course the COVID pandemic, which reversed a wide rage of health and economic indicators.

Bill Clinton assumed the presidency of the United States in January 1993. He was supportive of free trade and the Uruguay Round of of the General Agreement on Tariffs and Trade (GATT), which was completed in 1994. The successful conclusion of GATT led to the creation of the World Trade Organization (WTO) in January 1995.

Following the liberalisation of trade, global extreme poverty rates fell from 36% to 10% between 1995 and 2018. In South and South-East Asia the extreme poverty rates fell from 41% to 10%. In Sub-Saharan Africa, the extreme poverty rates fell substantially, but without the same speed or depth as elsewhere: 60% to 37%. The gains of trade liberalisation were also more advantageous to some markets than others, and it particularly benefited countries with cheap manufacturing capacity such as Bangladesh and Cambodia.

The sudden US reversal on tariffs will be punishing for those poor countries that have developed a manufacturing sector—particularly in shoes and garments—to provide cheap, volume goods based on low labour costs. Of course, the goods in the US need not be cheap, because there is considerable profit in branding.

If exports drop significantly, factories will want to cut staff numbers swiftly to retain their commercial viability. Poor households, particularly those reliant on a single income manufacturing jobs, will likely be thrown backwards into extreme poverty. The global economic gains of the last 30 years could begin to reverse. A major drop in exports will have an immediate impact on the factories’ labour force but there will be flow on effects to the entire economy of poor countries. In Bangladesh, for example, garment manufacturing is the single biggest source of export revenue, and reductions here will mean reductions in national tax revenue which supports health, education and welfare services.

In other LMICs that are less reliant on a global export market, shifts in tariffs will have a concomitantly smaller impact. Thus, the two natural experiments will intersect. The impact of foreign aid on health and the impact of foreign trade on health will play out with interacting effects.

Needless to say, none of this was ever framed as an experiment. Cutting aid and raising tariffs was all to “Make America Great Again”. It is a cruel, indifferent approach to trade and foreign policy. There will be no one in the Situation Room plotting a Kaplan-Meier survival curve. No policymaker will announce that the hypothesis has been confirmed/rejected: that wealth, when withdrawn or walled off, leaves people dead. Nonetheless, the data will tell its own story.

And when it does, it won’t speak in dollars or trade deficits. It will speak in the numbers of anaemic mothers, closed clinics, empty pharmacies, and missed meals. It will speak in children pulled from school to help at home. It will speak in lives shortened not by biology, but by policy

The Great Trade Experiment, like the Great Aid Experiment, won’t just test theories in global health and economics. It will test people—millions of them. And the results, while statistically significant, will not be ethically neutral. Some experiments happen by accident. Others, by design.

This one was designed—by the President of the United States.

Parasitising Human Rights

A snail glides slowly from the shelter of the underbrush into the sunlight. One of its eye stalks (ommataphore) pulses with an unnatural rhythm, swollen, brightly coloured and weirdly attractive. A thrush spots the movement and swoops down, drawn to the flickering lure, pecks off the stalks and flies away.

The thrush was fooled. What it mistook for a juicy caterpillar was a parasite seeking a new host. The parasite, Leucochloridium paradoxum, is a trematode that infects a snail and turns it into a self-destructive zombie. The life cycle is simple: bird eats parasitised snail, parasite reproduces in bird’s gut, bird defecates, snail eats infected droppings. Once the parasite has been eaten by the snail, it hijacks the snail’s behaviour. It migrates to the snail’s eye stalks and drives it out of the safety of the underbrush and into the sunlight, where it will lure a bird to eat it. Rinse and repeat.

It was only very recently that I realised that the Christian far-right groups had adopted an analogous strategy to attack the international human rights framework and women’s rights in particular.

The Geneva Consensus Declaration (GCD) and its companion, the Women’s Optimal Health Framework (WOHF), function with unnerving similarity to the apparently tasty snail. They are each packaged in the shiny and appealing language of “optimal health”, “human dignity”, and “family”. They infiltrate the human rights system—not to strengthen it, but to hijack it, disguising regressive aims as a legitimate rights discourse. Once absorbed by a State-host, the State is zombified to re-present the regressive framework in shiny, deceptively appealing language waiting to parasitise the next State.

The GCD was first presented to the United Nations as a letter under Donald Trump’s 45^th Presidency of the United States. It was an initiative of the Secretary of State, Mike Pompeo, a fundamentalist Christian. Borrowing the name of the City of Geneva, made famous by its association with refugees, human rights and the Geneva Conventions, the GCD is neither supported nor endorsed by Switzerland nor the the Republic and Canton of Geneva, nor is it adopted by the UN.

The GCD document opens with lofty and appealing commitments to universal human rights and gender equality—pulling deceptively and disingenuously on the Universal Declaration of Human Rights. It declares that “all are equal before the law” and that the “human rights of women are an inalienable, integral, and indivisible part of all human rights and fundamental freedoms”.

Once consumed, there is a parasitic turn. The GCD reverts to a framework that reduces women to vessels and vassals in service to cells and states. The foetus is elevated. It is endowed with rights that eclipse those of the woman herself. She becomes a fleshy bag—nutrients in, baby out—stripped of the autonomy to define her own purpose or direction. The role of the State shifts. It is no longer the guarantor of individual freedom but the authority that dictates what a woman may or may not be allowed to do. “The family”—a surprisingly labile cultural concept—is suddenly reified, declared “the fundamental group unit of society,” as if its meaning were fixed and universal. The document commits fully to a vision of a society where the population serves the State, and women serve the population—with the least autonomy.

Health is a human right as is the right to healthcare. The GCD and the WOHF want to parse this, playing a game of reductio ad absurdum. You might have a right to healthcare, they argue, but you do not have a right to an abortion. As if it makes sense to say you have a right to healthcare, but not if you have scabies, rabies, HIV, or malaria. Pregnancy is not a disease, but it does require healthcare and that care may include the termination of the pregnancy. A woman’s purpose is not reproduction—servitude to a foetus.

Men, too, are caught in the parasitic zombification. They should not mistake their apparent elevation in these structures for freedom. They lose something fundamental. Choice. Authoritarian gender orders assign roles to everyone. Power is not granted—it is rationed and always conditional. The State grants status for obedience and identity in exchange for submission. Those assigned dominance are especially bound by its terms. This constraint brooks no dissent. In a society of freedom, you can find your own place. In a society of roles, your place determines you.

These zombified States do not act alone. The US-backed Institute for Women’s Health promotes the destruction of women’s rights, replacing evidence with sleek visuals and rhetorically based policy tools. The materials are presented as neutral frameworks but embed deeply conservative ideologies—valorising motherhood, framing women’s worth through familial roles, and avoiding any substantive discussion of sexual rights.

States that adopt these frameworks serve as megaphones, amplifying anti-abortion and anti-diversity policies in UN negotiations and global fora. This is not a grassroots movement for gender justice. It is a top-down project of moral, political, and social control, disguised as health policy.

The GCD and WOHF are not neutral initiatives. They are a parasitic ideological vehicle that masquerades as progressive while advancing regressive policies. Their true function is to infiltrate human rights systems, hijack the language of empowerment, and turn States into agents of restriction.

We must name this strategy for what it is: a parasitic ideology—designed to deceive, manipulate, and replicate. Human rights advocates must remain alert, resist co-option, and expose these frameworks not just for their content, but for the insidious strategies they deploy.

The only antidote to such parasitism is clarity, resistance, and the refusal to surrender universal human rights to the State.

Building Research Capacity with AI

Over 25 years ago, the “10/90 gap” was used to illustrate the global imbalance in health research. Only 10% of global research benefited the regions where 90% of preventable deaths occurred. Since then, efforts to improve research capacity in low- and middle-income countries (LMICs)—where 90% of avoidable deaths occurred—have made important gains; nonetheless, significant challenges remain. A quarter of a century later, there are still too few well-trained researchers in LMICs, and their research infrastructure and governance are also inadequate. The scope of the problem increased dramatically in 2025 when governments cut North American and European overseas development assistance (ODA, i.e., foreign aid) precipitously. That aid—however inadequate—supported improvements in research capacity.

Traditional approaches to improving research capacity, such as training workshops and degree scholarship programs, have gone some way to address the expertise challenge. However, they fall short because they are not scalable. The relatively recent introduction of massive open online courses (MOOCs), such as TDR/WHO’s MOOCs in implementation research, goes a long way to overcoming that scalability problem—at least in instruction-based learning. Nonetheless, for many LMIC researchers, major bottlenecks remain because of poor or limited access to mentorship, one-off and quick advice, bespoke training, research assistance, and inter- and intra-disciplinary collaboration. The scalability problem can leave them at a persistent disadvantage compared to their high-income country counterparts. Research is not done well from isolation and ignorance.

The rise of large language model artificial intelligence (LLM-AIs) such as ChatGPT, Mistral, Gemini, Claude, and DeepSeek offers an unprecedented opportunity…and some additional risks. LLM-AIs are advanced AI models trained on vast amounts of text data to understand and generate human-like language. They are flexible, multilingual, and always available (24/7), offering researchers in LMICs immediate access to knowledge and assistance. If used correctly, LLMs could revolutionise approaches to building research capacity and democratise access to skills, knowledge, and global scientific discourse. Many online educational providers already integrate LLM-AIs into their instructional pipelines as tutors and coaches.

Unfortunately, LMICs risk further entrenching or increasing the 10/90 gap if they cannot take advantage of the benefits of LLM-AIs.

AI as a game changer

Researchers in resource-limited settings can access an always-on, massively scalable assistant for the first time. By massively scalable, every researcher could have one or more 24/7, decent research assistants for a monthly subscription of less than $20. They offer scalability and flexibility that traditional human research assistants cannot (and should not) match. However, they are not human and may not fully replicate a human research assistant’s nuanced understanding and critical thinking—and they are certainly less fun to have a cup of coffee with. Furthermore, the effectiveness of LLM-AIs depends on the sophistication of the user, the task complexity and the quality of input the user provides.

I read a recent post on LinkedIn by a UCLA professor decrying the inadequacies of LLM-AIs. However, a quick read of the post revealed that the professor had no idea how to engage appropriately with the technology.

Unfortunately, like all research assistants, senior researchers, and professors, LLM-AIs can be wrong. Like all tools, one needs to learn how to use them with sophistication.

In spite of any inadequacies, LLM-AIs can remove barriers to research participation by offering tutoring on complex concepts, assisting with literature reviews and data analysis, and supporting the writing and editing of manuscripts and grant proposals.

Reid Hoffman, the AI entrepreneur, described on a podcast how he used LLM-AIs to learn about complex ideas. He would upload a research paper onto the platform and ask, “Explain this paper as if to a 12-year-old”. Hoffman could then “chat” with the LLM-AI about the paper at that level. Once comfortable with the concepts, he would ask the LLM-AI to “explain this paper as if to a high school senior”. He could use the LLM-AI as a personal tutor by iterating-up in age and sophistication.

Researchers can also use the LLM-AIs to support the preparation of scientific papers. This is happening already because an explosion of generically dull (and sometimes fraudulent) scientific papers is hitting the market. This explosion has delighted the publishing houses and created existential ennui among the researchers. The problem is not the LLM-AIs—it is in their utilisation, and it will take time for the paper production cycle to settle.

While access to many LLMs requires a monthly subscription, some LLM-AIs, like DeepSeek, significantly lower costs and accessibility barriers by distributing “open weights models”. Researchers can download these open weights models freely and put them on personal or university computer infrastructure without paying a monthly subscription. They make AI-powered research assistance viable for most LMIC research settings, and universities and research institutes can potentially lower the costs further.

LLM-AIs allow researchers in LMICs to become less dependent on high-income countries for training and mentorship, shifting the balance towards scientific self-sufficiency. AI-powered tools could accelerate the development of a new generation of LMIC researchers, fostering homegrown expertise and leadership in relevant global science. They are no longer constrained by the curriculum and interests of high-income countries and can develop contextually relevant research expertise.

The Double-Edged Sword

Despite its positive potential, the entry of LLM-AIs into the research world could have significant downsides. Without careful implementation, existing inequalities could be exacerbated rather than alleviated. High-income countries are already harnessing LLM-AIs at scale, integrating them into research institutions, project pipelines, training, and funding systems. LMICs, lacking the same level of investment and infrastructure, risk being left behind—again. The AI revolution could widen the research gap rather than close it, entrenching the divide between well-resourced and under-resourced institutions.

There is also a danger in how researchers use LLM-AIs. They are the cheapest research assistants ever created, which raises a troubling question: will senior researchers begin to rely on AI to replace the need for training junior scientists? Suppose an LLM-AI can summarise the literature, draft proposals, and assist in the analysis. In that case, there is a real risk that senior researchers will neglect mentorship, training and hands-on learning. Instead of empowering a new generation of LMIC researchers, LLM-AIs could be used as a crutch to maintain existing hierarchies. If institutions see the LLM-AIs as a shortcut to productivity rather than an investment in building research capacity, it could stall the development of genuine human expertise.

Compounding these risks, AI is fallible. LLM-AIs can “hallucinate”, generating false information with complete confidence. They always write with confidence. I’ve never seen one write, “I think this is the answer, but I could be wrong”. They can fabricate references, misinterpret scientific data, and reflect biases embedded in their training data. If used uncritically, they could propagate misinformation and skew research findings.

The challenge of bias is not to be underestimated. LLM-AIs are trained on the corpus of material currently available on the web, reflecting all the biases of the web–who creates the content, what content they create, etc.

Furthermore, while tools like DeepSeek reduce cost barriers, commercial AI models still pose a financial challenge. LMIC institutions will need to negotiate sustainable access to AI tools or risk remaining locked out of their benefits—particularly of the leading edge models. The worst outcome would be a scenario where HICs use AI to accelerate their research dominance while LMICs struggle to afford the very tools that could democratise access.

A Strategic Approach

To ensure LLM-AIs build rather than undermine research capacity in LMICs, they must be integrated strategically and equitably. Training researchers and students in AI literacy is paramount. Knowing how to ask the right questions, validate AI outputs, and integrate results into research workflows is essential. This is not a difficult task, but it takes time and effort, like all learning. The LLM-AIs can help with the task—effectively bootstrapping the learning curve.

Rather than replacing traditional research capacity building, LLM-AIs should be embedded into existing frameworks. MOOCs, mentorship programs, and research fellowships should incorporate LLM-AI-based tutoring, iterative feedback, and language support to enhance—not replace—human mentorship. The focus should be on areas where LLM-AI can offer the greatest immediate impact, such as brainstorming, editing, grant writing support, statistical assistance, and multilingual research dissemination.

Institutions in LMICs should also push for local, ethical LLM-AI development that considers regional needs. This push is easier said than done, particularly in a world of fracturing multilateralism. However, appropriately managed, LLM-AI models can be adapted to recognise and integrate local research priorities rather than merely reinforcing an existing scientific discourse. The fact that a research question is of no interest in high-income countries does not mean it is not critically urgent in an LMIC context.

Finally, securing affordable and sustainable access to AI tools will be essential. Governments, universities, and research institutions must lobby for cost-effective AI licensing models or explore open-source alternatives to prevent another digital divide. Disunited lobbying efforts are weak, but together, across national boundaries, they could have significant power.

An Equity Tipping Point

The LLM-AI revolution is a key juncture for building research capacity in LMICs. Harnessed correctly, LLM-AIs could break down long-standing barriers to participation in science, allowing LMIC researchers to compete on (a more) equal footing. The rise of models like DeepSeek suggests a future where AI is not necessarily a privilege of the few but a democratised resource for the many.

Fair access will not happen automatically. Without deliberate, ethical, and strategic intervention, LLM-AIs could reinforce existing research hierarchies. The key to harvesting the benefits of the technology lies in training researchers, integrating LLM-AIs into programs to build research capacity and securing equitable access to the tools. Done well, LLM-AIs could be a transformative force, not just in scaling research capacity but in redefining who gets to lead global scientific discovery.

LLM-AIs offer an enormous opportunity. They could either empower LMIC researchers to chart their own scientific futures, or they could become another tool to push them further behind.

Acknowledgment: This blog builds upon insights from a draft concept note developed by me (Daniel D. Reidpath), Lucas Sempe, and Luciana Brondi from the Institute for Global Health and Development (Queen Margaret University, Edinburgh), and Anna Thorson from the TDR Research Capacity Strengthening Unit (WHO, Geneva). Our work on AI-driven research capacity strengthening in LMICs informed much of the discussion presented here.

The original draft concept note is accessible here.