Tag Archives: Research Organisations

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.

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.

Leaders can be bullies too.

Leaders can be bullies too. And their poor behaviour will infect the whole organisation.

When I hear the word “bully“, even at work, I inevitably recall the schoolyard bullies of my youth. Often with a clique of sycophants, they were the nasty kids who tried to intimidate others. Their gangs were not deeply committed to being mean. They were committed to survival. Better, they reasoned, to support a thug than get sand kicked in their faces. Or worse, become the butt-end of cruel taunts about bad haircuts.

Unfortunately, we do not leave the bullies behind when we leave the playground. Bullies grow up and find their niche in adult life. The ease with which they establish themselves in an organisation—think parasitic wasp, not butterfly—signals the workplace’s tolerance for bad behaviour.

In an organisation with a strong supportive culture, managers deal with bullying swiftly and seriously. Minor incidents are treated as teachable moments. At low levels, the strategy may be as simple as one colleague being empowered to stand up for another—to make it known there is a line in the sand. At higher levels, when bad behaviour escalates, complaints about bullying are heard, taken seriously, and investigated rather than diverted and buried.

In one organisation I worked for, the Chief Executive Officer (CEO) was a well-known, old-school playground bully without the finesse one might expect from a modern leader.

One day, he wandered into my office. He didn’t like my research group’s strategy and wanted to tell me so. Dropping into a chair without greeting or invitation, he rocked back and started into me. I held my position. He became angrier and raised his voice. His reputation for shouting preceded him, and I was prepared. I had decided to match him decibel for decibel. He became louder; I became louder.

He quickly realised that we were shouting at each other and began to drop the volume. I followed suit. For about 10 minutes, the loudness of the conversation rose and fell. At the end of it, he smiled at me, said, “good chat”, rocked himself out of the chair and left. We had not agreed, but we had reached a rapprochement, and he left me to manage my own team.

I would not recommend my strategy even though it worked at the time. It can be extremely frightening to have a large adult male shout at you. It is also precisely why they do it. Unless you can cope with the aggressiveness of the interaction (and frankly, why should you?), shouting back is not going to work. It’s also unprofessional and fails to address the more significant structural issue.

Bullying was a regular tactic in my boss’s amentarium, and I achieved a temporary, personal solution that left others exposed. Because no one had ever managed his behaviour, his experience was that shouting worked. It was rewarded by compliance, and compliance was what he wanted.

Much of the leadership literature is about the qualities that one requires to “bring people along”, sell a vision, encourage engagement, (re-)align activities, and gather support for the (new) organisational strategy. The CEO short-circuited that messy business by bullying staff. Instead of intelligent workers, he wanted compliant widgets. The tactic, however, is stupid and lazy. Leaders who adopt it will lose one of their greatest assets. Disempowering staff reduces an organisation’s human capital. The short term win of reluctant compliance is offset by a deterioration of morale, the loss of good employees, and an absence of fresh perspectives. Organisations that accept bullying in leadership tacitly agree to become weaker organisations.

Bullying is also a quickly learned behaviour that obviates the need for senior staff to hone their leadership skills. If at first you don’t succeed, shout louder. Others learn the strategy, and it becomes an existential danger for the organisation.

Unfortunately, bullies in leadership are often not ranting, physical thugs and they don’t wear convenient labels. “BEWARE, BULLY!!!”. They have more polished and sophisticated tactics. The techniques can be pretty subtle and their true nature is often concealed from those who are not the targets.

When the most senior person in the organisation is a bully, who then will take action? The organisation’s Board or equivalent should step in, but this is easier said than done. The bullied staff member needs to know how to raise their concerns to the Board, and the Board needs to have the willingness to listen and act.

For a bullied staff member to complain, they have to believe it will make a difference. Unfortunately, complaining is often the employment equivalent of stretching your neck out on the chopping block. The victim needs to trust the process, and many organisations provide no basis for that trust. For managing bullies in leadership, the process should be well known, straightforward, and direct to the Board. It never entered my head to complain about my former CEO. I thought it was my problem, and I did not know of any internal processes, let alone a route to the Board. There are also, almost certainly, gender dimensions to who is bullied, how they manage it, and how seriously they are taken.

To manage bullying complaints about leaders, Board members need to be informed, engaged, and empowered to take the complaints seriously. “The Board has an absolute and unmistakable obligation to exercise oversight of workforce culture“. For NGOs, not-for-profits and other non-commercial Boards, membership is often voluntary or unremunerated. Such part-time, “not too serious” Boards can be particularly vulnerable to Directors’ and Trustees’ ignorance and lack of training. There are also disincentives for Boards to take bullying complaints seriously about senior leadership.

The CEO is usually a member of the Board and a colleague of the rest of the members. Some of the Board members will have been nominated by the CEO. Others may have been a part of the CEO’s selection process. When the CEO nominates a person to the Board, the nominee’s sense of loyalty can cloud their judgment about the CEO’s wrong-doing. After all, if the CEO nominated me, she must be OK because I’m great. When the CEO is found wanting, there may be a real sense of failure or a loss of face by Board members involved in the appointment. If a CEO is a bully, clients and the senior leadership team may question the Board’s competence and seek a review of the due diligence processes, with all the attendant embarrassment that can flow from that. All these impediments encourage Boards to obfuscate.

A quick internal process in the guise of swift action is a short-term (wrong-headed) solution to complaints about senior leadership bullying. The result is a superficial examination of the complaint that gives the Board comfort. It allows for a peremptory dismissal of the complaint and avoids embarrassment or culpability. It is easy to imagine, for instance, excusing bullying as a matter of “management style” rather than seeing it for what it is. This is wrong. There is nothing stylish about a bully. Unfortunately (or perhaps, fortunately), superficial processes for managing leadership misconduct have a nasty habit of coming back to bite an organisation.

A better approach, which carries a higher initial cost, is to engage an external, independent party. Let them investigate the complaint. It demonstrates the matter is being taken seriously, managed impartially, and led by the evidence. It also sets a loud, zero-tolerance tone within the organisation, setting or reinforcing the organisational culture.

If there are any concerns that bullying may be ongoing, administrative leave for the CEO (without prejudice) can be applied while an investigation is conducted. An excellent example of this was the suspension of the newly appointed Director of SOAS following a complaint of racism. The suspension occurred within months of his appointment, and following an investigation, he was cleared and reinstated. Any initial embarrassment that may have been felt is washed away by sound processes.

Unfortunately, the entire premise of this piece rests on two things. First, staff must be prepared and able to raise concerns about bullying by those in leadership. Second, the Board must be trained, competent and serious about managing it. Pretty words are not enough.

Staff realities are such that it can be better to suffer in silence or leave the organisation. I have known numerous staff of various organisations who chose to go rather than complain about their toxic workplace. Until you have witnessed the pyrotechnic career collapse of those who complained and were not heard, it is sometimes difficult to understand the reluctance.

No one wants to join the ranks of the pilloried complainers. The received wisdom is to “slip away” or “put up with it”. If Boards are not prepared to hold CEOs accountable, “slip away” is sound advice—tragic and indicting, but sound.

The Leadership a-Gender — 1

After competence, are certitude, charisma and chutzpah the 3-Cs of research leadership?

An image encouraging positive thinking to overcome self-doubt. Just make sure there are no large dogs about.

When Rob Moodie was the CEO of the Victorian Health Promotion Foundation (VicHealth) he started a “conversations in leadership” series for the recipients of VicHealth Public Health Research Fellowships. The idea was to begin an explicit process to develop research leadership in public health, drawing us together to think about the qualities that were necessary.

There were ten of us at the first gathering; two men and eight women. Beyond the fact that it was a meeting for “future leaders”, none of us knew what it was all about. Rob went around the table, asking each of us in turn to introduce ourselves; he also asked how we felt about being identified as a future leader in public health research.

The gender divide was immediately and starkly revealed. When Rob asked Paul (the other man in the room) and me how we felt, we gave suitably immodest responses. I can’t remember our precise answers, but they would have reflected in some way on the appropriate recognition of our talent. Then the first woman spoke. She told, hesitantly, of a gnawing fear that she would be “found out”. Someone, probably sometime very soon, would realise that she was a fraud. She had no right to the VicHealth Fellowship, and she had even less claim on being a leader. Paul and I glanced at each other. Who were we to say that she was wrong? And then there was a visible sigh from the other women in the room. Each one, in turn, expressed an almost identical fear of being found out. This is a well-recognised phenomenon in the gender and leadership literature, described as, “imposter syndrome“: the fear of being found out.

Notwithstanding my bravado or Paul’s, I suspect neither of us felt quite as sure of our place as future leaders as we expressed. I know I didn’t. Nor, however, did I fear being found out in quite the same way the women had expressed. I may have worried a little about whether my performance would be good enough (was I leadership material?), but I did not experience the depth of self-doubt expressed by my colleagues. I had been invited into the room and, therefore, I had a right to be there! They received the same invitation but doubted their right.

An article in the Harvard Business Review on overcoming the feelings of inadequacy associated with imposter syndrome described individual, cognitive behavioural techniques (CBT) to help people manage the sense. If these techniques work, that’s great! The solution, however, reveals at least as much about organisational gender bias as it does about ways to overcome it. Underlying the CBT approach is not simply a view that self-doubt is misplaced, but that there is a deficit in the way a person’s brain works if they have that self-doubt. In other words, to succeed in leadership, you need to think more like me! The obverse problem, having an over-inflated and unrealistic view of one’s own excellence, is often rewarded in organisations, and the sufferer (or more likely the insufferable) is never referred to a Psychologist for therapy “because you’re not thinking right”. Having the 3-Cs of certitude, charisma and chutzpah — typically identified as leadership qualities and never as leadership deficits — means that you are thinking right.

It is worth noting that although the women expressed the fear of being found out, they had all applied for and won highly coveted VicHealth Fellowships, and they were all in that room — even with their doubt.

The researcher, Thomas Chamorro-Premuzic, suggests that many of the 3-C style traits that are traditionally associated with great leaders may in fact be emblematic of leadership weaknesses. Being quieter (a listener), more thoughtful (open to new ideas) and having some self-doubt (seeking out a diversity of expert advice) can be valuable traits in good leadership. These are traits often associated with women who are passed over for leadership positions because they have not yet had their “deficits” corrected.

There are some clearly terrible traits for research leaders to have. Being a bully, mean, harassing staff and being incompetent would be high on that list. In research leadership, raw incompetence would be unusual. The others, sadly, are not. Research organisations need methods for identifying good research leaders that do not fall back on tired tropes, and provide women fair paths of advancements. These are organisational systems issues, not individual deficits to correct. Almost two decades ago, Rob Moodie’s conversations in leadership was a gentle step in that direction: making us all ask the question, what is it to be a great leader? He never said, by I suspect that he hoped we would carry forward some insight into the leadership a-gender.