Picture this: You’re at the grocery store in the popcorn aisle trying to decide which kind to buy for movie night tonight. You want to provide delicious popcorn for your guests.
When deciding on a popcorn, lots of factors come into play.
Which popcorn is priced best? Which is most visually appealing? Which are others reaching for? Which most closely aligns with your nutritional needs?
Which has the most kernels per bag? How many people are coming to movie night? Does anybody have any allergies or dietary restrictions?
Even if you make this decision in a split second, odds are your brain goes through all these calculations.
Now imagine that same situation… but after staying awake for 24 hours straight. Perhaps you’ll get the one that’s easiest to reach. What about after a relaxed morning at home?
Maybe you’ll be more thorough in your consideration of which to buy. Regardless, your own personal situational and human biases will be relevant.
We already established that research bias – leading up to and within actual experiments – is profoundly prevalent. If you don’t know what research bias is and where it comes from, read our article about it to get up to speed.
However, research is not the only avenue susceptible to bias.
The grant funding process controls what research gets funded and is weighed down by countless forms of bias and bureaucratic egos.
The short answer is, yes: bias is definitely steering the grant funding process.
The grant funding process itself has become an experiment, and not enough efforts are being made to reduce systematic error.
Let’s take a look at how and why by analysing the current processes experts use and expose natural human tendencies towards bias.
How Does Bias Steer the Grant Funding Process?
When grant applications are being reviewed, many similar same factors, including superficial ones, which we discussed in the popcorn scenario come into play. Which scientists have the best looking resume? Did any of the scientists go to the same school as one of the reviewers?
There are four primary ways bias makes its way into the grant funding process. For those not familiar with the grant funding process, it refers to the way in which experts and funders decide which scientists receive grants and which scientists do not.
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The Reviewers: The traditional grant selection process provides a stack of completed applications to an average of three “expert” reviewers.
These people are naturally biased individuals (as are all human beings) which is unavoidable, but the size of the sample makes for a serious problem.
A sample of three is not considered meaningful in any other analytical assessment, so why is it considered meaningful in this context? -
Elitism: In grant management software, the first thing reviewers see is name, institution, past collaborators, and awards received (identifying information about the scientists) – not their actual research proposal or ideas.
This means that more often than not, reviewers will inevitably focus on the person receiving the award and simple facts about them, over the ideas at hand.
This is not the fault of the reviewers, who are typically volunteering their time to review hundreds of applications within a short time period and whose minds apply their own heuristics daily to make their lives as efficient as possible.
Definition: Mental Heuristics are mental shortcuts that can facilitate problem-solving and probability judgments.
These strategies are generalisations, or rules-of-thumb, reduce cognitive load and can be effective for making immediate judgments.
These are natural human impulses, however, they often result in irrational or inaccurate conclusions with real impacts.
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Incompleteness of Evaluation: Another problem with current grant management software is that they provide absolute rankings.
This means reviewers ascribe a number of 1-5 by category rather than relative rankings, where they would compare and choose the best 5 from a pile of 20.
There are certainly differences in the meaning behind each reviewer’s inputs and the fatigue each reviewer experiences with reading so many applications. -
Finding the Application: Funders can only offer funds to the scientists who managed to find and apply to one of the hundreds of grant management platforms that exist.
The odds of the right applicant finding the right application and being become ever more slim as we begin to take into account how time-consuming and cumbersome this process is for applicants.
Why Does it Matter that Bias is Funding the Grant Application Process?
Bias skews outcomes: especially when it comes to grant funding.
With so many different types of bias at play during any grant funding process, it becomes difficult to make a funding decision based solely on the most important factor: the quality of the research.
Think back to the popcorn scenario. Perhaps you’ve gotten caught up in the nitty gritty and you’ve lost sight of your goal, which is to give your guests a delicious popcorn experience.
That’s natural and normal, but we can’t afford to let the same thing happen in mental health research.
Allowing bias to influence funding creates a vacuum that centres funding around specific institutions, specific topics, or even specific types of people.
In order to create progress, innovative and even disruptive research is necessary.
This innovative research does not typically come from traditional, bureaucratic, or tenured sources.
Therefore, it becomes critical to ensure that qualities of the research and the science, NOT qualities of the researcher drive funding decisions.
Here are a few examples of the types of bias at play during most funding decision making processes. For more information, see our article on research bias.
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Observation Bias (The Hawthorne Effect): Either consciously or unconsciously, participants alter the way they act in a study or scientists or the answers they give about participants.
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Recall Bias: When subjects are being studied in any experiment, they are often asked to recall things that happened to them in the past, and different types of and certain events might get mixed up in a person’s memory.
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Selection Bias: Research samples can sometimes under-represent certain people or groups, and over–represent others.
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Confirmation Bias: This occurs during the interpretation of study data when researchers, consciously or unconsciously, look for information or patterns in their data that confirm the ideas or opinions that they already hold.
Removing bias in the review process, which at the moment functions as an experiment itself, is absolutely essential.
When we do this, we allow necessary interdisciplinary and innovative research to receive funding and carry out the following after-effects:
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Mental health research and evaluation inform public health professionals and policy decisions to help those in need of support.
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Present psychiatric treatments and theories focus on the symptoms of mental health because there is no understanding of the causes. We need to break away from thinking, talking, and addressing the symptoms without accounting for the causes.
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Disruptive research challenges the status quo and bureaucratic norms. However, disruption is uncomfortable and risky and therefore tends to be avoided. Yet the most significant advancements in human history came from disrupting the status quo. Why, in a field as vital as mental health, are we allowing the status quo of inefficiencies and inequities to rule the labs?
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The brain is an incredibly complex organ with 86 billion neurons and thousands of connections between each. Is it logical that all mental health treatments can be boiled down to a few neurotransmitters and chemicals?
At 1907 Foundation, we care about funding the best science, based on logical arguments. We care about prioritising patients, always.
We exist to catalyse discovery within mental health research because we believe discovery powers innovation and innovation creates progress.
That’s why when we uncovered the shocking amount of bias in the grant funding process, we decided to build a new and revolutionary grants management system that removes bias and prejudice from the grant funding process.
How Does 1907 Foundation Combat Bias in the Grant Funding Process?
Our team values innovation in technology and thinking critically about how we can improve our world with it, which is why we built, and are now scaling, Atala: a grant management toolkit designed to streamline the application, review, and award processes.
Atala creates collaboration opportunities between funders to ensure money gets into the hands of researchers who can make an impact and can create progress more ethically and equitably.
If Atala existed for popcorn, it would show you all the different types of popcorn in one place, including price, advertisements, nutrition information, quantity, taste information, availability, and even highlight areas where you might be unconscionably biased towards one popcorn or the other for irrelevant reasons.
When we uncovered the shocking amount of bias in the grant funding process, we decided to build a new and revolutionary grants management system that removes bias and prejudice from the grant funding process.
While our primary goal is to fund the best possible brain research, we realised there were fundamental gaps in the funding system that were preventing the best research from being executed.
Atala was designed specifically to solve the core problems with the current system that are outlined above:
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The Reviewers: By ensuring a large sample size of reviewers, Atala will be able to guarantee a higher degree of ethical decision-making. Reviewers have the option to invite another, external reviewer to add feedback to an application.
We have “dashboards” to display the stats and explicitly call out differences in opinion. And, before any applications are selected, everyone on our scientific council has individually reviewed the applications without the views of others. To avoid application review fatigue, any reviewer can hit a “recuse” button during application review at any time for any reason.
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Elitism: In Atala, reviewers don’t have access to identifying information (e.g. resume, published articles, diversity info, etc.) during application review. They can only see the relevant “novel idea” which pertains to the science, not the scientist. “Guards” make it difficult to expose identifying or sensitive applicant information. As a result, our reviewers review the science rather than the scientist.
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Incompleteness of Evaluation: Atala provides quantifiable metrics collected by the system that can be visualised in tables, diagrams, and charts. This also includes applicant and reviewer stats (e.g.time spent on each application, previous institutions, specialisation, etc), as well as stats on the studies themselves.
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Finding the Application: Atala seeks to synthesise the nonprofit grant management marketplace by serving as the one hub that all applicants and private funders can use. The idea is similar to that of the college Common Application – it offers students the ability to complete one application and essay and send it to multiple schools, as opposed to having to complete each individual college’s unique application.
Atala follows the same principle but with scientists and nonprofits instead of students and colleges.
If you can believe it, there are no other GMSs seeking to simplify the application process in this manner despite having proof of the benefits of this step in a tangential industry.
Atala is the first GMS of its kind designed for radical transparency, reduced bias, increased efficiency, and uncompromised equity within the funding process.
It encourages the best science at every stage of the process through a series of bias checks and paves the way not just for mental health research, but for all research grants.
We’re incredibly excited to watch it grow and better mental health for all.
