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New Directions in Research Integrity: Values to Value in Research and Publishing
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New Directions in Research Integrity: Values to Value in Research and Publishing
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Segment:0 .
Good to see everyone. My name is Jay Kelleher. I'm the director of sales at Mercier. And we're happy to sponsor the keynote, which is about research integrity, which is an enormous focus for what we do at Mercier. We work with societies and publishers with our early stage research platform, but we're laser focused on research integrity these days.
So if you want to continue the conversation, this is my shameless plug for get in touch with me or get in touch with Mercier. We'd love to get you a trial, give you some exposure to the system, show you what we can do. With that said, let me induce my friend introduce my friend Simone Taylor, who's publisher and chief of publishing operations at the American Psychiatric Association. Simone good afternoon, everyone.
Back in 2009, a survey by Daniele Fanelli, published in PLOS found that approximately 2% of scientists admitted to have modified their research results at least once, and nearly 34% admitted other problematic practices in research. In 2012. A review by ferric Fang et Al. On retracted articles indexed in PubMed published by the proceedings of the founded, found that while 21% of retractions were attributed to error, 67% were because of misconduct.
And only last month, climate researcher Patrick Brown told us that he had left out the full truth to get his paper published in Nature. Where do the solutions lie in ensuring adherence to standards that will guide the performance and reporting of research. Why is this important. Our speaker today is an associate professor of Psychiatry and director of the Center for Bioethics at Harvard Medical School.
She is also admitted to the Massachusetts bar. This intriguing background has allowed her to focus her work in education and research at the interface of psychiatry, medicine, law, ethics and human rights. Who better to lead us in a conversation on the values at stake in the research enterprise and how these values influence equity and social justice. Please join me in a warm welcome for Dr. Rebecca Brendel.
Thank you so much, Simone, and Thank you for having me. This was really don't get to write a lot of new lectures these days. And this really gave me an opportunity to think about the very basis of how we do our work in academia and how we think about research and how we think about the impact we can have on really what the goal of ethics is, which is to make the world a better place and to promote flourishing of all living things.
So with that, let me just start with my disclosures. I'm going to talk a lot about transparency. So I'm trying to do a not just a do what I say, but a do what I say and a do what I do. So I'm employed full time by Massachusetts General Hospital and Harvard Medical School. I get some royalties and honoraria for publications and speaking, and I have some uncompensated roles that do play may be implicated.
So I'm immediate past president of the American Psychiatric Association. So I do sit on the executive committee of our board, and I'm an appointed member of the American Medical Association council on ethical and Judicial Affairs. And I put that up there, mostly because anything I say, especially if it gets me into trouble, is my own opinion and not the opinion of the AMA. And I am a consultant for the community advisory board of osmonde, which is a mental health electronic medical record.
And that might be a little bit relevant. I have not received any compensation to date for that. All right. So I was trying to think about not doing yet another ethics talk that leaves everybody feeling anxious. I am, after all, a psychiatrist, too, in my day job. So especially one on the last thing between all of you and a lovely mingling and Happy Hour in the end of the day. But in thinking about why ethics matters and I wanted to really start with the kind of landscape that I find myself in every day at Harvard.
So we're in this publish or perish situation. So publishing is such an important and key metric for measurement of academic impact and success. We also care because good science has the potential to advance our knowledge and our health and other aims that matter to us. So it matters for academia, it matters for our science, and it also matters for the public, right. The public is aware of what's going on, but there's increasing skepticism, as we about what is fact and what's real and what's fabricated and not real or even what has evidence.
But isn't the right kind of evidence or isn't the convincing kind of evidence. And we've all as Simone just told us, we've been in a situation where we have increasingly public awareness and coverage of problems with research integrity. So we'll go into that a little bit, too. But why ethics, right. So ethics is in the simplest terms I could come up with, and I realize it's still a lot of words and a lot of them have more than three syllables.
But it's the discipline of philosophy dealing with questions of what is good. And then also what is bad, what's right, and then also what's wrong, and then what are moral responsibilities, what our commitments ought to be right to the things that matter. And values are one of the ways that we express all of these ideas. Of guiding commitments and principles.
And they're not always determinative. In fact, when ethics gets really complicated, it's when we have one or more values that come into conflict with each other. And then we have to figure out what the right thing to do is right. So a good example of this would be what do we do with potentially useful but ill begotten research research that did not attend to the humanity of the participants or did not use informed consent or even worse created was obtained through misconduct or abuse.
So in thinking about what values guided that we might talk about today, I was really lucky because when I got the slide template, your core values were right there in one of the slides. And so I really want to do is the title includes research integrity. So we're going to talk about research integrity. But I also am going to towards the end of the talk, come back and talk more not just about the conduct of the research, but also about how integrity really matters in terms of the kind of research that we do and what publishers.
Well, you can tell me if I'm wrong, but what publishers or journals might do in thinking about how we can advance the good. All right. So what is integrity. I was a little nervous, actually, about going to Merriam Webster to start, but I wanted to start with something basic. And the reason I couldn't be here with all of you today, the whole day, is that we had our annual lecture on reproductive rights and ethics this morning, first thing in the morning.
Fortunately, we could do both because the surgeons do everything before o'clock in the morning. But the speaker started with three definitions from the dictionary. So I'm in good company today. But let's talk about what integrity means, because I think there are three really important, three really important things to talk about. So one kind of integrity, which is, I think, probably the first thing we think of or at least the first thing I think of but that might be an occupational hazard is this idea of a firm adherence to a code of especially moral or artistic values.
Since I have no artistic talent, I stick with moral values. But really, this idea of incorruptibility that we have something that's a part of our character and our attitudes and our practice and the way we live our lives, right that we can't be we won't compromise them. And so we hold researchers to a very high standard in that respect. And then there are two other and related concepts of integrity, which are a little bit different, right.
This idea of being sound or unimpaired or the quality or state of being complete or undivided completeness. And you can see how these two have a lot to do with the science and our research. So what about research integrity. So I actually didn't have to try very hard to find these articles. You've all you've all seen them.
In fairness, the eight papers that were retracted, Simone sent me that. That one was a fabricated co-collaborator with the name of a prestigious University that the submitter on eight articles to Elsevier put there in order to increase the chances that the paper would get published. We know also that the former president of Stanford resigned admits questions about the integrity of his data.
So the outcome was not a smoking gun, at least to those of us who are lay in our ability to appreciate what that investigation was about. But it became so damaging that he felt he resigned nonetheless, feeling that it would really impair the ability, the reputation of the school and the ability to go on with important work. And then the most recent article, the one on the right, was about the Harvard professor, Francesca Gino.
She was doing studies about dishonesty, in fact, and her research, she collaborated with a well known social scientist, Dan Ariely. But their research together and her individual research began to raise questions about how she interpreted her data and whether, in fact, it was dishonest itself. And she's been suspended from her job. This just came out this weekend as I was going through the final slides for this lecture.
So this isn't a lot of places. It brings up a lot of questions for the kinds of work that we do because the public is reading it and wondering, what are these journals doing. How is this happening. Why don't we know about it. All right. So sometimes we can make mistakes, right. But I think this actually goes a little bit deeper.
And this is the 2012 paper that Simone referred to. But this paper found that it wasn't just mistakes, it was actual misconduct that was responsible for more than 2/3 of the articles that were retracted. So this was a detailed review of over 2000 articles that were retracted as of May of 2012. So there's no reason it's a little bit old and dated, but there's no reason to believe that we're doing any better or any worse now, especially with the publicity surrounding all the cases that are coming out on nearly a daily, weekly, if not daily basis.
So one question we could ask is, well, why would people do this. And I think there's a lot that ethics can say about this. And there are a couple of reasons. This is an info page done by the Office of research integrity at HHS. Many of you may have seen it before, but I thought it was really, really helpful. So the one thing about this is it's really, really high stakes, right.
It's really important to get published. We talked about publish or perish. We talked about the academic currency. And there's a lot of reasons why people might make mistakes in general. The anthropologists who and sociologists who have studied human behavior around mistakes in health care, that's part of the ethics literature have come up with two different categories of error.
One kind of error is a technical error. You didn't have enough training. Nobody ever taught you how to do that. That's the top right hand corner. And maybe even you had poor supervision. Nobody was helping you along the way. And I want to start with those two. But then there are these other kinds of errors that are really look at people's characters and behaviors.
Their errors of judgment. And those are a little more complicated. So what about these errors, these technical types of errors, as the sociologist Charles Bass called them. So these areas of error. That's the place where you can actually train people and give people skills. So if you don't know what to do can have core competencies. You can require people at every level of training to learn about the different domains of how you handle data and how you design research and how you manage that data and how you check it and how you analyze it.
And you can also go one step further and teach people about how to mentor and supervise. So that we can pass it on and pay it forward. We can continue to take that work and help others do better work. And then you can practice a lot. You can set expectations for collaboration and independent review and guidance. So that it doesn't become career ending if there's a mistake, but rather an opportunity for learning.
I'll say a little bit more about that when we talk about errors of judgment and then some of the resources, every one of the things our ethics center does is we collaborate with the responsible conduct of research program, which all the postdocs are required to take. We have a scientific citizenship initiative that talks about what can happen in a laboratory environment and how we communicate about science and set our standards. But there are really clear ways that we can build skills, right, which is one important component.
And skills matter. When we come back to this notion of integrity as soundness and completeness, you have to have the research skills. I actually I was a terrible researcher. Like I could make a sequencing gel crack in half from across the room, maybe even from the building next door. So it was good because I learned very quickly that I didn't have the aptitude for that kind of scientific research.
But you do need skills. You need even those basic skills about how not to break the glass. They might not even be glass anymore. I don't know. I'm dating myself. But the other piece of it, right, more than just the soundness and completeness is that skill really matters the most when the stakes are high.
And so this is really, really high stakes, right. If your post-doc work doesn't fit it from the very beginning, right. If your thesis work doesn't finish, you don't get your PhD. If you don't meet the metrics that your PI wants as a postdoc, you don't get to move on and get your own independent lab, right. If you don't publish, none of those things are going to happen, right. So the stakes are really high.
And so I want to give an example that for me always helps me remember this idea about high stakes and the importance of skill. All right. So how many people have gone to a team building or a leadership seminar and done the marshmallow challenge. People know what this is. All right. A couple people.
So this is a really highly technical, like hard core activity. So here's what happens. You get into a group of five, even though there are only four people on the screen, it might even be an AI generated image. I don't know. But OK, so you get five people, five around a table and you get 20 sticks of spaghetti, a yard of tape and a yard of string and there is a scissors.
People have asked me that. So you do have implements to cut the tape and the string. And the goal is you have to build the tallest structure that will support the marshmallow on top, the unadulterated marshmallow. OK, so you can't cut it into little pieces. You have to put the marshmallow on top. And it turns out that this is a little bit harder than it seems it's going to be when you take all the materials and look at them and put them on the table.
And so there's this. This prototyper and software designer Tom wujec, who actually studied the marshmallow challenge because he was really interested in knowing how people developed prototypes, how people work in teams and trying to come up with how to have creative environments and processes that foster people's work. All right. So if you want to watch it, there's a Ted Talk called the marshmallow challenge.
And it's great. Lots and lots of learning. But here's what happened, right. So if you give 10 teams, and he's done this with thousands and thousands of people from Fortune 500 CEOs all the way to recent kindergarten graduates who, by the way, are particularly talented at this. So you.
So if you do that, about 60% of the teams will be able to build something that stands and supports a marshmallow with a height that goes anywhere from, say, about 2020 2 inches all the way down to zero. So that's how the team performs. So he decided, well, I want to see what makes teams do better. So he went into the next series of marshmallow challenges and says it's a little dated now, but he said, I will give the winner whoever builds the tallest marshmallow tower, a $10,000 package of software from my company.
And what do you think happened. That's what happened. Nobody killed the talent, OK. This is a really, really important message, right. Low skill, right. People haven't done it before. Low skill and high stakes leads to bad outcomes. All right. That's why skills are so important when we talk about data integrity and about research integrity.
All right. Now he gave everyone a second chance. He said, well, I'm not screwed, right. There's $10,000 on the table here, guys, so I'm going to give you four months and we're going to work on some team building skills and maybe you'll practice, but you're going to come back in four months. And that $10,000 software package is still up for grabs. So what happens four months later.
Not only right did 90% of the teams build a tower that stood, but the average height of the team of the team. I don't know about that. But the average height of the tower. Went from about 2022 to almost 40. It almost doubled. So here's something else.
High stakes and high skill, right. That's the sweet spot. All right. So keep that in the back of our mind because it might ask us to want to have questions about what kinds of research, training and backgrounds scientists should have or what kinds of questions we might ask on submission, for example, about the circumstances and the environments in which people conduct or the labs in which people conduct their research.
All right, so that's the marshmallow challenge. And this is the sweet spot, right. High stakes and high skill and practice lead to the best outcomes. All right. So that's the skill part of it, right. That's the we don't really know how to do the research. We haven't been properly trained. We're using electronic lab notebooks, but we're sort of muddling our way through.
And maybe we're not inputting data the right way. Those are the skill competencies. But then there are these other professional, personal and judgment competencies, right. So I was under a lot of pressure. If I didn't get I did. I committed research, misconduct, and these are direct quotes from investigations from the Office of research integrity.
I needed to get into a high profile journal. Otherwise I wasn't going to get a job right. Or my immigration status. I was going to have to move right if I didn't have a good outcome. So personal circumstances and also individual psychology, right. I didn't want to be the person who wasn't performing in the lab.
That really mattered. So errors of judgment. So what can we say about errors of judgment. Well, coming back to integrity, right. This is number one. So we have this idea that we as people, if we're committed to doing good work if we're good scientists, if we're good professionals. Our values ought to be incorruptible that if we hold on to them as true, then we're going to act in accordance with our values.
All right. It turns out maybe not so much. So I'm going to give you some examples. These are psychology studies, but I think they illustrate the point. Well, and this is really about illustrating the point and remembering. So you're going to remember the high skill and the high stakes from the marshmallows.
You won't forget that. So let me tell you about a couple of studies. So people it turns out the take home point here is that we're really sensitive to and influenced by our environments. So no matter how much we hold on to what our values are, no matter how cultivated our character is, we're going to be led astray sometimes. And our behavior is going to be influenced by what's going on around us.
OK, so here's a very simple example about this. So remember who's old enough to remember payphone? Booth I almost can't even use this example anymore. Like, my kids are like, mom, like, what are you talking about. You mean those things in London. Those red things, those are just there for show. So So here. This is a phone booth.
Bystander example. So someone's in a phone booth and they set it up about whether you got a coin that came out in the coin return. People don't even use coins in vending machines anymore. So there's a coin there. All right. So if you found and you walk out of the phone booth and there is a bystander there that drops a folder full of papers, it turns out that if you found the coin in the slot, if you felt lucky, if you had a positive experience, almost 90% almost 9 out of 10 people would stop and help the person pick up their folder.
If you didn't feel so lucky and so disposed to being positive, only 4% would stop when they came out of the phone booth in order to help a stranger. Who would think that such a little thing could be so determinative of our altruism. OK, there is another. I mean, shopping malls, it turns out, are kind of dangerous places to go if you want to be if you don't want to be in a psychological study.
So they're in, on the other side of the mall while they're doing the phone booth. There's somebody standing outside in the mall asking for change for $1. We don't have to do that either. How many people remember having to get change to get a nickel for the parking meter. So we asked for change for $1 and they did it in one place. So it was just sort of in the middle of the mall.
And then they did it near a bakery. All right. Who do you think was more likely to stop and give change. The person who is in a pleasant environment that smells good. But the one that really stood out to me were seminary students. Seminarians, people who are devoting themselves to a higher calling and to the good in the world. So they told some seminary students, they divided them up into three groups on their way from one class to another class.
And they told one group, you are in a really big heart. They told the second group, you're in a little bit of a hurry. Please don't be late. And they said nothing to the third group. So as they're on their way, right. A bystander falls down and is groaning in pain and asking for help on the ground. So I don't know what we want to say about this, but 63% of people who weren't in a hurry stopped to help.
That went down to 10% Just being in a hurry in a big rush. Makes us less compassionate. OK that's really important, especially if you work in a hospital like I do that's really busy all the time. Like every morning, right. We are on we are on overcapacity alert. It's going to be a long wait. Please send people other places.
But the important point of this is not that this is determinative of whether we're good or bad because of action in one particular moment, but just that our environment really does influence us. So when we think about these questions of how do we promote behavior that comports with our notions of research integrity, right. We also have to think about the kinds of environments that we foster, right.
We can foster environments that bring out our better angels, right. Or our worst demons. And I think that that's something that certainly matters when we're talking about errors of judgment. The kinds of things that say, when I have a choice, I'm actually going to go away from what my commitment is because I'm being pressured by something else.
So we. So learning environments and productive environments are really important. All right. So what are some of the features for success in environments. So the environment could be being in a place with beautiful light, feeling good about being in a building, right. That's not contributing to global warming.
Being in a place that smells good, right. Putting your office over a bakery, well, that might have some other problems, but. OK, so. But the idea right back to the marshmallow, some of the things that mattered that Tom Talmudic talks about is what happens when you're quick and you're thrown into something versus having relationships that allow for learning and for trust.
The well, this is a spoiler alert, but I assume people have things to do other than watching the Ted Talk tonight. But the Fortune 500 CEOs didn't do so well. Recent business school graduates did the worst on the marshmallow challenge. As Tom says in the video, they were all trying to be the CEO of spaghetti Inc. But the people who did the best.
Were the recent kindergarten graduates because they were willing to prototype and try things out and they really weren't worried about what failed or didn't. And it was that kind of collaboration as opposed to keeping positions of power that really mattered. So environments that are collaborative, environments that are open to trying out new ideas. Not needing to go about evaluating or looking at the measures or the worth of the science, because that's the way we always did it.
But keeping an open mind to what is high impact research. And then the other thing that's really interesting is that the CEOs didn't do so well. But when you put their executive administrators with them, they did really, really great. What does that matter. Because oftentimes, especially amongst those of us who are big thinkers, we don't have the right kind of structure in place to really get the work done.
We don't have that. The skill set around the executive elements that really matter, like the executive in terms of the planning and the doing. And so thinking about labs and environments that really structure provide a structure for the work can also be key features to success. All right. You've done some things right to create an environment that's a learning environment.
So your code of conduct talks about the values that matter. And the way you're going to run this meeting and your work in order to create an environment. We similarly at Harvard Medical School have a community learning commitment to make sure that we're doing our very best to not create groupthink, but to welcome in different and dissenting ideas to make our work better. Including the kinds of things that came out in the marshmallow challenge.
Like we all make mistakes, right. So making mistakes is not the end of the world. We have to figure out how to correct them. Those would be technical mistakes, not judgment mistakes, how we listen, how we speak, and also how we listen. Being respectful of other people, bringing our empathy, being open minded. And just because recognizing that just because we believe something doesn't mean that it's true.
Evidence and opinion are different. All right. And so what are some of the other things we can do. So I wanted to show you some of the kinds of structures and programs that we can put into place to help our work along to promote research integrity. So this is the Harvard Medical School r-3 project. And its mission is to support a culture that advances research, rigor, reproducibility and responsibility.
And the thing that I want to really point out about this, many, many institutions have these kinds of programs, right. But the thing that I wanted to point out that is really important is a lot of this is technical stuff. The kinds of research design, data management analysis and interpretation standards and education about scholarly dissemination. But then there's also the light blue on the left side.
The light blue part of the pie is about scientific culture and community. So that you can't do one without the other. And the kinds of things that fall in scientific culture and community are leadership, mentorship, responsibilities, and ethics policies and standards and academic environments. So when you think about academic environments and advancement, right, if you have variable reinforcement and you don't know what the standards really are, right.
It can create an environment that's unpredictable and stressful and high stakes, right. Without the knowledge about how to get there. So both practice and environment are going to be really important for our academic integrity. OK a couple of other things, right. So there's this whole thing about negative studies or things that don't work out. We call them failures.
Probably not failures. Actually we would let ourselves learn from our failures. We could be great. So the psychologist Dan Quayle wrote this wonderful book called The Talent Code. But what he wanted to figure out in this book, it had nothing to do with science, really. But what he wanted to figure out in this book was how some of these athletes became such great elite athletes.
And what he did is he said, well, they must have these incredible training facilities in Latin America where many of them came from with these really sophisticated technology. And what are they doing. It turns out that there were chain link fences, maybe some pavement And some soccer balls and some really good coaches and what they looked for in the athletes. What he saw was that the athletes who became elite athletes as opposed to excellent athletes were those who practiced the things they weren't good at.
They were able to discern where their shortcomings and limitations were. And practice and practice and practice until they got it right. So being able, rather than to see things as failure, as opportunities for learning and change is really, really key at being excellent. We also know that talent is work. So we think about when you think about Malcolm Gladwell and outliers.
10,000 hours, you can be really, really musical if you want to be a concert musician. 10,000 hours. That's a lot of time, right. But this doesn't come quickly. So practice is what can lead to mastery. And if we take this all together, right, a great scientist, one of the great scientists of all time, Louis Pasteur.
Famously said chance favors the prepared mind. So making ourselves prepared to welcome what comes, even if it initially seems like failure. So there is quite a lot of value actually in negative studies, especially large clinical trials. In my field, we haven't had a really revolutionary medication advancement for a major psychiatric illness since I was in medical school. That was a really long time ago.
I'm not going to tell you how long. But But it was some time when there were where you needed nickels for parking meters. And there were payphones. There weren't any cell phones. So it's a while. You'll have to take my word for it. So I think here in this space, as we think about what would change the environment and what would change our ability to do good research, that there really are opportunities for publishing to lead on this.
So are there common features in failures of integrity. Both soundness and soundness and completeness, right. So when we look at all these retractions, right, since 2012, what else can we learn from other articles. Are there patterns you could even use for this. Are there patterns. Are there things that we might be able to see in submissions, right. That might lead us or give us a clue, right that there's something we ought to be looking for, right.
How are we using or how effectively are we using independent reviews, looking at data analysis and analytics and the methods. And then there may I think there are also probably really good opportunities to promote integrity in terms of the moral commitment as well. So if we think about failure in a different kind of way, we think about negative studies and we think about results that are unexpected.
Is there a way to embrace that as part of our learning as diminishing research waste. And that because if we don't know about negative trials or negative studies, people reproduce them instead of studying other things, right. So are we missing opportunities for learning and is there a way to shift the environment in the paradigm. I'm just thinking out loud, that's your expertise, not mine. But I hope we'll have time to talk about that a little bit at the end.
All right. So what about so integrity is a big piece of that. But there were four things right, in your values. And so we talked about research in terms of soundness and completeness. We talked about concepts of competence. Obviously, transparency and honesty come into the judgment part of integrity and also how much our environment matters.
But we're also in this really rapidly changing ecosystem, right. So I think that some of the questions we want to ask I gave a couple very feeble examples because I'm clearly not an expert in your area, but about how publishers and journals can incentivize or encourage research integrity and prevent deviations. But then there's this other piece of it, too, which is not just about the conduct of research or the research questions that we're asking.
But about promoting the good or our desired outcomes. So when you look back on the value statement, it's really about things like inclusivity, right. How do we advance and think about equity and diversity in our research. So health inequities are not a small matter. There are estimates that these health inequities account for $320 billion of spending every year in just the United States.
So if we're not conducting research that promotes equity, right, we're spending a lot of money on things that we could be using to promote good in other ways. Racialized minorities continue to have poorer outcomes for chronic conditions than others in our population. We know about the studies about zip codes being major predictors of life expectancy, and at least now we're talking about social determinants of health.
But there's a lot more in here also. And it's not just racialized minorities in my field. In psychiatry, we know that persons with major mental illness have a 25 year shorter life expectancy than the general population, and that's for the same zip code. So we know that there are some people with some illnesses, right. Who do who do do not benefit as much from our science and our treatments.
All right. So I think there are a lot of challenges in this. I think there's some really low hanging fruit in terms of some possibilities as well. And so some of the measures that have been recommended time and time again by those who have looked at problems of health disparities and made recommendations about what we could do to reduce disparities, some have been around standardized race measurements across health systems.
I call this the table 1 problem. So I don't know. I have a target on my forehead or something. So, I'm like a 23-year-old medical student and I'm sitting in my primary care clinic and this older man comes in and he says, well, where's my doctor. And I said your doctor will be here soon. I'm helping out today. And he said, well, I'll just wait for him.
And I said, no, no, no, you can talk to me. I really know what I'm doing. This is like day one. Anyway, so he says he brings in this copy of Time magazine, right. And he says, I want this pill. It was Viagra. He wanted to know if it would work for him. So I said, well, I don't really but let's see what the doctor has to say.
At least I told the truth. So my attending physician said this is so great because you have to do a whole talk during your rotation in medicine. And so you should really ask that question, is this medication going to help him. And so my table One example is when I went there, there was nobody included in that study. Who had chronic hypertension, who had vasculopathy, who we could identify their race or their and there was nobody included who was in the age group of my patient.
So when we want to think about it, and yet that was again, in the Stone Age. But we still have that problem. We are not doing a great job of doing standardized race and ethnicity measurements across health systems and across our research. So that's one very obvious recommendation, but also effective interventions, right. We often don't study interventions, to see if they work.
We come up with them. We say this is what the data is. Are there differences across populations. Are we asking the right questions and do we have a diverse enough workforce to be incentivizing the research that matters to those who are not in groups that have dominated the questioning and the research for a very long period of time, including women, by the way.
And then what about technology. How can it help us. Are there ways to leverage technology to do better at inequity and are there opportunities if we actually do a good enough job of looking at differences between different groups of people to embrace personalized medicine, to enhance health outcomes and address disparities. So I think that there are some things that journals and publishers can do in terms of this.
We talked about the table 1 problem, but I think if you can't publish a paper, if you don't have if you don't have identification of race, gender, ethnicity, people are going to start doing it. It's a really, really big incentive. And then setting metrics, but also measuring them and publishing them, being transparent about what's happening and what's not happening. I'm a big fan of report cards on websites.
Like, here's where we're doing well, here's where we're not doing well because it nothing like having to air one's dirty laundry to help one use the washer a little bit more. So to speak. So I think it really, really helpful thinking about effective interventions. One of the things is that health services research often gets underfunded or looking at standard therapies and how effective they are.
It's kind of hard to make that look really, really exciting. Year two of thunder or two, right or two a journal. So I think publication around implementation and impact is something really big that comes up. I'm in a Department of Global Health and Social Medicine. The actual impact on the world of the implementation science many of my colleagues are doing is much greater than some of the science that gets published. But it doesn't get incentivized because it doesn't get published the same way and it doesn't get funded the same way.
And so there's not a lot of academic right. We do it because it's a labor of love, but not because it's necessarily going to be promotable. And then really thinking about workforce diversity in terms of editorial boards, but also self-identification from authors and then real metrics from submission to publication to identify opportunities for interventions to advance equity. All right.
I want to also talk about the fourth recommendation technology. This also brings up your core value of adaptability. I couldn't possibly do a talk about anything in the world right now without talking about AI. So as we come to the end of my slides, I want to say a little bit about AI and technology. So experts, which I'm not one experts who are really looking at the uptake of AI and the cycles of hype and applicability tell us that.
That generative AI tools and AI algorithms will be routinely functioning in clinical practice within the next decade and probably within the next five years. And all of these tools are based in large data sets. So we need to be doing things to encourage broad, Representative and diverse data sets. There is a huge possibility for great benefit. I'll talk about that a little bit.
But I was just at a conference about ethical leadership, and we're all sitting in a group, and the problem that was given to us was this example of whether a practice should use a dermatology. A dermatology practice should use an AI algorithm, which did a much better job of identifying skin lesions in light skinned individuals as opposed to those with darker skin tones.
And so we're all sitting there saying, this is not ready for prime time. Why can't the dermatology lists just look at it. Why are we trying to stratify? And there was a radiologist in the group who said what, I think AI saves lives in my practice. It's not that I don't go read the films myself, but I have a computer that can look at these images as they come out and tell me which ones are likely to be high risk.
So I can look at them first so that if there's somebody who needs an intervention, it can actually help me use my time better. So they're already being used and there's this possibility for great disruption. But we have to make sure that we're adequately powering them and know that the algorithms are only as good as data input. The problem with this is that it's really costly to do data science projects, right.
So data science is expensive and time consuming 7 out of 10 projects fail. The average cost is about $200,000. And it's also hard to know what the definition of success is right. So the companies who are doing this, the definition of success is, can I make more money off of this. But the definition of success for us might be something different.
How likely is this to enhance health equity or access. Or treatment of disease. So I think we need to really ask the questions about what we expect of AI and what we expect of our technology. And I think that and also right, this is a great opportunity for impact. But in order for these algorithms to work, in order for generative AI to be able to help us with our problem solving, we need to have high quality clinical data.
This is where mental health again is in trouble, right. There is. OK well, I have to say it now because I started, but I'll say really quickly because we'll end on time. But there was this study done of psychiatrists and patients asking the psychiatrist, how much time do you spend talking in the session. And then asking the patient. So the psychiatrist said no, no, I listen the whole entire time.
And the patients were like, I can't get them to shut up. They're all constantly talking. The point of that is that when you look at mental health, electronic medical records, we are not rule followers. We don't like radio buttons. There's no Yes or no. There's a lot of narrative there. It's really hard to import that from a clinical record into a data set.
So again, we're going to have to think about ways both at the level of practice. And when we look at the power of the research that gets generated and the algorithms that get set up, how are we driving this data and what can we do to improve the quality of the data. So we can close that 25 year gap in life expectancy for persons with major mental illness. We have got to incentivize self-identification, right.
And keeping data about race and ethnicity. I'm thinking about I think journals if we ask about universal domains for data collection. So we just don't accept and publish papers that don't have adequate data, data inputs to address questions of equity and to really be broadly applicable. If we don't publish that from the beginning and say what we expect, perhaps we can drive that.
And then also finally really partnership with communities around this. So that no one's the science isn't going to do very much. If we don't establish ourselves as those who both do the research and promulgate it through publication. If we don't establish ourselves as worthy of that trust and we don't demonstrate benefit for persons who participate in that research.
All right. A couple other things to think about. You guys like we need your help on this copyright and basic integrity of generative AI. So there's this question. Some of it's true, some of it's not. So we've tested it out. We're figuring out how to use it all in academia, citations that don't exist.
I've seen my own words cited, other people and two people articles that actually have never been published. How do we protect authorship and work product. So it can't just be taken away or mass distributed misattribution accuracy, new kind of computer plagiarism. What When I was in medical school, we weren't allowed to use well, it wasn't called PubMed then.
It was a very archaic version of Medline, but we weren't allowed to use it because we were told, well, how are you ever going to figure something out if you don't know how to go into a library stack and find the journal and make a copy and read it. Yeah, well, fast forward during COVID, they turned our main journal reading room in the Countway Library of Medicine into a cafe, and people actually go there now.
So I just wanted to make sure if everyone was awake. But it's actually, it's true. OK So, we can't just be naysayers. We have to adopt things that are going to help us. But how do we come up with transparency and disclosure around it. How do we come up with standards for what's actually taken versus AI generated. How do we detect and evaluate it.
And how do we define parameters of use. What are the industry standards going to be for permissibility? And in all of this, not forgetting, even though it's really scary, like, I have kids in high school, so I've just gone through the second round of the dystopian 10th grade novels and boy, Ray Bradberry is really onto something with Fahrenheit 451 just saying. So I'm scared of it, too, right.
Like I'm prepared to say that this could be the end of us. But I think we can't forget about the opportunities for benefit either. All right. So as we're rounding out, I hope that this little these musings at the end of your day. Thank you for staying awake. I really appreciate it. But that value is really can drive value, meaning good and benefit from research.
But not alone. We can stand by the things that are important to us and the things that we think make our work legitimate. We can have personal commitment, but we also need to have high skill and supportive and enabling environments. Integrity considerations really do matter in both skill, right and character and commitments.
But we also have to think about the outcomes and goals that we think are important things like equity. And things like advancing health and other values. And really just to say that there's so much opportunity for leadership in this rapidly evolving ecosystem. And those of us who are on the other side sending you endless submissions and tweaking them and changing them, we need your guidance and your leadership in helping us to navigate this rapidly evolving ecosystem.
Thank you so much for inviting me to be with you, and I'd be happy to answer any questions. Just OK, since there's no soapbox here, I will not go on a whole thing about AI and dark skin and my articles. Really, really big problem. What I did take away, though, is as a publisher, we find that.
We don't influence the research funders. The research funders influence us. How do we influence the research funders? Yeah well, I ask myself the same question every day. No, but. But I. I I think it's important. To rip the Band-Aid off what the implicit biases are in this and what gets funded and what doesn't get funded.
So I think there are ways of looking at the number of submissions and relatively and thinking about what areas are overpopulated within the literature. Like how many me too studies do we have, for example, of antidepressants right at a slightly higher dose with a rating scale that nobody actually uses clinically and isn't sure is clinically relevant. So I'm being a little bit sarcastic, but only just a very tiny little bit.
But if we said something about the number of papers that get submitted in certain areas and the number of papers that don't get submitted in other really critical areas like health inequities and disparities that are costing us $320 billion a year. That's a lot bigger than any grant that any state is getting under the Affordable Care Act for Medicaid expansion. That's a lot of money.
So I think that we have to actually do use the data that we do have to bring numbers and figure out how to characterize it to influence those who hold the purse strings. I think there's a lot of power right from where I stand. There's a lot there's a lot of power in what gets published and what doesn't get. The other thing is, things aren't getting if things aren't getting published, then people aren't getting promoted and institutions then don't value that work.
And so it's really important that we bring those values first and saying what research is going to be valued in lots of different ways. I'm totally in favor of science. I think we should be doing lots of good science and if we had endless funds, it would be great to fund all of it. But we really have to ask some hard questions.
Who's being included, but also who's not being included and who's not benefiting. But I'll get on your soapbox if any day. Any other questions. I'm only taking questions from people who laughed at my jokes. So I know you're saying they were good. So I'm intrigued as to whether you had success at Harvard Medical School in changing the way things were done.
Because one of the constants across.
Come up with more, maybe like a year from now, but it's only been a couple of months. But actually, instead of there's also there's often this feeling like there's this great cartoon that says, run this by legal, but don't let ethics see you on the way. The idea that the ethics has always come in and they tell you that you're bad and you're doing the wrong thing and you have to do it differently.
Or we make this mistake of generating our own questions because they're philosophically intriguing, but they're totally out in left field from what the scientists would be doing. They don't even know that there's. So one of the things that we're trying to do is embed ourselves at the ground level and understand how scientists are making decisions about what to study and about how they're going to conduct research and using that right to actually generate the kinds of questions and considerations that we're thinking about.
So that we're there right along with them, not just when we run into trouble. And that's really hard because we have a bad reputation of telling people what to do. And then ethically, as many ethicists get into trouble as researchers. It's just because we know what the right thing to do is doesn't we always do it. So I think changing that narrative is really important from the ethics perspective.
But we have a lot of work to do. We have a lot of work to do. And how we keep doing that is we just keep plugging along one foot after another and start by asking the questions. Great question. The last question. This was a wonderful talk and Yeah, maybe a little bit apprehensive.
This morning we heard a panel and one of the speakers on the panel was Susanna Ramirez from the University of California Merced. And she teed you up kind of perfectly for us at the end of the day, because her talk was about how the National Institute of Health, due to the way it's organized, do the way the funding works, contributes to the ongoing health inequities.
So we were all sitting here like we're just sort of primed for this. But she had a chart about how the way the NIH was organized. And if you weren't and how you can't remember if somebody took better notes than I did. Like if you wanted to study something generally about say, nutrition, but there's not a division on nutrition. You have to find a disease that could be connected with that.
And it was just like it made just whack a doodle, right. There's just bananas. And so I wondered if there's any conversations about changing the structure of the NIH was probably like worse than Harvard, even though it hasn't been around longer. But to try to get at the root of that. And I just love to get your perspective. Yeah mean.
Well, look, if we're not doing the research that we need to do. and it's getting in the way of funding things that we know we ought to be doing in accordance with our values, then we need to figure out how to change it. I don't know enough about the structure or about the funding of research. I've never I've never even been part of an NIH grant. But but this story.
And is not an uncommon one. Like I stopped looking for funding through the federal government because what I was doing didn't fit in right to the way things were organized. Now, there's a lot of problems with that. Because if you're in an institution and even if you have donors, right, but if you're getting grants from foundations and private funding sources, they probably don't have the same indirect and overhead, right that the NIH grants do.
So the. So then you're not as valuable, right. Or you can't hire as many people on your team or you can't mentor more young people to come along and continue the work. Like that's the greatest thing of being in academia is all the students you meet every single day who are well, who are smarter than me at least, right. And then I say, OK, I can sleep tonight because they're going to carry this forward and continue to change the world so that it just it has so many downstream effects that we got to figure out how to get it right and we've got to invest in it.
So how that happens on a pan institutional level, I don't have a great answer. But the first thing is that people have to be aware of it. And so if we see that we're not doing good research in particular areas that matter, right, even if we're looking at very narrow questions like which chronic illnesses are costing cost Medicare the most, right. It's easy. Like there's lots of Medicare data.
So you can crunch it pretty I don't want to say easily because I couldn't do it and I certainly couldn't do it easily. But it's accessible, right. So there are ways of getting at some of this data to make the point. But it's a great Thank you for sharing that from this morning. I'm sorry you couldn't be here for that. Yeah so I'm looking forward to it. Thank you.
Segment:0 .
Good to see everyone. My name is Jay Kelleher. I'm the director of sales at Mercier. And we're happy to sponsor the keynote, which is about research integrity, which is an enormous focus for what we do at Mercier. We work with societies and publishers with our early stage research platform, but we're laser focused on research integrity these days.
So if you want to continue the conversation, this is my shameless plug for get in touch with me or get in touch with Mercier. We'd love to get you a trial, give you some exposure to the system, show you what we can do. With that said, let me induce my friend introduce my friend Simone Taylor, who's publisher and chief of publishing operations at the American Psychiatric Association. Simone good afternoon, everyone.
Back in 2009, a survey by Daniele Fanelli, published in PLOS found that approximately 2% of scientists admitted to have modified their research results at least once, and nearly 34% admitted other problematic practices in research. In 2012. A review by ferric Fang et Al. On retracted articles indexed in PubMed published by the proceedings of the founded, found that while 21% of retractions were attributed to error, 67% were because of misconduct.
And only last month, climate researcher Patrick Brown told us that he had left out the full truth to get his paper published in Nature. Where do the solutions lie in ensuring adherence to standards that will guide the performance and reporting of research. Why is this important. Our speaker today is an associate professor of Psychiatry and director of the Center for Bioethics at Harvard Medical School.
She is also admitted to the Massachusetts bar. This intriguing background has allowed her to focus her work in education and research at the interface of psychiatry, medicine, law, ethics and human rights. Who better to lead us in a conversation on the values at stake in the research enterprise and how these values influence equity and social justice. Please join me in a warm welcome for Dr. Rebecca Brendel.
Thank you so much, Simone, and Thank you for having me. This was really don't get to write a lot of new lectures these days. And this really gave me an opportunity to think about the very basis of how we do our work in academia and how we think about research and how we think about the impact we can have on really what the goal of ethics is, which is to make the world a better place and to promote flourishing of all living things.
So with that, let me just start with my disclosures. I'm going to talk a lot about transparency. So I'm trying to do a not just a do what I say, but a do what I say and a do what I do. So I'm employed full time by Massachusetts General Hospital and Harvard Medical School. I get some royalties and honoraria for publications and speaking, and I have some uncompensated roles that do play may be implicated.
So I'm immediate past president of the American Psychiatric Association. So I do sit on the executive committee of our board, and I'm an appointed member of the American Medical Association council on ethical and Judicial Affairs. And I put that up there, mostly because anything I say, especially if it gets me into trouble, is my own opinion and not the opinion of the AMA. And I am a consultant for the community advisory board of osmonde, which is a mental health electronic medical record.
And that might be a little bit relevant. I have not received any compensation to date for that. All right. So I was trying to think about not doing yet another ethics talk that leaves everybody feeling anxious. I am, after all, a psychiatrist, too, in my day job. So especially one on the last thing between all of you and a lovely mingling and Happy Hour in the end of the day. But in thinking about why ethics matters and I wanted to really start with the kind of landscape that I find myself in every day at Harvard.
So we're in this publish or perish situation. So publishing is such an important and key metric for measurement of academic impact and success. We also care because good science has the potential to advance our knowledge and our health and other aims that matter to us. So it matters for academia, it matters for our science, and it also matters for the public, right. The public is aware of what's going on, but there's increasing skepticism, as we about what is fact and what's real and what's fabricated and not real or even what has evidence.
But isn't the right kind of evidence or isn't the convincing kind of evidence. And we've all as Simone just told us, we've been in a situation where we have increasingly public awareness and coverage of problems with research integrity. So we'll go into that a little bit, too. But why ethics, right. So ethics is in the simplest terms I could come up with, and I realize it's still a lot of words and a lot of them have more than three syllables.
But it's the discipline of philosophy dealing with questions of what is good. And then also what is bad, what's right, and then also what's wrong, and then what are moral responsibilities, what our commitments ought to be right to the things that matter. And values are one of the ways that we express all of these ideas. Of guiding commitments and principles.
And they're not always determinative. In fact, when ethics gets really complicated, it's when we have one or more values that come into conflict with each other. And then we have to figure out what the right thing to do is right. So a good example of this would be what do we do with potentially useful but ill begotten research research that did not attend to the humanity of the participants or did not use informed consent or even worse created was obtained through misconduct or abuse.
So in thinking about what values guided that we might talk about today, I was really lucky because when I got the slide template, your core values were right there in one of the slides. And so I really want to do is the title includes research integrity. So we're going to talk about research integrity. But I also am going to towards the end of the talk, come back and talk more not just about the conduct of the research, but also about how integrity really matters in terms of the kind of research that we do and what publishers.
Well, you can tell me if I'm wrong, but what publishers or journals might do in thinking about how we can advance the good. All right. So what is integrity. I was a little nervous, actually, about going to Merriam Webster to start, but I wanted to start with something basic. And the reason I couldn't be here with all of you today, the whole day, is that we had our annual lecture on reproductive rights and ethics this morning, first thing in the morning.
Fortunately, we could do both because the surgeons do everything before o'clock in the morning. But the speaker started with three definitions from the dictionary. So I'm in good company today. But let's talk about what integrity means, because I think there are three really important, three really important things to talk about. So one kind of integrity, which is, I think, probably the first thing we think of or at least the first thing I think of but that might be an occupational hazard is this idea of a firm adherence to a code of especially moral or artistic values.
Since I have no artistic talent, I stick with moral values. But really, this idea of incorruptibility that we have something that's a part of our character and our attitudes and our practice and the way we live our lives, right that we can't be we won't compromise them. And so we hold researchers to a very high standard in that respect. And then there are two other and related concepts of integrity, which are a little bit different, right.
This idea of being sound or unimpaired or the quality or state of being complete or undivided completeness. And you can see how these two have a lot to do with the science and our research. So what about research integrity. So I actually didn't have to try very hard to find these articles. You've all you've all seen them.
In fairness, the eight papers that were retracted, Simone sent me that. That one was a fabricated co-collaborator with the name of a prestigious University that the submitter on eight articles to Elsevier put there in order to increase the chances that the paper would get published. We know also that the former president of Stanford resigned admits questions about the integrity of his data.
So the outcome was not a smoking gun, at least to those of us who are lay in our ability to appreciate what that investigation was about. But it became so damaging that he felt he resigned nonetheless, feeling that it would really impair the ability, the reputation of the school and the ability to go on with important work. And then the most recent article, the one on the right, was about the Harvard professor, Francesca Gino.
She was doing studies about dishonesty, in fact, and her research, she collaborated with a well known social scientist, Dan Ariely. But their research together and her individual research began to raise questions about how she interpreted her data and whether, in fact, it was dishonest itself. And she's been suspended from her job. This just came out this weekend as I was going through the final slides for this lecture.
So this isn't a lot of places. It brings up a lot of questions for the kinds of work that we do because the public is reading it and wondering, what are these journals doing. How is this happening. Why don't we know about it. All right. So sometimes we can make mistakes, right. But I think this actually goes a little bit deeper.
And this is the 2012 paper that Simone referred to. But this paper found that it wasn't just mistakes, it was actual misconduct that was responsible for more than 2/3 of the articles that were retracted. So this was a detailed review of over 2000 articles that were retracted as of May of 2012. So there's no reason it's a little bit old and dated, but there's no reason to believe that we're doing any better or any worse now, especially with the publicity surrounding all the cases that are coming out on nearly a daily, weekly, if not daily basis.
So one question we could ask is, well, why would people do this. And I think there's a lot that ethics can say about this. And there are a couple of reasons. This is an info page done by the Office of research integrity at HHS. Many of you may have seen it before, but I thought it was really, really helpful. So the one thing about this is it's really, really high stakes, right.
It's really important to get published. We talked about publish or perish. We talked about the academic currency. And there's a lot of reasons why people might make mistakes in general. The anthropologists who and sociologists who have studied human behavior around mistakes in health care, that's part of the ethics literature have come up with two different categories of error.
One kind of error is a technical error. You didn't have enough training. Nobody ever taught you how to do that. That's the top right hand corner. And maybe even you had poor supervision. Nobody was helping you along the way. And I want to start with those two. But then there are these other kinds of errors that are really look at people's characters and behaviors.
Their errors of judgment. And those are a little more complicated. So what about these errors, these technical types of errors, as the sociologist Charles Bass called them. So these areas of error. That's the place where you can actually train people and give people skills. So if you don't know what to do can have core competencies. You can require people at every level of training to learn about the different domains of how you handle data and how you design research and how you manage that data and how you check it and how you analyze it.
And you can also go one step further and teach people about how to mentor and supervise. So that we can pass it on and pay it forward. We can continue to take that work and help others do better work. And then you can practice a lot. You can set expectations for collaboration and independent review and guidance. So that it doesn't become career ending if there's a mistake, but rather an opportunity for learning.
I'll say a little bit more about that when we talk about errors of judgment and then some of the resources, every one of the things our ethics center does is we collaborate with the responsible conduct of research program, which all the postdocs are required to take. We have a scientific citizenship initiative that talks about what can happen in a laboratory environment and how we communicate about science and set our standards. But there are really clear ways that we can build skills, right, which is one important component.
And skills matter. When we come back to this notion of integrity as soundness and completeness, you have to have the research skills. I actually I was a terrible researcher. Like I could make a sequencing gel crack in half from across the room, maybe even from the building next door. So it was good because I learned very quickly that I didn't have the aptitude for that kind of scientific research.
But you do need skills. You need even those basic skills about how not to break the glass. They might not even be glass anymore. I don't know. I'm dating myself. But the other piece of it, right, more than just the soundness and completeness is that skill really matters the most when the stakes are high.
And so this is really, really high stakes, right. If your post-doc work doesn't fit it from the very beginning, right. If your thesis work doesn't finish, you don't get your PhD. If you don't meet the metrics that your PI wants as a postdoc, you don't get to move on and get your own independent lab, right. If you don't publish, none of those things are going to happen, right. So the stakes are really high.
And so I want to give an example that for me always helps me remember this idea about high stakes and the importance of skill. All right. So how many people have gone to a team building or a leadership seminar and done the marshmallow challenge. People know what this is. All right. A couple people.
So this is a really highly technical, like hard core activity. So here's what happens. You get into a group of five, even though there are only four people on the screen, it might even be an AI generated image. I don't know. But OK, so you get five people, five around a table and you get 20 sticks of spaghetti, a yard of tape and a yard of string and there is a scissors.
People have asked me that. So you do have implements to cut the tape and the string. And the goal is you have to build the tallest structure that will support the marshmallow on top, the unadulterated marshmallow. OK, so you can't cut it into little pieces. You have to put the marshmallow on top. And it turns out that this is a little bit harder than it seems it's going to be when you take all the materials and look at them and put them on the table.
And so there's this. This prototyper and software designer Tom wujec, who actually studied the marshmallow challenge because he was really interested in knowing how people developed prototypes, how people work in teams and trying to come up with how to have creative environments and processes that foster people's work. All right. So if you want to watch it, there's a Ted Talk called the marshmallow challenge.
And it's great. Lots and lots of learning. But here's what happened, right. So if you give 10 teams, and he's done this with thousands and thousands of people from Fortune 500 CEOs all the way to recent kindergarten graduates who, by the way, are particularly talented at this. So you.
So if you do that, about 60% of the teams will be able to build something that stands and supports a marshmallow with a height that goes anywhere from, say, about 2020 2 inches all the way down to zero. So that's how the team performs. So he decided, well, I want to see what makes teams do better. So he went into the next series of marshmallow challenges and says it's a little dated now, but he said, I will give the winner whoever builds the tallest marshmallow tower, a $10,000 package of software from my company.
And what do you think happened. That's what happened. Nobody killed the talent, OK. This is a really, really important message, right. Low skill, right. People haven't done it before. Low skill and high stakes leads to bad outcomes. All right. That's why skills are so important when we talk about data integrity and about research integrity.
All right. Now he gave everyone a second chance. He said, well, I'm not screwed, right. There's $10,000 on the table here, guys, so I'm going to give you four months and we're going to work on some team building skills and maybe you'll practice, but you're going to come back in four months. And that $10,000 software package is still up for grabs. So what happens four months later.
Not only right did 90% of the teams build a tower that stood, but the average height of the team of the team. I don't know about that. But the average height of the tower. Went from about 2022 to almost 40. It almost doubled. So here's something else.
High stakes and high skill, right. That's the sweet spot. All right. So keep that in the back of our mind because it might ask us to want to have questions about what kinds of research, training and backgrounds scientists should have or what kinds of questions we might ask on submission, for example, about the circumstances and the environments in which people conduct or the labs in which people conduct their research.
All right, so that's the marshmallow challenge. And this is the sweet spot, right. High stakes and high skill and practice lead to the best outcomes. All right. So that's the skill part of it, right. That's the we don't really know how to do the research. We haven't been properly trained. We're using electronic lab notebooks, but we're sort of muddling our way through.
And maybe we're not inputting data the right way. Those are the skill competencies. But then there are these other professional, personal and judgment competencies, right. So I was under a lot of pressure. If I didn't get I did. I committed research, misconduct, and these are direct quotes from investigations from the Office of research integrity.
I needed to get into a high profile journal. Otherwise I wasn't going to get a job right. Or my immigration status. I was going to have to move right if I didn't have a good outcome. So personal circumstances and also individual psychology, right. I didn't want to be the person who wasn't performing in the lab.
That really mattered. So errors of judgment. So what can we say about errors of judgment. Well, coming back to integrity, right. This is number one. So we have this idea that we as people, if we're committed to doing good work if we're good scientists, if we're good professionals. Our values ought to be incorruptible that if we hold on to them as true, then we're going to act in accordance with our values.
All right. It turns out maybe not so much. So I'm going to give you some examples. These are psychology studies, but I think they illustrate the point. Well, and this is really about illustrating the point and remembering. So you're going to remember the high skill and the high stakes from the marshmallows.
You won't forget that. So let me tell you about a couple of studies. So people it turns out the take home point here is that we're really sensitive to and influenced by our environments. So no matter how much we hold on to what our values are, no matter how cultivated our character is, we're going to be led astray sometimes. And our behavior is going to be influenced by what's going on around us.
OK, so here's a very simple example about this. So remember who's old enough to remember payphone? Booth I almost can't even use this example anymore. Like, my kids are like, mom, like, what are you talking about. You mean those things in London. Those red things, those are just there for show. So So here. This is a phone booth.
Bystander example. So someone's in a phone booth and they set it up about whether you got a coin that came out in the coin return. People don't even use coins in vending machines anymore. So there's a coin there. All right. So if you found and you walk out of the phone booth and there is a bystander there that drops a folder full of papers, it turns out that if you found the coin in the slot, if you felt lucky, if you had a positive experience, almost 90% almost 9 out of 10 people would stop and help the person pick up their folder.
If you didn't feel so lucky and so disposed to being positive, only 4% would stop when they came out of the phone booth in order to help a stranger. Who would think that such a little thing could be so determinative of our altruism. OK, there is another. I mean, shopping malls, it turns out, are kind of dangerous places to go if you want to be if you don't want to be in a psychological study.
So they're in, on the other side of the mall while they're doing the phone booth. There's somebody standing outside in the mall asking for change for $1. We don't have to do that either. How many people remember having to get change to get a nickel for the parking meter. So we asked for change for $1 and they did it in one place. So it was just sort of in the middle of the mall.
And then they did it near a bakery. All right. Who do you think was more likely to stop and give change. The person who is in a pleasant environment that smells good. But the one that really stood out to me were seminary students. Seminarians, people who are devoting themselves to a higher calling and to the good in the world. So they told some seminary students, they divided them up into three groups on their way from one class to another class.
And they told one group, you are in a really big heart. They told the second group, you're in a little bit of a hurry. Please don't be late. And they said nothing to the third group. So as they're on their way, right. A bystander falls down and is groaning in pain and asking for help on the ground. So I don't know what we want to say about this, but 63% of people who weren't in a hurry stopped to help.
That went down to 10% Just being in a hurry in a big rush. Makes us less compassionate. OK that's really important, especially if you work in a hospital like I do that's really busy all the time. Like every morning, right. We are on we are on overcapacity alert. It's going to be a long wait. Please send people other places.
But the important point of this is not that this is determinative of whether we're good or bad because of action in one particular moment, but just that our environment really does influence us. So when we think about these questions of how do we promote behavior that comports with our notions of research integrity, right. We also have to think about the kinds of environments that we foster, right.
We can foster environments that bring out our better angels, right. Or our worst demons. And I think that that's something that certainly matters when we're talking about errors of judgment. The kinds of things that say, when I have a choice, I'm actually going to go away from what my commitment is because I'm being pressured by something else.
So we. So learning environments and productive environments are really important. All right. So what are some of the features for success in environments. So the environment could be being in a place with beautiful light, feeling good about being in a building, right. That's not contributing to global warming.
Being in a place that smells good, right. Putting your office over a bakery, well, that might have some other problems, but. OK, so. But the idea right back to the marshmallow, some of the things that mattered that Tom Talmudic talks about is what happens when you're quick and you're thrown into something versus having relationships that allow for learning and for trust.
The well, this is a spoiler alert, but I assume people have things to do other than watching the Ted Talk tonight. But the Fortune 500 CEOs didn't do so well. Recent business school graduates did the worst on the marshmallow challenge. As Tom says in the video, they were all trying to be the CEO of spaghetti Inc. But the people who did the best.
Were the recent kindergarten graduates because they were willing to prototype and try things out and they really weren't worried about what failed or didn't. And it was that kind of collaboration as opposed to keeping positions of power that really mattered. So environments that are collaborative, environments that are open to trying out new ideas. Not needing to go about evaluating or looking at the measures or the worth of the science, because that's the way we always did it.
But keeping an open mind to what is high impact research. And then the other thing that's really interesting is that the CEOs didn't do so well. But when you put their executive administrators with them, they did really, really great. What does that matter. Because oftentimes, especially amongst those of us who are big thinkers, we don't have the right kind of structure in place to really get the work done.
We don't have that. The skill set around the executive elements that really matter, like the executive in terms of the planning and the doing. And so thinking about labs and environments that really structure provide a structure for the work can also be key features to success. All right. You've done some things right to create an environment that's a learning environment.
So your code of conduct talks about the values that matter. And the way you're going to run this meeting and your work in order to create an environment. We similarly at Harvard Medical School have a community learning commitment to make sure that we're doing our very best to not create groupthink, but to welcome in different and dissenting ideas to make our work better. Including the kinds of things that came out in the marshmallow challenge.
Like we all make mistakes, right. So making mistakes is not the end of the world. We have to figure out how to correct them. Those would be technical mistakes, not judgment mistakes, how we listen, how we speak, and also how we listen. Being respectful of other people, bringing our empathy, being open minded. And just because recognizing that just because we believe something doesn't mean that it's true.
Evidence and opinion are different. All right. And so what are some of the other things we can do. So I wanted to show you some of the kinds of structures and programs that we can put into place to help our work along to promote research integrity. So this is the Harvard Medical School r-3 project. And its mission is to support a culture that advances research, rigor, reproducibility and responsibility.
And the thing that I want to really point out about this, many, many institutions have these kinds of programs, right. But the thing that I wanted to point out that is really important is a lot of this is technical stuff. The kinds of research design, data management analysis and interpretation standards and education about scholarly dissemination. But then there's also the light blue on the left side.
The light blue part of the pie is about scientific culture and community. So that you can't do one without the other. And the kinds of things that fall in scientific culture and community are leadership, mentorship, responsibilities, and ethics policies and standards and academic environments. So when you think about academic environments and advancement, right, if you have variable reinforcement and you don't know what the standards really are, right.
It can create an environment that's unpredictable and stressful and high stakes, right. Without the knowledge about how to get there. So both practice and environment are going to be really important for our academic integrity. OK a couple of other things, right. So there's this whole thing about negative studies or things that don't work out. We call them failures.
Probably not failures. Actually we would let ourselves learn from our failures. We could be great. So the psychologist Dan Quayle wrote this wonderful book called The Talent Code. But what he wanted to figure out in this book, it had nothing to do with science, really. But what he wanted to figure out in this book was how some of these athletes became such great elite athletes.
And what he did is he said, well, they must have these incredible training facilities in Latin America where many of them came from with these really sophisticated technology. And what are they doing. It turns out that there were chain link fences, maybe some pavement And some soccer balls and some really good coaches and what they looked for in the athletes. What he saw was that the athletes who became elite athletes as opposed to excellent athletes were those who practiced the things they weren't good at.
They were able to discern where their shortcomings and limitations were. And practice and practice and practice until they got it right. So being able, rather than to see things as failure, as opportunities for learning and change is really, really key at being excellent. We also know that talent is work. So we think about when you think about Malcolm Gladwell and outliers.
10,000 hours, you can be really, really musical if you want to be a concert musician. 10,000 hours. That's a lot of time, right. But this doesn't come quickly. So practice is what can lead to mastery. And if we take this all together, right, a great scientist, one of the great scientists of all time, Louis Pasteur.
Famously said chance favors the prepared mind. So making ourselves prepared to welcome what comes, even if it initially seems like failure. So there is quite a lot of value actually in negative studies, especially large clinical trials. In my field, we haven't had a really revolutionary medication advancement for a major psychiatric illness since I was in medical school. That was a really long time ago.
I'm not going to tell you how long. But But it was some time when there were where you needed nickels for parking meters. And there were payphones. There weren't any cell phones. So it's a while. You'll have to take my word for it. So I think here in this space, as we think about what would change the environment and what would change our ability to do good research, that there really are opportunities for publishing to lead on this.
So are there common features in failures of integrity. Both soundness and soundness and completeness, right. So when we look at all these retractions, right, since 2012, what else can we learn from other articles. Are there patterns you could even use for this. Are there patterns. Are there things that we might be able to see in submissions, right. That might lead us or give us a clue, right that there's something we ought to be looking for, right.
How are we using or how effectively are we using independent reviews, looking at data analysis and analytics and the methods. And then there may I think there are also probably really good opportunities to promote integrity in terms of the moral commitment as well. So if we think about failure in a different kind of way, we think about negative studies and we think about results that are unexpected.
Is there a way to embrace that as part of our learning as diminishing research waste. And that because if we don't know about negative trials or negative studies, people reproduce them instead of studying other things, right. So are we missing opportunities for learning and is there a way to shift the environment in the paradigm. I'm just thinking out loud, that's your expertise, not mine. But I hope we'll have time to talk about that a little bit at the end.
All right. So what about so integrity is a big piece of that. But there were four things right, in your values. And so we talked about research in terms of soundness and completeness. We talked about concepts of competence. Obviously, transparency and honesty come into the judgment part of integrity and also how much our environment matters.
But we're also in this really rapidly changing ecosystem, right. So I think that some of the questions we want to ask I gave a couple very feeble examples because I'm clearly not an expert in your area, but about how publishers and journals can incentivize or encourage research integrity and prevent deviations. But then there's this other piece of it, too, which is not just about the conduct of research or the research questions that we're asking.
But about promoting the good or our desired outcomes. So when you look back on the value statement, it's really about things like inclusivity, right. How do we advance and think about equity and diversity in our research. So health inequities are not a small matter. There are estimates that these health inequities account for $320 billion of spending every year in just the United States.
So if we're not conducting research that promotes equity, right, we're spending a lot of money on things that we could be using to promote good in other ways. Racialized minorities continue to have poorer outcomes for chronic conditions than others in our population. We know about the studies about zip codes being major predictors of life expectancy, and at least now we're talking about social determinants of health.
But there's a lot more in here also. And it's not just racialized minorities in my field. In psychiatry, we know that persons with major mental illness have a 25 year shorter life expectancy than the general population, and that's for the same zip code. So we know that there are some people with some illnesses, right. Who do who do do not benefit as much from our science and our treatments.
All right. So I think there are a lot of challenges in this. I think there's some really low hanging fruit in terms of some possibilities as well. And so some of the measures that have been recommended time and time again by those who have looked at problems of health disparities and made recommendations about what we could do to reduce disparities, some have been around standardized race measurements across health systems.
I call this the table 1 problem. So I don't know. I have a target on my forehead or something. So, I'm like a 23-year-old medical student and I'm sitting in my primary care clinic and this older man comes in and he says, well, where's my doctor. And I said your doctor will be here soon. I'm helping out today. And he said, well, I'll just wait for him.
And I said, no, no, no, you can talk to me. I really know what I'm doing. This is like day one. Anyway, so he says he brings in this copy of Time magazine, right. And he says, I want this pill. It was Viagra. He wanted to know if it would work for him. So I said, well, I don't really but let's see what the doctor has to say.
At least I told the truth. So my attending physician said this is so great because you have to do a whole talk during your rotation in medicine. And so you should really ask that question, is this medication going to help him. And so my table One example is when I went there, there was nobody included in that study. Who had chronic hypertension, who had vasculopathy, who we could identify their race or their and there was nobody included who was in the age group of my patient.
So when we want to think about it, and yet that was again, in the Stone Age. But we still have that problem. We are not doing a great job of doing standardized race and ethnicity measurements across health systems and across our research. So that's one very obvious recommendation, but also effective interventions, right. We often don't study interventions, to see if they work.
We come up with them. We say this is what the data is. Are there differences across populations. Are we asking the right questions and do we have a diverse enough workforce to be incentivizing the research that matters to those who are not in groups that have dominated the questioning and the research for a very long period of time, including women, by the way.
And then what about technology. How can it help us. Are there ways to leverage technology to do better at inequity and are there opportunities if we actually do a good enough job of looking at differences between different groups of people to embrace personalized medicine, to enhance health outcomes and address disparities. So I think that there are some things that journals and publishers can do in terms of this.
We talked about the table 1 problem, but I think if you can't publish a paper, if you don't have if you don't have identification of race, gender, ethnicity, people are going to start doing it. It's a really, really big incentive. And then setting metrics, but also measuring them and publishing them, being transparent about what's happening and what's not happening. I'm a big fan of report cards on websites.
Like, here's where we're doing well, here's where we're not doing well because it nothing like having to air one's dirty laundry to help one use the washer a little bit more. So to speak. So I think it really, really helpful thinking about effective interventions. One of the things is that health services research often gets underfunded or looking at standard therapies and how effective they are.
It's kind of hard to make that look really, really exciting. Year two of thunder or two, right or two a journal. So I think publication around implementation and impact is something really big that comes up. I'm in a Department of Global Health and Social Medicine. The actual impact on the world of the implementation science many of my colleagues are doing is much greater than some of the science that gets published. But it doesn't get incentivized because it doesn't get published the same way and it doesn't get funded the same way.
And so there's not a lot of academic right. We do it because it's a labor of love, but not because it's necessarily going to be promotable. And then really thinking about workforce diversity in terms of editorial boards, but also self-identification from authors and then real metrics from submission to publication to identify opportunities for interventions to advance equity. All right.
I want to also talk about the fourth recommendation technology. This also brings up your core value of adaptability. I couldn't possibly do a talk about anything in the world right now without talking about AI. So as we come to the end of my slides, I want to say a little bit about AI and technology. So experts, which I'm not one experts who are really looking at the uptake of AI and the cycles of hype and applicability tell us that.
That generative AI tools and AI algorithms will be routinely functioning in clinical practice within the next decade and probably within the next five years. And all of these tools are based in large data sets. So we need to be doing things to encourage broad, Representative and diverse data sets. There is a huge possibility for great benefit. I'll talk about that a little bit.
But I was just at a conference about ethical leadership, and we're all sitting in a group, and the problem that was given to us was this example of whether a practice should use a dermatology. A dermatology practice should use an AI algorithm, which did a much better job of identifying skin lesions in light skinned individuals as opposed to those with darker skin tones.
And so we're all sitting there saying, this is not ready for prime time. Why can't the dermatology lists just look at it. Why are we trying to stratify? And there was a radiologist in the group who said what, I think AI saves lives in my practice. It's not that I don't go read the films myself, but I have a computer that can look at these images as they come out and tell me which ones are likely to be high risk.
So I can look at them first so that if there's somebody who needs an intervention, it can actually help me use my time better. So they're already being used and there's this possibility for great disruption. But we have to make sure that we're adequately powering them and know that the algorithms are only as good as data input. The problem with this is that it's really costly to do data science projects, right.
So data science is expensive and time consuming 7 out of 10 projects fail. The average cost is about $200,000. And it's also hard to know what the definition of success is right. So the companies who are doing this, the definition of success is, can I make more money off of this. But the definition of success for us might be something different.
How likely is this to enhance health equity or access. Or treatment of disease. So I think we need to really ask the questions about what we expect of AI and what we expect of our technology. And I think that and also right, this is a great opportunity for impact. But in order for these algorithms to work, in order for generative AI to be able to help us with our problem solving, we need to have high quality clinical data.
This is where mental health again is in trouble, right. There is. OK well, I have to say it now because I started, but I'll say really quickly because we'll end on time. But there was this study done of psychiatrists and patients asking the psychiatrist, how much time do you spend talking in the session. And then asking the patient. So the psychiatrist said no, no, I listen the whole entire time.
And the patients were like, I can't get them to shut up. They're all constantly talking. The point of that is that when you look at mental health, electronic medical records, we are not rule followers. We don't like radio buttons. There's no Yes or no. There's a lot of narrative there. It's really hard to import that from a clinical record into a data set.
So again, we're going to have to think about ways both at the level of practice. And when we look at the power of the research that gets generated and the algorithms that get set up, how are we driving this data and what can we do to improve the quality of the data. So we can close that 25 year gap in life expectancy for persons with major mental illness. We have got to incentivize self-identification, right.
And keeping data about race and ethnicity. I'm thinking about I think journals if we ask about universal domains for data collection. So we just don't accept and publish papers that don't have adequate data, data inputs to address questions of equity and to really be broadly applicable. If we don't publish that from the beginning and say what we expect, perhaps we can drive that.
And then also finally really partnership with communities around this. So that no one's the science isn't going to do very much. If we don't establish ourselves as those who both do the research and promulgate it through publication. If we don't establish ourselves as worthy of that trust and we don't demonstrate benefit for persons who participate in that research.
All right. A couple other things to think about. You guys like we need your help on this copyright and basic integrity of generative AI. So there's this question. Some of it's true, some of it's not. So we've tested it out. We're figuring out how to use it all in academia, citations that don't exist.
I've seen my own words cited, other people and two people articles that actually have never been published. How do we protect authorship and work product. So it can't just be taken away or mass distributed misattribution accuracy, new kind of computer plagiarism. What When I was in medical school, we weren't allowed to use well, it wasn't called PubMed then.
It was a very archaic version of Medline, but we weren't allowed to use it because we were told, well, how are you ever going to figure something out if you don't know how to go into a library stack and find the journal and make a copy and read it. Yeah, well, fast forward during COVID, they turned our main journal reading room in the Countway Library of Medicine into a cafe, and people actually go there now.
So I just wanted to make sure if everyone was awake. But it's actually, it's true. OK So, we can't just be naysayers. We have to adopt things that are going to help us. But how do we come up with transparency and disclosure around it. How do we come up with standards for what's actually taken versus AI generated. How do we detect and evaluate it.
And how do we define parameters of use. What are the industry standards going to be for permissibility? And in all of this, not forgetting, even though it's really scary, like, I have kids in high school, so I've just gone through the second round of the dystopian 10th grade novels and boy, Ray Bradberry is really onto something with Fahrenheit 451 just saying. So I'm scared of it, too, right.
Like I'm prepared to say that this could be the end of us. But I think we can't forget about the opportunities for benefit either. All right. So as we're rounding out, I hope that this little these musings at the end of your day. Thank you for staying awake. I really appreciate it. But that value is really can drive value, meaning good and benefit from research.
But not alone. We can stand by the things that are important to us and the things that we think make our work legitimate. We can have personal commitment, but we also need to have high skill and supportive and enabling environments. Integrity considerations really do matter in both skill, right and character and commitments.
But we also have to think about the outcomes and goals that we think are important things like equity. And things like advancing health and other values. And really just to say that there's so much opportunity for leadership in this rapidly evolving ecosystem. And those of us who are on the other side sending you endless submissions and tweaking them and changing them, we need your guidance and your leadership in helping us to navigate this rapidly evolving ecosystem.
Thank you so much for inviting me to be with you, and I'd be happy to answer any questions. Just OK, since there's no soapbox here, I will not go on a whole thing about AI and dark skin and my articles. Really, really big problem. What I did take away, though, is as a publisher, we find that.
We don't influence the research funders. The research funders influence us. How do we influence the research funders? Yeah well, I ask myself the same question every day. No, but. But I. I I think it's important. To rip the Band-Aid off what the implicit biases are in this and what gets funded and what doesn't get funded.
So I think there are ways of looking at the number of submissions and relatively and thinking about what areas are overpopulated within the literature. Like how many me too studies do we have, for example, of antidepressants right at a slightly higher dose with a rating scale that nobody actually uses clinically and isn't sure is clinically relevant. So I'm being a little bit sarcastic, but only just a very tiny little bit.
But if we said something about the number of papers that get submitted in certain areas and the number of papers that don't get submitted in other really critical areas like health inequities and disparities that are costing us $320 billion a year. That's a lot bigger than any grant that any state is getting under the Affordable Care Act for Medicaid expansion. That's a lot of money.
So I think that we have to actually do use the data that we do have to bring numbers and figure out how to characterize it to influence those who hold the purse strings. I think there's a lot of power right from where I stand. There's a lot there's a lot of power in what gets published and what doesn't get. The other thing is, things aren't getting if things aren't getting published, then people aren't getting promoted and institutions then don't value that work.
And so it's really important that we bring those values first and saying what research is going to be valued in lots of different ways. I'm totally in favor of science. I think we should be doing lots of good science and if we had endless funds, it would be great to fund all of it. But we really have to ask some hard questions.
Who's being included, but also who's not being included and who's not benefiting. But I'll get on your soapbox if any day. Any other questions. I'm only taking questions from people who laughed at my jokes. So I know you're saying they were good. So I'm intrigued as to whether you had success at Harvard Medical School in changing the way things were done.
Because one of the constants across.
Come up with more, maybe like a year from now, but it's only been a couple of months. But actually, instead of there's also there's often this feeling like there's this great cartoon that says, run this by legal, but don't let ethics see you on the way. The idea that the ethics has always come in and they tell you that you're bad and you're doing the wrong thing and you have to do it differently.
Or we make this mistake of generating our own questions because they're philosophically intriguing, but they're totally out in left field from what the scientists would be doing. They don't even know that there's. So one of the things that we're trying to do is embed ourselves at the ground level and understand how scientists are making decisions about what to study and about how they're going to conduct research and using that right to actually generate the kinds of questions and considerations that we're thinking about.
So that we're there right along with them, not just when we run into trouble. And that's really hard because we have a bad reputation of telling people what to do. And then ethically, as many ethicists get into trouble as researchers. It's just because we know what the right thing to do is doesn't we always do it. So I think changing that narrative is really important from the ethics perspective.
But we have a lot of work to do. We have a lot of work to do. And how we keep doing that is we just keep plugging along one foot after another and start by asking the questions. Great question. The last question. This was a wonderful talk and Yeah, maybe a little bit apprehensive.
This morning we heard a panel and one of the speakers on the panel was Susanna Ramirez from the University of California Merced. And she teed you up kind of perfectly for us at the end of the day, because her talk was about how the National Institute of Health, due to the way it's organized, do the way the funding works, contributes to the ongoing health inequities.
So we were all sitting here like we're just sort of primed for this. But she had a chart about how the way the NIH was organized. And if you weren't and how you can't remember if somebody took better notes than I did. Like if you wanted to study something generally about say, nutrition, but there's not a division on nutrition. You have to find a disease that could be connected with that.
And it was just like it made just whack a doodle, right. There's just bananas. And so I wondered if there's any conversations about changing the structure of the NIH was probably like worse than Harvard, even though it hasn't been around longer. But to try to get at the root of that. And I just love to get your perspective. Yeah mean.
Well, look, if we're not doing the research that we need to do. and it's getting in the way of funding things that we know we ought to be doing in accordance with our values, then we need to figure out how to change it. I don't know enough about the structure or about the funding of research. I've never I've never even been part of an NIH grant. But but this story.
And is not an uncommon one. Like I stopped looking for funding through the federal government because what I was doing didn't fit in right to the way things were organized. Now, there's a lot of problems with that. Because if you're in an institution and even if you have donors, right, but if you're getting grants from foundations and private funding sources, they probably don't have the same indirect and overhead, right that the NIH grants do.
So the. So then you're not as valuable, right. Or you can't hire as many people on your team or you can't mentor more young people to come along and continue the work. Like that's the greatest thing of being in academia is all the students you meet every single day who are well, who are smarter than me at least, right. And then I say, OK, I can sleep tonight because they're going to carry this forward and continue to change the world so that it just it has so many downstream effects that we got to figure out how to get it right and we've got to invest in it.
So how that happens on a pan institutional level, I don't have a great answer. But the first thing is that people have to be aware of it. And so if we see that we're not doing good research in particular areas that matter, right, even if we're looking at very narrow questions like which chronic illnesses are costing cost Medicare the most, right. It's easy. Like there's lots of Medicare data.
So you can crunch it pretty I don't want to say easily because I couldn't do it and I certainly couldn't do it easily. But it's accessible, right. So there are ways of getting at some of this data to make the point. But it's a great Thank you for sharing that from this morning. I'm sorry you couldn't be here for that. Yeah so I'm looking forward to it. Thank you.
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The cadmore media player is a professional video and audio player experience designed to deliver media as well as relevant transcript, segmentation, and metadata. On the screen is a traditional html video player with buttons such as play, pause, forward, captioning, language and play speed selection and more. Adjacent to the video player are generally tabs, one of which shows a scrolling transcript of the video and the other shows any segmentation of the media, sometimes referred to as chapters. At the top of the video is a menu bar containing an Explore button and Tools button. The Explore button allows you to dig deeper into the video by opening up an explore dialog which contains tabs for a visual navigation of the video using thumbnails, an about tab which contains rich metadata about the media, a segments tab which contains further information about the video, and a related tab which contains links to other media related to this media. The tools button opens a menu with options for sharing the media on social media, creating bookmarking links, creating embed codes for your website, generating citations, and more.
Keyboard Shortcuts
Keyboard shortcuts only work while you are in forms mode. By entering forms mode, you will be able to quickly access portions of the media player with single key commands.
Spacebar: Play/Pause Toggle
I: Info Menu,
O: Tools Menu
T: Transcript Tab,
S: Segments Tab
F: Forward 15,
R: Rewind 15
L: Languages,
P: Playspeed
M: Mute Toggle,
V: Volume Bar
N: Video Only Toggle,
B: Full Screen Toggle
C: Captions Toggle
Escape: Closes dialogs and menus / Exits Player
Z: Next Transcript Chapter
X: Previous Transcript Chapter
Q: Next Transcript Paragraph
W: Previous Transcript Paragraph
Entire Media
Segment(s)
Custom Clip
Start At:
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Embed Size
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You Size - Responsive
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We Size - Responsive
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Picture Overlay Popup
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400 x 225
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512 x 288
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400 x 700
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768 x 1024
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1024 x 512
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1280 x 608
Form Title Cite
No citation provided.
Info
Segments
Related
Thumbnails
Exit Clip Mode
You have requested to see a portion of the video outside of the clip.
Click OK to switch to the full video or click if you would like to stay inside the clip. You can always go back to the clip with Tools and Clip Mode