I just want to say that Mark said it so perfectly. before, that this has just been so great. And, you know, Erin, we talked, what was it, 11 months ago, about the possibility of something like this, and imagined what it would be like. And I just want to thank you, and I want to thank the forum leadership and the forum membership for being here. It's so much better. It's, like, beautiful. It's great. It's just, you know, really, you drop it. You drop it. I mean that. No, I mean that in the best way. You drop the pebble in the water. so important and so I just want to thank you. So, I think that all the talks that I heard here were very impactful, some of the most impactful talks that I've heard. And we're here to listen, but we're also here to act. And so, I thank you and we thank you for AOSSM for trusting us to come here to the forum, not to be sucked up by the giant gorilla, but hopefully by having a stage and educating us and seeing what it's like to have communication and issues that we don't address that are so critical. So, we're here to listen, and we're here to listen to you, and we're here to act, and we're here to make a difference. With your help. Thank you. Thank you. Appreciate it. I'd like my panel to come up to the podium. I don't know if anyone knows the answer to this or if it's even possible to find it, but I'm thinking as I'm sitting here with several other double minorities or double underrepresented being both Latina and a woman, when you look at those statistics and it was 2 point something percent Hispanic, are those separated? Like how many of the underrepresented are also women? And I think that's an interesting statistic that I'd like to see. I can't believe, yeah, because it depends on the study. Yeah, I don't think. And they don't always say, and that's kind of the problem. They'll group women, they'll group men and women in a subcategory, usually Latino, Latina together, and they don't often pull them out. So, even when you look at orthosurgeons compared to neurosurgeons to cardiac, it really depends on how they do their data, because the surveys are not so hot. You know, low result. So we need to start better doing it with that already. Yeah, so the website, it asks you to self-identify, then they can pull. And then, as we've heard lately, it's a problem if you're Asian, Southwest Asian, East East Asian, you were going to say something. I think the AMC actually has some of the data. It's more granular, but this is not going to be at the professional level. It's going to be at the medical school to residency level. Yeah, and that's something Kim Templeton pointed out, orthopedic surgeon who's been working hard with the AMA to try to get this information, because if we don't know what the baseline is, then, you know, we're only guessing. I tried to get some information from Anna a couple of years ago on what percentage we had of women, and they could not tell me because they didn't ask. And so when you filled out your application, it was just like name, address, et cetera. And actually, in a way, it seemed kind of good because it was gender blind, but I think it was actually because everybody in the organization was mostly men, so it wasn't an issue. But it was hard to get that data. So I think, I hate to ask this because I'm getting to the point now where I decline to state on everything, gender, race, everything, but I guess maybe we are going to get the data, we can't decline to state all the time. »» And that was part of our charges as a task force. We are going back over the next several years, you will be getting surveys for people who are already members. But incoming members will be asked these questions as they come in, and they always have the option not to answer. But part of the thing is just figuring out what are the categories. And so we've been looking through different organizations to try to figure out how to define categories and how do we separate this out. It's mostly just to get the data and try to monitor improvement. And if you aren't making improvement, you're not doing the right things.

gratitude

underrepresentation

available data

specific data

diversity improvement