Transcript for May 2015 Brown Bag

>> So, kind of bring both of those interests together so we're able to kind of focus on higher ed and traumatic brain injury. That's something, you know, I enjoyed quite a bit. And then this brown bag session is actually based on a presentation that we did, a poster presentation at [inaudible] psychology conference earlier this year which actually took place in San Diego, which is really nice. We didn't have to travel or anything like that. And then we're actually working on a manuscript in publication based on kind of what we're showing here. And so, what will happen, as we go along, that, you know, if you have questions or ideas about what that would be interesting or maybe that would be interesting. You know, we'll kind of keep track of those ideas and then they give us something to kind of think about heading forward.


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>> Yeah. And just to build on that, you know, Mark mentioned some of the research, what we've done in the past. One of the studies we worked on was building a survey of leaders to different brain injury associations state chapters. So, the idea was that they would really be in a good position to know, you know, what standards are safe or what's available in the states in terms of supporting people with traumatic brain injury. And one of the areas we got into was, you know, some problems or lack in services in the area of vocational rehabilitation. So, Mark and I were looking at, you know, could we look at the RSA 911 data, maybe as a way to get some answers in terms of some of the things the BIA directors, you know, were talking about. [Inaudible] is a unique opportunity to really kind of mine the data. And we're going to be talking about the fact that this is an incredibly large publicly available data set. It has limitations. It also has a lot of positive elements to it. And so, the area of looking at these particular issues of looking at vocational rehab for people with traumatic brain injury when, you know, they have the benefit of a college education. You know, this data provided some unique opportunities for that. But one of the things I think we're trying to advance in the area of voc rehab for people with traumatic brain injury is to try to have more of, like, an evidencebased, you know, type of way of supporting people. Not the term that we, you know, used often in the field. And the idea is that, you know, states just, kind of working off of [inaudible] of what you think might work best. To really look at, you know, the data indicates that there are some best practices and approaches to that. But for the 911 data, it is not, like, the complete answer to that but it's a step to the right direction. So, that's part of where we're trying to get to, I think. And I think, like, when we write this article, that's one of the angles, you know, we're going to try to really get at. And that, and again, just a lack of really understanding for this particular disability. And perhaps, you know, this data can give us some good answers.


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>> So, kind of just to give you some organizing context at the start, really sort of our goals that we set out to do were to identify specific VR services that were associated with successful  and we'll see in some cases  maybe not so successful outcomes for individuals who go through VR and are a part of VR. Get college university training along the way. And then, furthermore, we kind of zeroed in on a particular group that was, those folks who  and we'll kind of get into the details more down the road, but who  as part of that college, like the highest level of education they completed changed between the time they came into the VR program and when they left the program. Let me [inaudible]. Oh, [inaudible] you have a question.

>> Do you want questions as you go? Or save it till the end?

>> As we go is fine.

>> Okay.

>> Yeah, I think as we go is good.

>> Okay.

>> Okay. So, just to  Chuck, you can chime in on this as well.

>> Yeah, yeah, yeah. So, the ideal is that we're assuming that if people with traumatic brain injury have access to higher education that the very likelihood of success in terms of pursuing their employment goals. You know, part of the idea that I was looking at, you know, if you look at national data, in the general population of, you know, if you have a bachelor degrees or higher it's shown like hundreds and thousands of dollars difference over the course of a person's lifetime. In terms of the kind of economic return that you get from that. And then there have been a number of studies that have talked about, in VR specifically, for people that have access to some form of college. You know, it does give people new opportunities because they can train in different areas or qualify to work in different areas. So, making this, kind of, core assumption. The other part I was thinking about this is when I look at this post secondary participation in general, and this really kind of ties into a lot of market research. And we're thinking about people with cognitive disabilities. The idea of looking at post secondary participation is really more feasible. Or there's programs in place to support that. Whereas in the past, we may have not really thought that that was possible. So, I think on of the good example of this and, you know, Nick is in the room and he has experience with CTC, it's one example of this. You know, we're looking at post secondary options for people with intellectual disabilities and more severe forms of options, more in the past, we may not have thought that was possible. So, you know, kind of in the same [inaudible] with people with brain injury, we're looking at, you know, if people can participate in college, we would assume that that would have a positive benefit. Again, that's kind of one of our core assumptions going into this study.


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>> So, Chuck, you can talk about this next slide if you want, but the room is full of mostly rehab insiders. So, I think the quick version would probably be good. We sort of prepared two levels of detail depending upon who was in the room.

>> Yeah. So, do you guys all are familiar with the U.S. Department of Education, Rehab Services Administration, the State VR system. We'll talk about this a little bit in the next slide as we go further. But in looking at this data, this was a unique opportunity to look at a national data set for providing vocational rehab services to people with traumatic brain injury. And, you know, there's really no other comparable data that is, you know, that we're familiar with or national programs that support people with traumatic brain injury. And I think the only possible comparison might be the VR need program and the VA for veterans with traumatic brain injury. But, if we look at this data aside, maybe we could look at it from a national point of view. And Mark, let's go to the next slide. And I want to hit on, you know, looking at people with traumatic brain injury. One of the things that's really characteristic about this population is the mark of communitybased longterm support. So, for people with this injury, when they first get injured, we have the system of acute hospitalbased inpatient care. It's great. It's probably the best in the world, you know? So, when we get people out of this setting and into the community there's a big drop off. So, we do encourage them to populations, like, people with intellectual disabilities. You know, in my classes and, you know, and Nick probably knows this example already. If we look at people with [inaudible] class. A lot of people with intellectual disabilities, for example, they have this huge system of various kinds of communitybased support. So, for people with brain injury, in comparison, we don't really have that. To back to this idea, of like a national kind of perspective, to State VR data and really, really the only kind of option we have to look at this. And if we look at the population of people with TBI, a very commonly incurred disability. The Centers for Disease Control estimates there are approximately this 1 .7 million TBI. That's probably an underestimation of what the true number is. But that's the number we can pretty confidently say with [inaudible]. It's a disability that has a number of possible psychosocial, physical, personal changes. That, if we think about work, really presents a lot of barriers of getting into employment. And, I think, one of the key challenges of this population is you have a pre and a post disability life. You have a pre and a post work identity. And so, trying to, then, get into a new type of career if the person is not able to go back to what they used to do, can be a very strong challenge for them. You know, really, kind of turns into a realization that maybe I can't do what you used to do and have to retrain and have to rethink about new directions for my life. We know the data says that there's a lot of unemployment. And there's a range of different studies that we'll talk about. You know, rates up until 80% unemployment. [Inaudible] we can confidently say that unemployment and underemployment is a big issue with this population, a big challenge. We know the data says that most of people with brain injury do not go back to what they used to do in terms of the hours worked per week, the wages, the kind of work they did. And then when we think about this lack of employment participation, it not only sets the person, in terms of their financial wellbeing, but also affects the family. And there's a number of those psychosocial consequences where if you lose your sense of work identity, there can be heightened depression. Kind of a sense of loss of purpose. And just really struggling to know what you're going to do, you know, with this major disability. I think part of the challenge in this population, too, is that you often will look the same. And your family and coworkers and other people in your life may look at you and not really understand why is it that you don't remember what they used to say, or why is it you have trouble concentrating. Or if you have [inaudible] some if these things challenges can present employment challenges. You know, it really adds to the whole mix of why this is a difficult population to get back into employment.


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>> So, this next slide here kind of covers a previous research that bears some kind of resemblance to the research that we're trying to kind of pull off now. And one thing to kind of keep in mind is that most of the other research, there's basically been nothing that's really isolated this group of folks with TBI that we know of, that have adopted this kind of steady format. That the other findings that come from, kind of, related research generally are finding that applied all disability populations that would give certified VR. Or they may look at specific other disability groups that are not traumatic brain injury. But some of the common things that you see, the outcomes that get measured are typically employment and earnings. If you think about what the V [inaudible] agencies are normally on the hook for. Number one, how many people, you know, did you get to go to work? And then secondary, comes from questions like, how much do they make? And things like that. So, those are kind the primary outcomes they get accepted. And we did take a look at those as well. We'll kind of see some results on that later on. And in terms of like looking at rehab services, you know, with state VR agencies across the nation and even [inaudible] data with people who are in, you know, Guam and American [inaudible] and places outside the [inaudible] as well. The predictor that they can provide certain services. And they basically categorize them into 22 different services. And colleges [inaudible]. So, we're not  that's sort of a given figure for everybody in this particular study. So, we started out with the other 21 and plenty of other studies have done that as well. And things that have kind of surfaced in previous literature is being predictive of favorable rehab outcomes at things like, do they get job placement services. And that one tend to be one of the strong predictors that shows up in their research. The person's level of education on their way in, which probably speaks somewhat to personal characteristics. Folks who have a desire to study the rehab services. The total amount of money they can spend on a case. The way that we have it here, you can't figure out, like, how much money you spent for college and how much we spent for job placement. And things like that. You just get one total lump sum money. So, you can't really tell where they spend it, but you can get some idea of how much financial resources they invested in a particular case. And then there's been some previous studies of college as well in terms of predicting outcomes. And the few studies there are are kind of mixed. You see some studies that say, okay, college is associated with  people who get college is being part of a being a plan generally have better outcomes. And you also see, kind of findings coached in negatively where they'll say, okay, other people who got into college. Still, when their cases were closed less than half of them were employed. So, you see, kind of, both of those things. So, we'll kind of address that question a little bit later on for this particular population.


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>> Yeah, Nick, normal area of context market we can mention is that, Mark and I have done a couple of different statewide needs assessment with Nevada. And so, basically, these are assessments that are done as part of the 3year plan that all state agencies have to come up with in terms of looking at disability services with New York State. And so, like Mark mentioned, it's kind of common areas of VR services. And we looked at that in terms of successful outcomes and, you know, earnings and so on with Nevada. And if they consisted in which level to kind of research Mark talks about, you know, we also saw things like. I'd say, if you kind of expect to see things like on a job to [inaudible] job. Job placement. Those kinds of very extensive kinds of jobs. The poor types of services will often, you know, there seem to be successful outcomes. And one of the [inaudible] for Mark and I to think about, the use of this 911 data. Again, it's, you know, it's incredibly rich, you know, source of data. It's kind of just prime to be able to mine it. And so, the needs assessment maybe kind of planted the seed for us to look at this.

[ Background Noise ]

>> All right. So, rehabilitation plans in college. There are some things that we wanted to look at in terms of, you know, rehab plans [inaudible] in college. And some of the things we wanted to, you know, first consider in terms of looking at the use of college training is that this is a pretty expensive VR service. And Mark and I were talking yesterday about some of these, like, outliers that we've seen in some of the 911 data. I think, Mark, you talked about this one case that was like 12 years old? Twelve years in duration or longer than that? Do you know how many years?

>> Yeah. I mean, we've seen cases in the past where you just go look for extreme cases. You can find cases that stretch over, like, 25 years. I mean, they're rare.

>> Yeah.

>> But, kind of interesting when you see one.


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>> [Inaudible] suggested every college plan for 20 years, but I think generally, it's going to be longer in duration. It's more expensive, obviously, because it's not only the time the case is open, but it's a case where, you know, college education is not cheap. Especially, if the person is in a major in which the IP will say that the person has to go through some kind of private university or college. So, we see [inaudible] use expenses which it definitely is. But if you go to, let's say, the USD, and you can make an argument that you had a college plan where that was the only place in San Diego that offered that. You know, the costs are, obviously, going to go way up. And it involves a lot of client time and effort. And in some cases, the clients often share in costs if they're financially able to do so.


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>> Yes, so, it's a good thorough investment. Not only on the agency's side, the counselor, the time the counselor puts in. But also with the client has to do as well. They're a demanding plan. Which is another reason why we keep a lookout. So, the primary research questions that we kind of /PRAOUFD. I'll just kind of read these. Which combination of VR service variables best predict competitive employment outcomes of VR participants with TBI who advanced their post secondary education? And then within the prediction model, that we kind of try to develop a model that would do a good job at predicting whether somebody would be employed or not when their rehab claims are closed. Within the prediction model, which specific VR services have the strongest association of competitive employment for VR participants with TBI who advanced their post secondary education? So, kind of wordy, but if we know a little bit of information of each of the service they got within this model, you can kind of figure out, like, which of the services are doing more of the prediction work? You know, kind of carrying the load and sort of telling you the way that things have turned out. So, use this technique and an analysis approach called binary logistic regression that I think it's [inaudible] to look out for anything like that. But it's just, you know, inner information about the different services they got. And try to use them the service information, use that to kind of develop a prediction model, in essence. And will tell us whether somebody's employed or not.


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>> And to do it, basically, we had to manipulate the data where when you look at the 911 data, there was a whole set of various reasons why the case would be closed. We'll talk about the fact that in our 911 data, these are all closed cases. So, I think there are 8 or 9 different categories of why a case would be closed. And so, we basically just broken it into, you know, this yes/no kind of binary question, where they can [inaudible] closed. And then basically, all of the categories were classed together.

>> I'm going to just respond to a question that Nick asked, which was a repredicting of college. And it [inaudible] college. [Inaudible] yes, they have completed some form of college. So, different degree levels, that type of thing. Yes. And, here, what we're specifically predicting is, whether they were employed or not at the end of their [inaudible] plan. We sort of talked about, just, kind of like, thinking about the next level analysis. I'd like to actually kind of bring a similar approach to bear on to see whether we can predict whether they complete college or not. Like, setting that as an outcome as opposed to setting employment up as the outcome. Are there certain services or supports that VR can put into place that seem to be reliably associated with people doing well in college as opposed to doing less well.


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>> And it, interestingly, you know, Mark mentioned when we  this present study was presented as the rehab psychology division 22 of the American Psychological Association. We got that the proposal, the one today it was accepted. But the thing that Mark just talked about was not accepted. So, you know, it's kind of basically [inaudible] viewers and the people that reviewed that other question. Why did they complete college? [Inaudible] I guess they didn't see a vision for it. But, I think that is an important area to look out for future presentation and research.

>> Yeah. [Inaudible] might be more interested in something like that. Or NCRE might be more interested in something like that. And rehabs might be interested. So, we still saved all the language [chuckles]. So, I'll talk  I think it's me, right? Talking briefly about the data source or is it Chuck? Or did you already talk about it?


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>> Yeah. Let's see here. All right. So, I mentioned that we looked at this 911 data in the past. And basically, the 911 data is presented in terms of a fiscal year. So, you know, [inaudible] federal government doesn't go by how the rest of the population looks at time, they have their own unique way of telling time, which is like October 1st to September 30th. And they looked at all VR participant cases closed during the federal fiscal year. So, I mentioned in data, and for researchers like, you know Ron, who's in the audience. Mark and myself, you know, we love this kind of data. We can just look at it all day. And there's so many different directions to look at. So, we've got, in just, you know, this is not like over several years. Just in federal fiscal year 2013, 589,402 cases to look at. So, those are all the cases from all 50 states in all U.S. territories where there's a state VR agency. Now, let me talk about the fact, when we get into limitations. On the surface, that is like, incredible and awesome. But there is also a drawback in the sense that you have to rely on the individual counselor at all these different 50 state VR agencies and U.S. territory accurately inputting the data. You know, so, they are like part of your research team in a sense. And you can't always have complete faith that that data has been accurately been entered. So, that is, if you ever see a study with RSA911 data, there's always a limitation because you don't know. There probably are mistakes. There are probably are different ways that these different service categories are interpreted. You know, so, you have to work with it. But with the idea that, you know, there probably are some errors in the data.


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>> And that, just to add to what Chuck was saying. That 589,000, it's pretty reliable year to year. You're going to be somewhere in the high 500 thousands, low 600 thousands in terms of cases closed. So, it's an awful lot of folks. It's limited information, but it's about a lot of people.

>> Okay. And this data is free to [inaudible]. So, for a masters students, if you guys want to do some some kind of project, you can write to the U.S. Department of Education and they will provide it to you. Now, Mark is going to talk about they don't provide it in a user friendly format, but it is free. And it's available to everyone.

>> Yeah. So, Nick is saying there's a question. Well, [inaudible] was just wondering are you closer or are you closer to the microphone?

>> Me?

>> Yeah.

>> Oh. I'm getting closer to the microphone.


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>> Get a little bit closer, it's kind of difficult to hear. Bring it a little bit closer.

>> Hear Chuck a little bit better, too. Okay. So, yeah, you can go to the next slide. So, Chuck mentioned that it doesn't come in a particular userfriendly format. So, that's sort of what it looks like when it comes to you. It's just a text file. You can request it through the Department of Education. And it is basically like a 1 line of this stuff per person. So, about 589,402 cases would be 589,402 lines of data. And it's so they can't email it to you. It's too big for that. They have to accepted you a disk with it. And you have to promise not to share with anybody else and stuff like that in raw form, you know? And then from this, you kind of have to work on bringing it from a format like that. Like, each character in each line tells you something about somebody. And there's a sort of dictionary that tells you what each of those things is. But you have to kind of figure out how to bring that in. We use a program called SPSS, which is just like a little tool, a computer application. And so, we have to import it in and, you know, basically, tell SPSS what each character means. You know, and so, it's labor intensive. But once you get it set up once, you can use it again from year to year until they make a change and you kind of have to go back to the drawing board again. And they're changing it this year, so, we'll be kind of going back to the drawing board. But it's, you know, all in all it's worth it for, you know, kind of the fairly rich data that you can get out of it.


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>> Hey, Mark, do you want to mention the idea about you doing a syntax article on 911 data?

>> [Chuckles] this is an idea Chuck had yesterday. [Chuckles] of me doing an article. So, years ago, a couple of  I can't remember who these, I know who these guys are. But we [inaudible] yeah, published an article, I think, in the 90's basically, that contained a recipe that for folks who use SPSS, had the recipe for importing the data. And it, you know, the format has changed considerably. So, Chuck's idea was, hey, wouldn't it be a good idea for Mark to publish something where he puts together the syntax and then shares it with everybody. And it's kind of an interesting idea. It would certainly get, it would save other people time, I think. So, I haven't had enough time to think about that [chuckles]. But it could be something interesting in the future [chuckles]. Yeah, okay. So, we're kind of at about the halfway point, Chuck. It's kind of the key variables that we've focused in on. And these are embedded in the data set. Is we focused on competitive employment as an outcome. So, when their cases close, and these 589,402 cases were all closed in the fiscal year. It wasn't because they were open in the fiscal year. They could have been open in the 70's and they could have been open in, you know, 2013. But they were closed in that particular year. So, at that point, they have outcome data on people. So, we look at competitive employment, which is not simply if you have a job, but are, say, or, you know, the state federal VR agencies define it very specifically. It's got to be, like, a competitive employment setting, meaning, you're working with other people with and without disabilities. You're not in the, like a sheltered workshop where you'd be only working with people with disabilities. You're making above minimum wage. I'm trying to think of what else was in that definition besides that. You can be selfemployed. But basically.


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>> With or without support, right?

>> Yeah. Right, if you're not, I think, I don't think you can have longterm ongoing support [inaudible].

>> Competitive employment list support [inaudible].

>> Right.

>> So, business enterprise program to state run program for individuals who are blind who set up [inaudible] facilities, stores. That counts.

>> So, when, Chuck and Mark, when you look at the outcome variable compared to employment, it wasn't simply a binary yes/no.

>> No.

>> It was [inaudible]?

>> Right. And so, we had to sort of reinterpret it as, meet the parameters and competitively employed/ does not meet the parameters of the competitively employed. And the reason they might not meet that are a number of, you know, quite vary. And Chuck may, you have an explanation of that later on, don't we Chuck?

>> Yes.


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>> Why somebody would be closed and not be competitively employed. We also looked at education level on their way in and on their way out. And that's kind of tracked down [inaudible] level. So, like, no formal education. Elementary, you know, there's several categories. Up to things like graduating from high school. Some folks secondary education without a degree. And the one's that we really focused in on, you know, completion of, this is grouped together, an associates degree or vocational technical certificate. Bachelors degree. And then they grouped anything higher than that, sort of, together. Masters or higher. So, we were using that to kind of figure out who came in, got college and advanced their overall level of education. And then there are also the service variables that they talked about before. There's a whole list of services that can be provided. And we'll see a number of those. But there are things like assessments, diagnoses and treatment of impairment. Counselor man guidance. Occupational training. On the job training. You know, academies. Remedial and literacy training. All these kinds of trainings that are in services rehab technology. Things like that that the agency can provide. And we basically had to treat those the analysis like a yes/no. So, they either got the service or they didn't. So, like, for example, they got college. They might've started one class and is not finished it. That would still count as a yes, they got some college. They might've gone from, you know, elementary education and finished a master's degree. That's also going to count, like, they got some college. So, the variables are kind of relatively insensitive to, kind of the amount or the quality. But we can just tell whether they got or they didn't get it. And those are, kind of the variables, that are sort of at the heart of the analysis that we'll get into.


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>> All right, so, population we looked at. I think, you know, kind of looking at the slides of the analysis, if we look at the amount of people that are served in federal fiscal year 2013 overall. And we look at specifically, the amount of people that had a primary disability of TBI that were serviced in the system. It's really small. And especially, you know, we talked about before, the Centers for Disease Control will estimate that there is at least 1.7 million new TBI's every year. But we only have around 9 thousand people with the primary disability of TBI that were served in the entire CPR system. So, we're now looking at, just California, you know? This is across everything. And for a small population, compared to the U.S. population of people with TBI. It's a very small population compared to the other disability population serving within the state VR system. And then if we look at the more, specifically, the kind of analysis we're looking at. All the 8,948 people that had a primary disability of TBI closed the federal fiscal year of 2013, 845 were provided with some form of college. And that number, 273 advanced to a higher degree. So, this is a very small number. 273, again, out of the entire state VR system. And so, when we talk about the advance to a higher degree, as Mark was saying, it's basically, you have somebody come in and they have some level of education. And the 3 numbers, they are advance to associates degree or VocTec, advance to a bachelors degree, advance to a master's degree or higher. That's where they advance to at the time they were closed. So, theoretically, it's possible, let's say, if you have somebody with a  a kid with a high school degree. And they were closed with a master's degree. They would be included in the 33 number. I mean, I think it's probably more likely that you have people that have advanced, like one level. You know, so, they came in here with an associates and they finished with a bachelors. Or they came here with a bachelors and they finished with a masters. But that, again, that's like their end point.


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>> And just kind of highlighting one limitation, you know, Chuck said it's a real rich data set, but it's got its limits. So, one of the things about that is we can't pick up on things like, somebody comes in with a bachelors degree and they earn a second bachelors degree. We can't figure that out in the data set in the way that it's tracked. Likewise, if somebody comes in with a master's degree and gets their PhD, we can't tell because they're all lumped into that same category. Ron has a question.

>> For Chuck and Mark, it's been [inaudible] since I looked at the 911 data. And Mark you mentioned that [inaudible] for some variable redefinitions next year. But have they, on [inaudible] tracking here. They used to have type of disability and then etiology, okay. So, did you use those in combination to take a look, to try to get at TBI? Or was it something that was more, like, congenital?


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>> Yeah, well, I think we did. So, they had cognitive impairment and what do they call it, it's area impairment and cognitive Impairment.

>> Something like that, yeah.

>> Yeah. So, area of impairment would tend to be more functional. You know, if the issues are, I don't know 

>> Like cognitive impairment would be one.

>> Right, exactly.

>> And [inaudible].

>> Good, good. So, we didn't use that impairment to select these folks because cognitive impairment could arise from a number of different things. But within the cause of impairment, essentially, there is a category of TBI.

>> Okay.

>> So, we zeroed in on that population and would exclude people who might have similar function of limitations due to another etiology.

>> Okay. So, that's good. What about  are they doing anything new with age of onset?

>> Nothing there. Well, as far as I know  well, I probably can't say for sure. Until now, nothing on that. I don't know if the new stuff  I think the new stuff actually focuses in more on  I think we may be able to tease out more specific, say, college outcomes, with the new data.

>> Yeah.


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>> Then we can with this data. I don't think there's anything related to that, but I wouldn't testify to that in Court at the moment [chuckles]. I wouldn't look it up, yeah. And, in fact, that's one of the things, you know, when the state comes to us. And I should have mentioned this earlier. You know, the feds are [inaudible] make it [inaudible] gets to us. So, things like, social security numbers are stripped up. There's no names. There's no addresses. The closest you can zero in on geographically is a state or a territory level. So, you know, you might know that they're on American [inaudible] but you wouldn't know anything more than that. Or you know they're in California, but you wouldn't know more than that. So, it's really hard to figure out who is who. One of the variables I really like to have that we don't, we used to get age. Actually, they didn't give you  yeah. They took that out now so you can't even figure out how old people are. So, I thought that was kind of interesting.

>> Do they leave out, like, one's culture as well?

>> They have, kind of some very broadly defined. They have, I think, 5 race categories and one ethnicity, [inaudible] ethnicity. And those are all answered on a yes or no basis. So, you can say yes to multiple races and ethnicities. But it's not super sensitive. They're pretty broad definitions, like, white, black or African American, Asian, you know. And there's a lot of differences embedded within each of those categories. So, you can't tease them out. So, Chuck, you want to talk about gender?

>> All right. Yeah, in terms of the gender distribution, we're going to talk about with the demographics on the gender. Another possible limitation is how representative is this population of the overall U.S., you know, the population of people with TBI. Of the 273, we had 143 that were male and 133 that were female. And that's, you know, you really don't see that kind of equal, roughly equal distribution of gender in the overall TBI of population as many more people that are male than female. And, you know, one of the reasons, historically, has been males are more attracted to dangerous pursuits. Or the kind of work that they do may be more inclined to give them a brain injury. Or, you know, if you look at the military personnel, they're more likely going to have males who are going to be in combat zones. Although, these days, you know, in Iraq and in Afghanistan, there really is no, like, combat and noncombat zone. That basically, everything, potentially is a combat zone. But I think we didn't really expect to see this kind of number. So, you know, and again, it calls to question, why would that be so close? So, we're not really sure. And then, I mean, that might be [inaudible] to kind of speculate. Do you guys have ideas on why that may be kind of roughly the same? This is one of the things that Mark and I will be talking about in the manuscript.


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>> One thing that occurs to me is more women are going to college than men now. So, that might be a factor.

>> Okay. That's a good point.

>> You guys mentioned earlier that the VA has their own data set. So, if, for example, if VA and Workers' Comp are targets for these people of head injuries to get their services, you're not seeing them show up under RSA. And, that would have siphoned off more males generally than females?

>> Yeah, true.

>> Yeah. That's a good point. Yeah. Yeah, Mark, we should mention that. I think that's an important thing to remember for the article. Again, you know, we had to question how representative is this. [Inaudible] match the population of people with TBI in the United States. So, you got out of 273 people, you know, 241 are white. And that's going to be a major limitation in the articles, you know, on many kind of [inaudible] who would try to [inaudible] from this. [Inaudible] on what might be happening, you know, I'm not really sure. Mark, what are your thoughts on this? I feel we kind of went back and forth on this and, you know, this, I think potentially some, for us, issues that may be going on. But the only [inaudible] are that is that this might be another area to get some ideas from the crowd.

>> I think Ron had his hand up.

>> Yeah. Should I be looking at this large [inaudible] to represent 273?

>> Yes, 273.

>> I'm having trouble with my arithmetic.

>> So, people can report multiple races and ethnicity.

>> Okay.

>> So, in fact, almost everybody that, I think, I looked at the entire data set. And, for example, almost everybody who checks they're Hispanic and Latino, also checked either white or black, you know?


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>> I'm thinking for publication purposes, you might want to include that a multiple column or something to make sure your numbers look right. And again, it's good. This, just like the previous one, [inaudible] females insights that point that who is being captured by RSA services doesn't necessarily represent the national profile. And it would be advisable if you follow [inaudible] like recommendations for further research would be to see what's happening under VA rehab or Workers' Comp rehab [inaudible].

>> Yeah, these things are a subset of a larger, even the larger VR data set. And this race and ethnicity information doesn't even line up with the larger  you know? So, that makes you think, okay, is there something systematic going on within the agency? Counselors have attitudes about who's college material and who's not? Or even kind of things where you find, certain types of service, like, [inaudible] like. Some states, you'll hear kind of an unstated policy, like, we're not paying for college for anybody. You know, in other states, they are, you know? And I don't think they don't come out and put that in print or anything like that. But let's say, at a given point in time, there are more, states in the Midwest that are, kind of, okay with college. And some of the states is more, ethnically diverse areas, or racially diverse areas are kind of clanking down at the time. You can see stuff jump around, like dynamic, like that I doubt we'll ever be able to teeth out. But it's certainly possible that that's a factor.

>> It's certainly worth mentioning, it's a caveat. Chuck, can you hear from the audience okay?

>> Yeah, I can hear you, yeah. Yeah. All right. So, we look at the living situation. I guess this is another area where it's probably not all that surprising because 286, almost everyone that we have captured here, or actually more. So, Mark, I just noticed that it's 286 and we had 233, so. How does that happen [chuckles]? Well, anyways.

>> I agree with Chuck, apparently. Chuck, [inaudible] twice.

>> Yes [chuckles]. I would really like to look at this one. But anyways, it makes sense in terms of why you would have multiple people coming from a private residence. Because if they're in a rehab facility, they're, you know, not really at a point in which they could be looking at college and a nursing home. Same with the homeless shelter. You know, I think, this gets back into another limitation, the 911 data. We don't know if this means if they're living with family, if they're living in their own apartment or house. Or if they're living with roommates. But, you know, generally, they're all lumped into together in terms of private residence.

>> Yeah, I just checked that. It's 266. So, it was [inaudible] on my part.


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>> [Chuckles] all right. Well, at least it makes sense now. Okay, good. All right. Again, kind of just providing a profile of the people that are part of this study. We see that most people are not receiving public SSI and SSDI, you know, benefits. So, on one end you have the market [inaudible] we're kind of surprised. We thought, this is a population that you can make an argument, you know, for challenges in terms of getting back into employment or not being able to be employed. But if you look at, like, who was actually able to get into college or could benefit from college. Perhaps, your level of disability is not high enough to qualify for SSI or SSDI. And maybe they don't know about the benefits. Maybe they haven't pursued that. Maybe they've chose not to receive benefits and instead of trying to go toward employment. So, you know, a number of possible factors going on.


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>> Okay. So, now we'll talk about findings a little bit. So, yeah, good, thanks [inaudible]. Just some kind of basic findings. When we've talked about early on some of the issues or some of the characteristics of cases that involve college. And so, this is for the 273 folks. You can get, like, an average cost of purchase services. This would include money spent on college. But money spent on anything else. So, if they got a job coach, that money would be in there. If they got an interpreter, the money spent on that would be in there. Any kind of  so, it's not really a measure of how much college costs, but you can certainly look at how much was spent on, you know, a particular case that involves college. And it's pretty interesting. If you would see a case like. So, for the 273 cases, the average was $19,750. That's a pretty substantial investment of funds. I'm trying to remember, if we look at, like, across all disabilities, anybody who got as far as having a rehab plan developed, the average was $4,500. So, it was considerably larger. And, Nick?

>> During your presentation, was it deaf of hard of hearing or blind field services slash deaf and hard of hearing?

>> Deaf, blind. Yeah.

>> The most expensive?

>> That was among the hearing, you know, that was one way of, kind of, slicing the data by the different hearing impairment categories. Deaf, blind, [inaudible] I think fairly expensive.


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>> Do you remember, correctly, but where would that cut came from was a type of disability? And what Mark was saying was, he didn't  to call that group out. To focus on that group, you can go by type of disability, use cause of disability. So, when that data was presented, seems like a million years ago. We weren't  people with traumatic brain injury were not captured and compared as a group because it was a different variable. That's an amazing figure here. Gees.

>> It is really. I put the median down there as well. So, Dr. Jacobs used to teach this 690 course before I did. You know, when you see, like, a discrepancy between the mean and the median like that and, like, a such a large standard deviation spread. You're thinking, there's got to be some outliers on the high side, right? And I didn't spend a lot of time analyzing this, but I did look a little bit. And there was one case in there that was $181,000. So, that's pulling the mean up by slightly less than the thousand dollars for each case, you know? So, the median, half of the cases, we spent more than this. Half of the cases, we spent less. That's $11,656, which is still a pretty substantial investment in more than twice with the typical case involved. It's just some other indication of effort. You know, in resources invested, both, in this case, both on the part of the client and whoever's, the agency supporting. The average case duration, 2,334 days. So, that's 6.4 years. So, yeah, and that's from the time the case was open until the time the case was closed. So, that's, again, a substantial investment. And then, kind of, speaking to this idea of mixing findings regarding the, you know, the value of college in a rehab plan. This is, at least, with the people who complete, you know, advance their education. 75.5% of those folks were employed at the time their case was closed. So, that's a considerable improvement over if we were to compare them to the people with TBI who get as far as developing a rehab plan. Meaning they get to the point where they're getting services. But that larger group wraps up, you know, when their cases are closed up, 45% of them are competitively employed. So, it is associated with, you know, a considerable difference in employment rates for them. And so, going back to this original research question that we talked about. Which combination of VR service variables best predicts competitive employment outcomes of VR participants with TBI who advanced their post secondary education? So, this is the 273 folks. Chuck, do you want to talk about the predictors?


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>> Yeah, so, basically, you look at these common types of VR services. And as Mark said before, it was a yes/no were they provided or not provided. We don't know, like, the intensity in which they were provided. And in some cases, the predictors could also serve as predictors of negative outcomes. So, basically, you know, Mark will get deeper into how we look at this synopsis. That these were, in all the factors we enter into our prediction model.

>> So, just real quick, because I know the text is small. So, things that round up in the final model as a best way, guess, best model that the analysis technique could come up with were things like, vocational rehabilitation counseling and guidance. Disabilityrelated augmentative skills training. Job assistance. Job placement assistance. On the job support. Transportation services and information referral services. Things that got kicked out: Assessments, diagnosis and treatment of impairments. Meaning, these things didn't add substantially to the model's ability to predict outcomes. Occupational and vocational training. Basic academic, remedial and literacy training. Job readiness training. Miscellaneous training. Maintenance. The rehabilitation technology. And other services. And there were about 5 services that we checked out, that, they were provided [inaudible] folks. Like, 1 out of the 273 people got it. Or none of the 273 people. So, we took those out of the analysis beforehand to avoid problems. So, sort of what we found out are what we learn through this exercise. How much of the variation of competitive employment outcomes could we predict if we knew which Rehability services folks got or didn't get. Well, it turns out, like, if you think about, being able to predict 100% would be  you can predict, you know, properly, the outcome, the employment outcome for every single person. Like, if you give me these 8 service variables, I can, with 100% certainty, tell you whether they were employed or not. Well, if we were shooting for 100%, we got 15 and a half percent [chuckles]. So, we cannot predict the whole lot of the variance in employment outcome. But this is, I think, is probably a good question to talk over. You know, we have  why is it that we can only predict 16% of the variation of employment outcomes based on having information about whether they got these with the [inaudible] rates or  any thoughts?


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>> Yeah. It's almost like you guys are apologizing for hitting 16%. [Chuckles] let's talk about 16%. Would you be okay with the 16% raise?

>> [Chuckles] yeah.

>> You know, given  looking at the variables are fixed. You know, thinking of how you would write the article. If these variables are fixed, they're entered by a number of counselors with a varying degree of enthusiasm to be precise in their data. And you two guys know better than anybody else, there's a lot about the variables that lack precision. A lot of times you're dealing with yes/no when it's like, well, yeah, we provided this service to her but  let me tell you more about it. And you don't get a chance to do that.

>> Right.

>> So, I think, if you could  what I would do would be to describe this in terms of 16% is a sizable chunk. Not what's wrong that we only got 16%. If you were screening somebody, an applicant for, you know, a competitive employment position or something like that, and you had a variable that would provide that much accuracy in predicting his or her success on the job, that'd be a pretty powerful tool.

>> Yeah, we're probably right up with that the GRE, with our 16%, you know what I mean?

>> It's not about GRE's, it's way [chuckles].

>> We beat the GRE. We charge 300 bucks [chuckles].


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>> Well, that's how I'd be looking at it. I think it's something to brag about. And the other thing is, too, because you're not looking  one of the things that I would be real interested in knowing about is what's happened? Seems like one of the biggest, most rapidly growing areas in this client population is war areas. And you've probably not seen too many of those people come through this system.

>> Right.

>> But this other system that I don't know the extent to which they're capturing and scrutinizing that kind of information and you may not either. And maybe the best you can do, in putting together a manuscript, is to strongly voice a recommendation for further research that would take a look at these other systems like the VA.

>> Yeah. I mean, that's a good point.

>> It's a great idea.

>> Yeah. Yeah. And I think we can probably make a statement about the utility of variables that are defined more precisely. So, if you got job placement assistance was it a 15 minutes with a disinterested high school grad or whether it's four months of intensive services with a, you know, a rehab counselor.

>> Yeah.

>> And that we don't get. We don't get, the got or  no, they didn't.

>> Chuck and Mark. When the new, or even the 2013 911, are they including things like [inaudible] scores to get its severity?

>> No. I think  so, there is a variable in there for [inaudible] disability on a yes/no technically. And it's basically checked for everybody.

>> Yeah.


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>> So, it doesn't really work as a  and outside is level of severity of disability. And another thing about them is I think each state uses a different approach to assessing the severity of an individual's disability. I think they might have a little bit of trouble kind of bringing those together across. I mean, I just have a few minutes left. So, I'm going to kind of [inaudible] up a few of these. So, yeah. One of the ways that you assess the model is by looking at how other classified cases. In this case, because we have the data already, we can compare, sort of, the outcome to the model's ability. The known outcome to the model of the way they predict the outcome. And so, if you don't have any predictors in this model, I think the takeaway is the number on the bottom right hand corner, you can sort of correctly predict 75.5% with nothing, with no information. Because 75.5% of the people were competitively employed. So, what the model would do is, in the lack of any better information, we're just going to predict that everybody's successful. And we're going to hit right 75.5% of the time. So, it doesn't predict correctly any of the not competitively employed people who are not competitively employed. Because it predicts that they're all going to be employed. So, it's got a 0% rate in predicting that correctly. And then 100% on the competitively employed because it's simply predicting that everybody's competitively employed. And if you switch it to what the models with their predictors in there, actually, we'll be getting nothing better than simply predicting that they're all going to be successful. Again, that's 75.5% that is predicted correctly. The correct prediction rating proves on the people who are not competitively employed, it now, you know, 17.9% of those people it can correctly predict. Which is still terrible, not a great rate. But yet, it actually loses some of the  it doesn't predict the competitively employed people nearly as well. It predicts only them correctly only 94.2% of the time. So, we had hoped for better with this [chuckles]. And I'm just going to keep going. So, that's not great. But this kind of model actually gives you some other interesting information that, I feel like, is interesting, even if something like that is not terribly interesting. So, the idea here is, okay, within the prediction model, which of the services are doing most of the work? Which ones are contributing the most to the abilities to correctly predict? And so, Chuck will talk about that for a second.


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>> You know, a lot of this makes sense. So, if we look at VR counseling and guidance, you know, especially with a lot of the emotional and psychological kinds of adjustments. You know, the people with the TBI have to make in terms of their vocational adjustments. You know, it makes sense why having this kind of enhance guidance and counseling in the VR process could be helpful. And we also look at, you know, job search assistance. And things like helping with interviews and how to get ready for the job search. And all those kind of support you might get in that area, like job club might be one example. You know, it also makes sense in terms of what's going on. And if we look at VR counseling and guidance and job search assistance, the one program that comes to mind is, maybe like a model trying to do this kind of thing. It might be like the work ability programs, like work ability for free with a community colleges or work absent for with the four year universities. I think in a lot of ways, you know, they do do this kind of thing. But one of the things that Mark and I also saw that was not really expected, and we'll have to try to explain this in the manuscript. Is that the person who received the transportation services, they were less likely to be competitively employed. And so, Mark and I, we're kind of speculating, you know, what might be going on. And one idea may be that, you know, just perhaps this person has a higher level of support overall. Maybe it's expressed, you know, through this type of variable. And perhaps that, if you need enhanced transportation, it might just relate to a high level of need in general, which would reduce your chances of becoming competitively employed. I mean, it's kind of a stretch in terms of thinking. But, you know, we're still trying to think to try to kind of wrap our minds around this. Mark, anything you'd like to add on that about transportation?


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>> Well, just, I want to kind of highlight something here that these odd ratios are, I think, one of the things I really appreciate about this common analysis. So, Chuck mentioned that three services that came up as statistic [inaudible]. So, the one's doing a lot of the kind of pulling up, you know, in terms of prediction. And the odds ratio is pretty easy for people to interpret. So, the way to kind of look at the one, we only interpret the one's that are statistically significant. But for New York counseling and guidance, you see the odd ratio is 2 .272. The way to think about that is, an individual who got VR counseling and guidance was 2.2 times as likely to be employed at the end of the case as somebody who didn't. Okay? So, the same thing with job, what was it? Job search assistance. A person who got that was 3.3 times as likely to be competitively employed when their case was closed as a person who didn't. And then the one that's maybe a little harder to figure out is transportation services. A person who got transportation services was only fourtenths as likely to be employed. So, they were actually less likely to be employed if they got transportation service than if they didn't get transportation services.


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>> So, the [inaudible] level on the job supports is not statistically significant. But the odds ratio indicates [inaudible] times more likely to. I would think that that would be a big predictor. I mean, based on articles that state that supported [inaudible] on predicting successful employment outcomes.

>> Yeah, I didn't [inaudible] in this particular model. But the observation ratio is very large. You know, if somebody is getting on the job support, they're already in an employment setting. You know, which is another kind of consideration is, their feet is already on the floor some place. And that can wind up to be kind of a considerable advantage, if you're actually in a  get yourself as far in rehab as, you know, to actually being at a setting. Yeah. So, you know 


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>> There could be 

>> Go ahead.

>> It could be a [inaudible] issue we may not have enough sample size to result in this significant finding.

>> Yeah. Okay. So, we're going over on time a couple of minutes. Real quickly. Were you talking about this or was I talking about this [inaudible].

>> Yeah, you go first Mark.

>> All right. So, in terms of implications, it kind of comes back to the evidencebased contributing something to the evidence base. Not everything turned out exactly how we wanted in the study, but I think we do have some things that we can kind of come away with that are useful in terms of adding to the knowledgebased around individuals with TBI who go to college. What their outcomes are like, what kinds of services are associated with successfully and sometimes unsuccessfully closed cases. And, you know, that relates to the identification of potential supports. If we understand that, or at least from our, you know, looking around, the VR counseling and guidance and job assistance are pretty important in the models. And we can start to, you know, that may be something that can translate to the field a little bit. If I got a client, I'm working with them. I'm not sure how this is going to be turn out. I might think about, okay, I'm going to look to literature and find out what types of services have been associated with successful closures in the past. Likewise, I wouldn't say that counselors should say, go, oh, I'm not going to give transportation assistance to anybody because these people don't have good outcomes. But it might be a flag of somebody who might need a little bit of support. If they need transportation assistance, it's likely they don't have much in the way of money. They might don't have a way of social support or friends who can drive them around. And so, if a [inaudible] can see that as an indication that, okay, I need to focus a little bit here. You know, I'm working on a client around these issues. That can, then, also be leveraged to be something that tips the balance, you know, towards the favorable outcome.


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>> You know, I think just this general, for counselors not to assume a college education is going to lead to a good outcome that there's a combination, there's a [inaudible] of support. The person who need  you know. Hopefully, if they have that mindset that can be one major contribution of the study. And it might be a different thing for every population. And, you know, for one thing, Mark and I talked about is looking at these kinds of factors for different populations. Or maybe, if we look at it in terms of areas of impairment as well instead of the cause of disability, there might be a number of ways we kind of further analyze it.

>> [Inaudible] you have a question?

>> You have been inputting that with regards to the sample size and the distribution between men and women that women may be more likely to seek out department of rehab services [inaudible]. Those are two [inaudible].


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>> Yeah, it's possible. We could certainly look over the years and see if there is, you know, who they've served other the years. Find out if there are differences in access. Yeah. So, last slide. And I know we've kept you over time. I apologize for that. Just a few of the limitations associated with the research. You know, there's always precautions about not over interpreting. So, it's an ex post factor design that we really wanted to, kind of, assess without any doubt, some kind of, whether vocational or a rehab guidance counselor has in effect. You have to, kind of take through [inaudible] randomly assign them and give one of them the service whether they need it or not. And the other doesn't get the service whether they need it or not. That's just not ethical to do. Still, there's still limitations to be effective because to the extent that we can assume cause and effect. And also, the procedure that we did, that we looked at a bunch of different variables. And the model sort of kicks out the ones that are not doing a good job at predicting and keeps the ones that are. But one of the things that can happen is, say, two variables that are kind of weak on their own might team up and explain some of the variance that one variable, you know, would also explain. If there is some commonality, something's going to get kicked out. So, you could actually have good predicting service variables and getting kicked out because two or more of the variables are on this side. Or one or more of them covers the same territory. So, you know, the job placement actually usually services in these analysis as being incredible. Often time it's first, it's the most important. It's the one carrying the most weight. And it didn't show up in this point. And that my suspicion has been job search assistance. And probably something else cover the same territory. So, that's kind of the key methods there is, don't assume [inaudible] our particular model, that is not important. Chuck, anything you want to add?


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>> Yeah. You know, we talked about the population issue. I think [inaudible] question [inaudible] community. Just with the, yeah, the number of people. The gender of distribution. The way the assessments lead to distribution. You know, I think that is the shortcoming of the [inaudible]. But it's, you know, again, it's limited by the data in that you have benefits from the data. You have limitations to the data. But, I think, in the need it's still valuable to get the information out despite this kind of limitation.

>> All right.

>> That's it. That's all I have.

>> Okay. Any other questions?

>> Did you  on the transportation  did you include looking at the secondary disability?

>> No. We didn't. We talked about that. We talked about whether we wanted to bring in people with TBI as a primary. And people with TBI as a secondary.

>> Yeah.

>> We didn't do that. We just went with people who had TBI identified as the primary area cognitive impairment. And likewise, we didn't do an analysis of whether there was a second variable disability present or not, but we certainly could.

>> Yeah. But just speaking on transportation [inaudible] sort of a parenthetical analysis real quick and how secondary disability yes/no by the transportation issue.

>> Okay. Or even, we could potentially look at that and say, okay, and if there's a secondary disability, is it one that would impact mobility?

>> Correct, right, right, yeah.

>> I wonder if, like, the geographic location had anything to do with the transportation services as well because in other research that I've read, you know, it shows comparing also 911 database that those living in rural areas will have less access to transportation services. And therefore, it's more difficult for then to become competitively employed. And then there's other predictors involved too. But [inaudible] one of them that's strongly associated.


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>> Yeah. I would think that, you know, it could very well be that that's the case. These folks were able to get themselves to and from college long enough to get to, you know. So, they had transportation [inaudible] in college. But, you know, it could be that they live next door to the college. And work is 15 miles away and there's no buses to, you know. And we've certainly found that in kind of rural areas. I'd love to have the ability to kind of drove down more geographically than at the state level. But for right now, what you get is there in California. Are there in Nebraska? You know, which, you know, those two you might be able to kind of make. But there's some, you know, you might say, well this state's more urban than this state's more rural. But even within California, we've been places where, it's like, there's no buses here. And there's no buses within 50 miles of here. And, you know, so, it's [inaudible] zip code or something like that would be awesome. County, yeah. We'll put it on our wish list that we can send to the feds.


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>> Well, it's all limitations and [inaudible] exactly. And I'm sorry I don't know your name but 

>> Nick.

>> Nick's comment is great.

>> Yeah, yeah. I'm sure, you know, like, just by doing the [inaudible] have some state agencies. If you go to a rural area, transportation [inaudible] are much bigger usually than if you're kind of in an urban area. So, you know, we'd be like okay, we're in San Francisco. It's not a problem. Or like Washington, D.C. There's no problem. You can enter into the district, you know, on public transportation. But then [inaudible] or something like that, it's not happening, yeah.

>> [Inaudible] same area.

>> [Inaudible] or you can argue that it's an issue because the public transportation's really not nearly as good, like, the Bay Area, for example.

>> Right. Yeah.

>> So, you live in an urban area, you know that some areas are going to be better served than others.

>> If you're not in San Francisco, Boston, Chicago, New York, or DC, it's probably an issue. I mean, those are the  really, not that many others that are model transportation systems. John?

>> Of the sample of just the TBI population that we looked at, 8,948 really tell or hash out one of the great shared in the cost of VR services that they received? Or was this [inaudible]?

>> We didn't do that. But you can do it to some  I don't know if you could answer shared. But one thing you could look at is who paid for it. But there's only one category. You know, there's only one listing of who paid for it. So, one option. So, they're probably  anything in there. They're probably saying the state agency.

>> Yeah. I was just curious of the information about SSI, SSDI just be [inaudible] with the difficult case.

>> Higher representation of people at SSI, SSDI than this particular group. Yeah. Their ability to contribute could potentially be a factor. But I'm kind of guessing that this is a guess. But I'm sort of guessing that most of the folks that went to college and advanced with their degrees were probably on the lighter end of the TBI spectrum. And so, that probably had a lot to do with dictating who. Which I think a lot of folks with TBI who were on SSI or SSDI, may not have, you know. We could actually do that kind of analysis but we didn't. But, yeah, we can compare them to the folks who didn't get college. And then look at if there is discrepancies in their grades in which they are accessing SSI, SSDI. And we can look at those sort of comparison.


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>> I'm just wondering if that computed to the transportation issue.

>> Oh.

>> This example had more resources.

>> We can look at that because you also get  earnings and application and you can when their case is closed. And so you can use that. So, you can use TBI as [inaudible] indicator of weekly that you could actually match. There's a variable limit of it just sort of says. You know, like stuff that worked. But this is how much the person said they were making on the week end on the way in and the way out. So, we can probably zero in on that pretty quickly.

[ Background Noise ]

Well, thank you. Thanks for staying late. I really appreciate it.

>> Yeah, thank you guys.

>> Thank you Chuck and Mark.

>> Our pleasure.

>> Thank you [claps].

>> [Inaudible] I didn't mention this. Chuck's not here because he didn't want to be in the room with you guys. He twisted ankle earlier this week. And so, he's got a mobility limitation of his own for a little while. So, he's probably sitting with his foot elevated someplace and not walking on it. If he had his choice he would have rather been here.

>> Yeah, absolutely [chuckles].