WEBVTT 00:00:10.900 --> 00:00:12.200 position:50% align:middle - [Brendan] Good afternoon, everyone. 00:00:12.200 --> 00:00:13.730 position:50% align:middle I hope you all had a nice break. 00:00:13.730 --> 00:00:16.860 position:50% align:middle My name is Brendan Martin, and I'm the associate director of research 00:00:16.860 --> 00:00:18.430 position:50% align:middle here at NCSBN. 00:00:18.430 --> 00:00:22.360 position:50% align:middle I will be kicking off segment two of today's presentation by providing a bit 00:00:22.360 --> 00:00:26.750 position:50% align:middle of background information on the annual report, quantitative data analysis. 00:00:26.750 --> 00:00:31.340 position:50% align:middle But before I get into the details of this study, I just wanted to reiterate what my 00:00:31.340 --> 00:00:34.570 position:50% align:middle colleague Nancy said at the beginning in the first section. 00:00:34.570 --> 00:00:40.200 position:50% align:middle And that is, please submit any questions that you have to the online interactive QA 00:00:40.200 --> 00:00:43.550 position:50% align:middle chat room, and we'll be happy to address those at the scheduled time at the end 00:00:43.550 --> 00:00:45.520 position:50% align:middle of segment two. 00:00:45.520 --> 00:00:50.000 position:50% align:middle To begin, I wanted to give you a bit of background information on the research 00:00:50.000 --> 00:00:52.990 position:50% align:middle questions that really drove our analysis. 00:00:52.990 --> 00:00:57.350 position:50% align:middle But after that, I will also discuss briefly the study design and methodology. 00:00:57.350 --> 00:01:00.270 position:50% align:middle And then following those sections we'll get into a little bit the 00:01:00.270 --> 00:01:03.880 position:50% align:middle sample composition, the specific model results. 00:01:03.880 --> 00:01:08.090 position:50% align:middle And then we'll end the segment by discussing what is the typical profile 00:01:08.090 --> 00:01:11.830 position:50% align:middle of a fully approved program based on the evidence. 00:01:11.830 --> 00:01:15.670 position:50% align:middle So to start let's look at the research questions that drove the analysis. 00:01:15.670 --> 00:01:18.850 position:50% align:middle First and foremost, what we really wanted to capture in this analysis were what were 00:01:18.850 --> 00:01:22.380 position:50% align:middle those performance indicators that ultimately aligned or were associated 00:01:22.380 --> 00:01:26.470 position:50% align:middle with full program approval of prelicensure nursing education programs. 00:01:26.470 --> 00:01:29.090 position:50% align:middle And then conversely, we also wanted to know the flip side 00:01:29.090 --> 00:01:29.780 position:50% align:middle of that coin. 00:01:29.780 --> 00:01:33.390 position:50% align:middle So we wanted to know what were those criteria that ultimately aligned with full 00:01:33.390 --> 00:01:38.690 position:50% align:middle approval being removed or lost for those prelicensure nursing programs. 00:01:38.690 --> 00:01:42.420 position:50% align:middle To supplement these topics, we also wanted to investigate 80% or more 00:01:42.420 --> 00:01:46.590 position:50% align:middle NCLEX pass rates as a supplemental outcome. 00:01:46.590 --> 00:01:49.940 position:50% align:middle And importantly, we positioned that as a supplemental outcome because as my 00:01:49.940 --> 00:01:51.880 position:50% align:middle colleague Nancy said, in the first segment, 00:01:51.880 --> 00:01:55.920 position:50% align:middle we really viewed that as a lagging indicator in this particular study. 00:01:55.920 --> 00:01:58.940 position:50% align:middle And what we mean by that is, we mean that essentially poor performance 00:01:58.940 --> 00:02:02.750 position:50% align:middle on the licensure exam, is likely indicative of other program 00:02:02.750 --> 00:02:07.790 position:50% align:middle deficiencies and not vice versa. 00:02:07.790 --> 00:02:11.500 position:50% align:middle So the next slide here, you can see provides a bit of background 00:02:11.500 --> 00:02:12.840 position:50% align:middle on the study design. 00:02:12.840 --> 00:02:17.230 position:50% align:middle So first and foremost, before any outreach efforts commenced, 00:02:17.230 --> 00:02:21.650 position:50% align:middle we submitted the study for approval and review by the Institutional Review Board. 00:02:21.650 --> 00:02:24.780 position:50% align:middle And then at that point, essentially our recruitment efforts began 00:02:24.780 --> 00:02:26.020 position:50% align:middle in earnest in early 2018. 00:02:26.020 --> 00:02:31.670 position:50% align:middle At that time, we asked participants to submit any and all annual report data 00:02:31.670 --> 00:02:37.980 position:50% align:middle documentation via a secure password-protected data repository. 00:02:37.980 --> 00:02:42.390 position:50% align:middle The documentation itself ranged considerably, as you might guess, 00:02:42.390 --> 00:02:46.710 position:50% align:middle from Word documents, and PDFs, all the way to photocopies and raw data 00:02:46.710 --> 00:02:50.470 position:50% align:middle exports in Excel. 00:02:50.470 --> 00:02:54.930 position:50% align:middle So once we had that information, we looked to close the data collection 00:02:54.930 --> 00:02:58.960 position:50% align:middle period after about half a year, and we closed it formally in late 00:02:58.960 --> 00:03:01.070 position:50% align:middle September of 2018. 00:03:01.070 --> 00:03:05.810 position:50% align:middle The study itself was a retrospective cross-sectional study. 00:03:05.810 --> 00:03:09.730 position:50% align:middle It was a cohort design so we were looking at the annual report data from nursing 00:03:09.730 --> 00:03:14.660 position:50% align:middle programs that were provided to us by the nursing regulatory boards. 00:03:14.660 --> 00:03:19.800 position:50% align:middle Our first step in the data analysis was really to conduct some data exploration. 00:03:19.800 --> 00:03:24.790 position:50% align:middle And we did that by generating some summary statistics to really identify what would 00:03:24.790 --> 00:03:28.360 position:50% align:middle be the best modeling approach and ultimately settle on something that would 00:03:28.360 --> 00:03:32.850 position:50% align:middle give us the most parsimonious and ultimately predictive outcome. 00:03:32.850 --> 00:03:35.610 position:50% align:middle What we ended up settling on was the univariable generalized 00:03:35.610 --> 00:03:37.710 position:50% align:middle linear mixed-effects models. 00:03:37.710 --> 00:03:40.900 position:50% align:middle There were three advantages that we really perceived to this approach. 00:03:40.900 --> 00:03:45.450 position:50% align:middle The first was that it seamlessly accounted for the longitudinal data structure of the 00:03:45.450 --> 00:03:47.050 position:50% align:middle information that we had. 00:03:47.050 --> 00:03:51.160 position:50% align:middle The other was when we were in a position where we felt as though it was important 00:03:51.160 --> 00:03:55.890 position:50% align:middle to control or adjust for other important covariates, we wanted that flexibility and 00:03:55.890 --> 00:03:57.710 position:50% align:middle this model provided that. 00:03:57.710 --> 00:04:02.390 position:50% align:middle And then finally, the odds ratio estimates that really result from these models are 00:04:02.390 --> 00:04:05.070 position:50% align:middle universal in nature, and we hoped would be pretty 00:04:05.070 --> 00:04:07.310 position:50% align:middle easy to interpret. 00:04:07.310 --> 00:04:12.230 position:50% align:middle What we did see though when we got into the data analysis and started reviewing 00:04:12.230 --> 00:04:16.800 position:50% align:middle the data was that there were many gaps in the information that we had available. 00:04:16.800 --> 00:04:20.820 position:50% align:middle And the real reason for that at the core of it was that there really were no 00:04:20.820 --> 00:04:25.130 position:50% align:middle uniform data tracking standards across jurisdictions. 00:04:25.130 --> 00:04:28.480 position:50% align:middle We'll get into that a little bit more as we progress in the presentation. 00:04:28.480 --> 00:04:32.150 position:50% align:middle But just keep in the back of your mind that what we're presenting here today, 00:04:32.150 --> 00:04:36.100 position:50% align:middle I think, could have been even that much more robust had we had standardized core 00:04:36.100 --> 00:04:40.360 position:50% align:middle data elements across the jurisdictions. 00:04:40.360 --> 00:04:44.440 position:50% align:middle So when we discuss the overall sample for the analysis, in the end, 00:04:44.440 --> 00:04:48.460 position:50% align:middle we had 43 nursing regulatory boards that participated in the study sample, 00:04:48.460 --> 00:04:56.480 position:50% align:middle and they ultimately submitted about 11,378 annual report data elements. 00:04:56.480 --> 00:05:00.660 position:50% align:middle So while we were thrilled with the level of participation and the volume 00:05:00.660 --> 00:05:05.470 position:50% align:middle of information that we had to reference to really bring to inform on the analysis, 00:05:05.470 --> 00:05:09.420 position:50% align:middle we started discovering that there were many gaps in the data. 00:05:09.420 --> 00:05:13.150 position:50% align:middle And what we mean by gaps in the data is first and foremost, there might have been 00:05:13.150 --> 00:05:18.240 position:50% align:middle specific data elements that some jurisdictions tracked, and others did not. 00:05:18.240 --> 00:05:21.440 position:50% align:middle But even in the instance, where a nursing regulatory body multiple 00:05:21.440 --> 00:05:25.920 position:50% align:middle jurisdictions tracked the same general type of element, the naming and tracking 00:05:25.920 --> 00:05:30.860 position:50% align:middle conventions often vary just enough that it really didn't allow us to seamlessly 00:05:30.860 --> 00:05:32.810 position:50% align:middle merge the information. 00:05:32.810 --> 00:05:37.660 position:50% align:middle So as is often the case, data cleaning, and ultimately reconciliation across the 00:05:37.660 --> 00:05:44.990 position:50% align:middle jurisdictions emerged as one of the most complex challenges to the entire study. 00:05:44.990 --> 00:05:46.870 position:50% align:middle With the data in place, one of the things that we really 00:05:46.870 --> 00:05:50.800 position:50% align:middle like to do here at NCSBN is we like to provide a bit of a sense 00:05:50.800 --> 00:05:52.430 position:50% align:middle of the analysis sample. 00:05:52.430 --> 00:05:57.020 position:50% align:middle So we want to give you a sense of what was a typical board that was included in our 00:05:57.020 --> 00:06:00.220 position:50% align:middle analytical sample so that it can help provide some of the context for the 00:06:00.220 --> 00:06:02.920 position:50% align:middle statistical results that we're about to cover. 00:06:02.920 --> 00:06:07.490 position:50% align:middle So generally, as you can see here on the slide, the median age of a typical program 00:06:07.490 --> 00:06:09.840 position:50% align:middle in our sample was about 23 years. 00:06:09.840 --> 00:06:14.050 position:50% align:middle The interquartile range, so that 25th to 75th percentile was 00:06:14.050 --> 00:06:17.160 position:50% align:middle about 7 to 33 years. 00:06:17.160 --> 00:06:21.420 position:50% align:middle The median enrollment capacity for a typical program was about 66 students, 00:06:21.420 --> 00:06:25.790 position:50% align:middle that also ranged from about 32 to about 123 students. 00:06:25.790 --> 00:06:29.100 position:50% align:middle And then when we got into the program outcomes, you can see that they were quite 00:06:29.100 --> 00:06:32.630 position:50% align:middle strong across the average program in our sample. 00:06:32.630 --> 00:06:35.160 position:50% align:middle So the medium graduation rate was about 70%. 00:06:35.160 --> 00:06:41.990 position:50% align:middle And the median NCLEX pass rate for first-time participants was 87%. 00:06:41.990 --> 00:06:48.870 position:50% align:middle In the study sample, overall, about 90% of all the programs that 00:06:48.870 --> 00:06:51.970 position:50% align:middle submitted information received full program approval 00:06:51.970 --> 00:06:55.150 position:50% align:middle during the analysis period. 00:06:55.150 --> 00:07:00.810 position:50% align:middle So now with a better sense of kind of the model sample, we turned to the 00:07:00.810 --> 00:07:02.650 position:50% align:middle actual specific results. 00:07:02.650 --> 00:07:07.830 position:50% align:middle And first up are mixed model results for our primary outcome going back to those 00:07:07.830 --> 00:07:10.870 position:50% align:middle two research questions that we addressed on the first slide. 00:07:10.870 --> 00:07:13.970 position:50% align:middle So if you kind of think back to that slide, the thing that we were primarily 00:07:13.970 --> 00:07:18.120 position:50% align:middle interested in with this analysis was aligning those performance indicators that 00:07:18.120 --> 00:07:21.950 position:50% align:middle ultimately could tell us something and kind of correlate with full program 00:07:21.950 --> 00:07:25.080 position:50% align:middle approval of prelicensure nursing education programs. 00:07:25.080 --> 00:07:28.920 position:50% align:middle So what you see here is a forest plot of the univariable results. 00:07:28.920 --> 00:07:32.450 position:50% align:middle The outcome in this particular instance was binary. 00:07:32.450 --> 00:07:34.650 position:50% align:middle And by that, I mean, you know, on one side of the coin, 00:07:34.650 --> 00:07:39.020 position:50% align:middle we had full program approval on the other we had not approval. 00:07:39.020 --> 00:07:43.820 position:50% align:middle So as a result of that, the estimates from these models were 00:07:43.820 --> 00:07:48.250 position:50% align:middle ultimately reported as odds ratio estimates and confidence intervals. 00:07:48.250 --> 00:07:51.550 position:50% align:middle So again, we really thought that that allowed for greater access and 00:07:51.550 --> 00:07:54.500 position:50% align:middle understanding of the results themselves. 00:07:54.500 --> 00:07:58.120 position:50% align:middle For this particular analysis, we tested upwards of 30 performance 00:07:58.120 --> 00:08:03.210 position:50% align:middle indicators and we recognized for the purposes of our presentation setting, 00:08:03.210 --> 00:08:07.200 position:50% align:middle that it's really not super feasible and particularly when you're trying to squint 00:08:07.200 --> 00:08:11.280 position:50% align:middle at the PowerPoint slide, to get over 30 performance indicators 00:08:11.280 --> 00:08:12.490 position:50% align:middle on the slide. 00:08:12.490 --> 00:08:17.120 position:50% align:middle So what we really did is we narrowed the focus for this particular slide to just 00:08:17.120 --> 00:08:20.900 position:50% align:middle those significant results and those marginal results. 00:08:20.900 --> 00:08:24.880 position:50% align:middle Without going into too much detail, I just wanted to give you kind of a 00:08:24.880 --> 00:08:29.730 position:50% align:middle high-level overview and understanding of our full approval outcome. 00:08:29.730 --> 00:08:34.310 position:50% align:middle So in general, what you can see is that larger proportions of full-time faculty 00:08:34.310 --> 00:08:38.120 position:50% align:middle national accreditation, longer-standing program, 00:08:38.120 --> 00:08:44.140 position:50% align:middle so older programs, high NCLEX pass rates, and administering multiple program sites 00:08:44.140 --> 00:08:49.570 position:50% align:middle all emerged as significant drivers of full program approval in our analysis. 00:08:49.570 --> 00:08:55.750 position:50% align:middle Conversely, exclusively online program formats, lower enrollment 00:08:55.750 --> 00:09:02.260 position:50% align:middle capacity programs, really...and then the private for-profit program status all 00:09:02.260 --> 00:09:06.110 position:50% align:middle emerged as significant barriers in our analysis. 00:09:06.110 --> 00:09:10.040 position:50% align:middle So one of the things that I'll go ahead and do here is I'll highlight for you, 00:09:10.040 --> 00:09:14.390 position:50% align:middle essentially the performance indicators that ultimately emerged as significant 00:09:14.390 --> 00:09:16.520 position:50% align:middle drivers or barriers in our analysis. 00:09:16.520 --> 00:09:20.240 position:50% align:middle But as I did mention, we also put the marginal results on this 00:09:20.240 --> 00:09:24.520 position:50% align:middle slide so that you could get a better sense of really what are the types of metrics, 00:09:24.520 --> 00:09:27.700 position:50% align:middle what are the types of characteristics that are important to keep in mind. 00:09:27.700 --> 00:09:31.780 position:50% align:middle Because with a sample this large, those marginal findings will still inform 00:09:31.780 --> 00:09:35.950 position:50% align:middle on the results and the takeaways. 00:09:35.950 --> 00:09:41.150 position:50% align:middle So for our secondary outcome, you can see that these are the mixed model 00:09:41.150 --> 00:09:44.710 position:50% align:middle results for our supplemental NCLEX outcome. 00:09:44.710 --> 00:09:47.980 position:50% align:middle Again, here you see that this is a forest plot of the univariable, 00:09:47.980 --> 00:09:50.250 position:50% align:middle generalized linear mixed-effects models. 00:09:50.250 --> 00:09:54.270 position:50% align:middle And as before, without getting into too much detail, I'll just give you kind of a 00:09:54.270 --> 00:09:57.140 position:50% align:middle high-level overview of what we found. 00:09:57.140 --> 00:10:02.040 position:50% align:middle So for the drivers, we found that director credentials was important, 00:10:02.040 --> 00:10:07.400 position:50% align:middle as well as again, program age and administering multiple program sites. 00:10:07.400 --> 00:10:11.730 position:50% align:middle When we got into the barriers associated with high performance on the NCLEX, 00:10:11.730 --> 00:10:13.960 position:50% align:middle we saw a little bit more tease out. 00:10:13.960 --> 00:10:18.140 position:50% align:middle So programs that were exclusively in person or exclusively online, 00:10:18.140 --> 00:10:23.240 position:50% align:middle non-BSN programs, again, for-profit, private institutions, 00:10:23.240 --> 00:10:27.230 position:50% align:middle and director attrition, all emerged as significant barriers in our 00:10:27.230 --> 00:10:33.460 position:50% align:middle analysis when looking at our supplemental NCLEX outcome. 00:10:33.460 --> 00:10:36.670 position:50% align:middle So one of the things that I hope becomes kind of readily apparent as we go 00:10:36.670 --> 00:10:40.950 position:50% align:middle through these two slides in kind of like sequence is that there were some criteria 00:10:40.950 --> 00:10:44.880 position:50% align:middle that emerged and were consistent across the multiple outcome measures. 00:10:44.880 --> 00:10:48.990 position:50% align:middle But one of the things that we felt really validated our approach in the 00:10:48.990 --> 00:10:53.150 position:50% align:middle statistical modeling, by expanding our outcome to look 00:10:53.150 --> 00:10:56.910 position:50% align:middle beyond the NCLEX pass rates, was the fact that there were criteria that 00:10:56.910 --> 00:11:02.100 position:50% align:middle really uniquely aligned with one outcome or the other and sometimes, you know, 00:11:02.100 --> 00:11:03.490 position:50% align:middle one and not the other. 00:11:03.490 --> 00:11:07.070 position:50% align:middle So I think that that's important as one of the key takeaways from the 00:11:07.070 --> 00:11:11.190 position:50% align:middle quantitative data analysis. 00:11:11.190 --> 00:11:15.980 position:50% align:middle So again, these are the significant results for our forest plot. 00:11:15.980 --> 00:11:19.200 position:50% align:middle But as I had mentioned before with this large of a sample, please keep in mind 00:11:19.200 --> 00:11:24.280 position:50% align:middle that even the marginal findings displayed here will be archived on this presentation 00:11:24.280 --> 00:11:29.110 position:50% align:middle are important to keep in mind when digesting the results. 00:11:29.110 --> 00:11:34.520 position:50% align:middle So then, what is the typical profile of a fully approved program in our analysis 00:11:34.520 --> 00:11:36.760 position:50% align:middle based on this most recent study? 00:11:36.760 --> 00:11:38.330 position:50% align:middle I'm glad you asked. 00:11:38.330 --> 00:11:43.190 position:50% align:middle So generally, a fully approved program in our sample based on the evidence 00:11:43.190 --> 00:11:45.630 position:50% align:middle has national accreditation. 00:11:45.630 --> 00:11:50.700 position:50% align:middle They tend to offer both traditional and hybrid modalities, really where we saw the 00:11:50.700 --> 00:11:54.020 position:50% align:middle disconnect with full approval was with those programs that 00:11:54.020 --> 00:11:56.970 position:50% align:middle were exclusively online. 00:11:56.970 --> 00:11:58.860 position:50% align:middle They tend to be longer-standing programs. 00:11:58.860 --> 00:12:01.320 position:50% align:middle I don't think that this will probably come as much of a surprise. 00:12:01.320 --> 00:12:04.250 position:50% align:middle So the more established programs, the ones that have a bit of a higher 00:12:04.250 --> 00:12:11.990 position:50% align:middle program age, those tended to correlate better with full program approval. 00:12:11.990 --> 00:12:13.890 position:50% align:middle And then higher enrollment capacity. 00:12:13.890 --> 00:12:17.560 position:50% align:middle So you'll see this is one of the criteria along with two of the other criteria that 00:12:17.560 --> 00:12:20.720 position:50% align:middle we're about to list that kind of correlated a bit and begin to paint a 00:12:20.720 --> 00:12:27.660 position:50% align:middle picture of what Nancy was referencing in segment one about public institutions. 00:12:27.660 --> 00:12:32.420 position:50% align:middle Before we get to that point, though, not surprisingly, 80% or higher NCLEX 00:12:32.420 --> 00:12:36.450 position:50% align:middle first time pass rates emerged as one of the corollaries 00:12:36.450 --> 00:12:38.100 position:50% align:middle with full program approval. 00:12:38.100 --> 00:12:42.000 position:50% align:middle And then these last two criteria were the ones that I was just referencing. 00:12:42.000 --> 00:12:46.170 position:50% align:middle So those programs that administer more than one program site, 00:12:46.170 --> 00:12:50.450 position:50% align:middle as well as those programs that have public or private, not-for-profit status. 00:12:50.450 --> 00:12:54.110 position:50% align:middle So we saw kind of those administering more than one program site, 00:12:54.110 --> 00:12:59.060 position:50% align:middle higher enrollment capacity and public status as very correlated. 00:12:59.060 --> 00:13:02.140 position:50% align:middle And so that kind of paints a picture of what are the types of institutions which 00:13:02.140 --> 00:13:07.810 position:50% align:middle ultimately presented the most successful profile in our data analysis. 00:13:07.810 --> 00:13:13.240 position:50% align:middle So with that, I will conclude the quantitative data analysis section, 00:13:13.240 --> 00:13:17.910 position:50% align:middle and I'll turn the podium back over to Nancy to discuss our new annual report 00:13:17.910 --> 00:13:19.110 position:50% align:middle Core Data Template. 00:13:19.110 --> 00:13:22.240 position:50% align:middle - [Nancy] Thank you, Brendan. 00:13:22.240 --> 00:13:23.510 position:50% align:middle Isn't he great? 00:13:23.510 --> 00:13:29.070 position:50% align:middle We are so lucky that he came to NCSBN just as we were analyzing the results 00:13:29.070 --> 00:13:31.210 position:50% align:middle of the quantitative study. 00:13:31.210 --> 00:13:33.960 position:50% align:middle And he has been just wonderful to work with. 00:13:33.960 --> 00:13:37.780 position:50% align:middle And any questions about the quantitative study, he'll be able 00:13:37.780 --> 00:13:41.080 position:50% align:middle to answer very quickly. 00:13:41.080 --> 00:13:45.660 position:50% align:middle So I feel like this picture here is me. 00:13:45.660 --> 00:13:47.590 position:50% align:middle Yes, we did it. 00:13:47.590 --> 00:13:52.790 position:50% align:middle You have been asking for a long time for an annual report template and we really 00:13:52.790 --> 00:13:58.300 position:50% align:middle just didn't have the data in order to use to put on a template. 00:13:58.300 --> 00:14:03.090 position:50% align:middle You know, I know some of the workforce areas have some data templates that wasn't 00:14:03.090 --> 00:14:04.460 position:50% align:middle what the boards wanted. 00:14:04.460 --> 00:14:11.250 position:50% align:middle So this is so exciting that we finally have a template that all of you are able 00:14:11.250 --> 00:14:14.520 position:50% align:middle to use and it's really based on data. 00:14:14.520 --> 00:14:18.510 position:50% align:middle So it's really evidence-based, you can go out and collect the data 00:14:18.510 --> 00:14:19.580 position:50% align:middle from the program. 00:14:19.580 --> 00:14:25.600 position:50% align:middle And you know, being evidence-based, you'll be able to make sure that all the 00:14:25.600 --> 00:14:27.450 position:50% align:middle programs can do it. 00:14:27.450 --> 00:14:31.440 position:50% align:middle And also think of it this way, you know, Brendan talked about some of the 00:14:31.440 --> 00:14:37.300 position:50% align:middle significant data, some of the marginal findings, but also some of those findings 00:14:37.300 --> 00:14:42.180 position:50% align:middle that were somewhat potential that we could maybe use again, you know, 00:14:42.180 --> 00:14:45.660 position:50% align:middle we'll be able to put the core data template. 00:14:45.660 --> 00:14:47.540 position:50% align:middle So why is this so important? 00:14:47.540 --> 00:14:49.430 position:50% align:middle Why am I so excited? 00:14:49.430 --> 00:14:52.480 position:50% align:middle I see it as a win-win situation for all of you. 00:14:52.480 --> 00:14:58.150 position:50% align:middle First of all, and probably foremost, it can decrease your work. 00:14:58.150 --> 00:15:02.410 position:50% align:middle We can send out...and I'll show you how this would work in just a minute. 00:15:02.410 --> 00:15:07.360 position:50% align:middle A link to you to send to all your programs, you can get the results back, 00:15:07.360 --> 00:15:11.970 position:50% align:middle we can analyze the descriptive data to you, and send you the reports. 00:15:11.970 --> 00:15:14.900 position:50% align:middle Definitely decreases your workload. 00:15:14.900 --> 00:15:20.970 position:50% align:middle But also it provides consistency to national data that we'll be seeing, 00:15:20.970 --> 00:15:24.370 position:50% align:middle it'll help to build a national database. 00:15:24.370 --> 00:15:27.400 position:50% align:middle And I think, you know, as Brendan was talking, 00:15:27.400 --> 00:15:29.070 position:50% align:middle you can see the importance of that. 00:15:29.070 --> 00:15:35.920 position:50% align:middle If we all collect the same data, the same type of areas of the data, 00:15:35.920 --> 00:15:40.540 position:50% align:middle then we'll be able to maybe find out some more significant findings, right, Brendan? 00:15:40.540 --> 00:15:44.750 position:50% align:middle We'll be able to, you know, see for sure is it the hybrid, 00:15:44.750 --> 00:15:47.380 position:50% align:middle the online programs that are better? 00:15:47.380 --> 00:15:51.080 position:50% align:middle And we really weren't quite sure, based on our findings, 00:15:51.080 --> 00:15:54.240 position:50% align:middle and then also looking at the literature. 00:15:54.240 --> 00:15:59.190 position:50% align:middle So what does that core data template look like? 00:15:59.190 --> 00:16:04.290 position:50% align:middle Well, first of all, it's 50 questions, so we kept it to core data. 00:16:04.290 --> 00:16:09.220 position:50% align:middle I'm going to just highlight some of the areas that the questions are in. 00:16:09.220 --> 00:16:15.690 position:50% align:middle The areas would be certainly what is the approval status of the program? 00:16:15.690 --> 00:16:17.950 position:50% align:middle And what is that accreditation status? 00:16:17.950 --> 00:16:22.440 position:50% align:middle You found from the literature or quantitative data accreditation is 00:16:22.440 --> 00:16:23.760 position:50% align:middle really very important. 00:16:23.760 --> 00:16:29.380 position:50% align:middle And then the ownership is it for-profit, nonprofit, public. 00:16:29.380 --> 00:16:33.200 position:50% align:middle Remember, that came out strongly in the quantitative, as well 00:16:33.200 --> 00:16:37.280 position:50% align:middle as in the literature, some of the national studies. 00:16:37.280 --> 00:16:41.780 position:50% align:middle The age of the program, again, a long-standing program, 00:16:41.780 --> 00:16:45.810 position:50% align:middle both in the ODA [SP] Marion [SP] national study, as well 00:16:45.810 --> 00:16:48.360 position:50% align:middle as in our quantitative studies. 00:16:48.360 --> 00:16:53.490 position:50% align:middle And then the learning modality, you know, we've wondered for a long time is 00:16:53.490 --> 00:16:58.480 position:50% align:middle it hybrid, is it online, is it traditional which is it? 00:16:58.480 --> 00:17:03.930 position:50% align:middle A lot of studies point to hybrid, and a lot of studies point away from only 00:17:03.930 --> 00:17:06.790 position:50% align:middle online but we really don't have that answer. 00:17:06.790 --> 00:17:14.450 position:50% align:middle So we're thinking if we collect more data on it we'll be able to have more answers. 00:17:14.450 --> 00:17:19.910 position:50% align:middle Then looking at student services, you will see when we go through the site 00:17:19.910 --> 00:17:23.110 position:50% align:middle visit study, this is going to be very important. 00:17:23.110 --> 00:17:28.630 position:50% align:middle The offering of student services is important to the program in terms 00:17:28.630 --> 00:17:32.840 position:50% align:middle of learning disabilities do they have services for that? 00:17:32.840 --> 00:17:36.460 position:50% align:middle Do they have services for English as a second language? 00:17:36.460 --> 00:17:38.110 position:50% align:middle And remediation. 00:17:38.110 --> 00:17:43.160 position:50% align:middle Remember, this is not only remediation if they have programs for students that are 00:17:43.160 --> 00:17:48.640 position:50% align:middle in trouble, but this is also remediation if they have errors or near misses 00:17:48.640 --> 00:17:49.760 position:50% align:middle in the clinical. 00:17:49.760 --> 00:17:54.390 position:50% align:middle And remember, this is something that we regulators really care a lot about, 00:17:54.390 --> 00:17:56.670 position:50% align:middle so this is really important. 00:17:56.670 --> 00:18:00.920 position:50% align:middle And then have there been any major organizational changes? 00:18:00.920 --> 00:18:04.840 position:50% align:middle Organizational changes have been linked...And again, you'll see this on the 00:18:04.840 --> 00:18:06.320 position:50% align:middle site visit study. 00:18:06.320 --> 00:18:12.920 position:50% align:middle To a program down the line, usually, if it's a major change failing in one 00:18:12.920 --> 00:18:13.680 position:50% align:middle to three years. 00:18:13.680 --> 00:18:16.140 position:50% align:middle And I'll talk to you about that in a bit, but we're going to be 00:18:16.140 --> 00:18:17.970 position:50% align:middle collecting that data. 00:18:17.970 --> 00:18:20.990 position:50% align:middle And then, of course, clinical experiences. 00:18:20.990 --> 00:18:25.960 position:50% align:middle Quality clinical experiences came out time and time again on all these studies. 00:18:25.960 --> 00:18:31.360 position:50% align:middle So we'll be collecting data on numbers of hours in simulation, 00:18:31.360 --> 00:18:36.920 position:50% align:middle in clinical and maybe in a skills lab. 00:18:36.920 --> 00:18:42.480 position:50% align:middle You know, I have thought of those of you that know me, a lot of you do by now that 00:18:42.480 --> 00:18:45.930 position:50% align:middle it really isn't the numbers of hours, that's important, but instead, 00:18:45.930 --> 00:18:48.810 position:50% align:middle it's the quality of the clinical experiences. 00:18:48.810 --> 00:18:52.500 position:50% align:middle But now we'll really have data to see if that's the case. 00:18:52.500 --> 00:18:57.280 position:50% align:middle I know some boards do have minimum hours, maybe there is a minimum point that you 00:18:57.280 --> 00:18:58.790 position:50% align:middle need to set things at. 00:18:58.790 --> 00:19:05.360 position:50% align:middle But with consistent data across all the boards, we can have that information. 00:19:05.360 --> 00:19:10.630 position:50% align:middle Then on the core data template, we'll look at the simulation lab and 00:19:10.630 --> 00:19:11.710 position:50% align:middle is it accredited? 00:19:11.710 --> 00:19:15.220 position:50% align:middle Are the faculty certified in the simulation lab? 00:19:15.220 --> 00:19:23.280 position:50% align:middle You know, I talked a little bit to Jan about this, are the programs going to be 00:19:23.280 --> 00:19:24.560 position:50% align:middle having to do this? 00:19:24.560 --> 00:19:28.770 position:50% align:middle Not really, this is something maybe that they could strive toward. 00:19:28.770 --> 00:19:34.490 position:50% align:middle But as Jan said, they would use our guidelines for simulation at least. 00:19:34.490 --> 00:19:37.260 position:50% align:middle So this is something that you could look at. 00:19:37.260 --> 00:19:43.340 position:50% align:middle And you know, hopefully down the line, many of the programs will be accredited 00:19:43.340 --> 00:19:47.930 position:50% align:middle and the faculty be certified. 00:19:47.930 --> 00:19:53.650 position:50% align:middle So again, we're going to be collecting data on the program director, you saw, 00:19:53.650 --> 00:19:58.250 position:50% align:middle time and time again, how that came out very strongly. 00:19:58.250 --> 00:20:02.900 position:50% align:middle You'll see in the site, visit, study being a nurse is very important. 00:20:02.900 --> 00:20:06.610 position:50% align:middle What is the highest degree of the program director? 00:20:06.610 --> 00:20:11.440 position:50% align:middle We found in the quantitative data, it was a doctoral degree and that's 00:20:11.440 --> 00:20:13.400 position:50% align:middle in RN programs. 00:20:13.400 --> 00:20:16.290 position:50% align:middle It would be a graduate degree NPM programs. 00:20:16.290 --> 00:20:19.210 position:50% align:middle And then turnover of directors. 00:20:19.210 --> 00:20:22.640 position:50% align:middle Time and time again, we have seen this in the literature, 00:20:22.640 --> 00:20:27.020 position:50% align:middle the Delphi, and now the quantitative study, and you'll see it on the 00:20:27.020 --> 00:20:29.590 position:50% align:middle site visit study. 00:20:29.590 --> 00:20:34.920 position:50% align:middle If they're a director of allied health as well, this can divide their time. 00:20:34.920 --> 00:20:39.550 position:50% align:middle If they are, do they have an associate director that takes care of the day to day 00:20:39.550 --> 00:20:42.090 position:50% align:middle operations of the nursing program? 00:20:42.090 --> 00:20:49.890 position:50% align:middle So again, really important data that we can look at. 00:20:49.890 --> 00:20:54.180 position:50% align:middle Next, on the core data template, we look at faculty. 00:20:54.180 --> 00:20:58.750 position:50% align:middle Now you probably remember how important faculty are in approval of nursing 00:20:58.750 --> 00:21:02.220 position:50% align:middle programs and in programs that are doing well. 00:21:02.220 --> 00:21:06.490 position:50% align:middle So, what is that percent of full-time faculty? 00:21:06.490 --> 00:21:11.710 position:50% align:middle You know, in the quantitative study we have found 35% of faculty 00:21:11.710 --> 00:21:13.470 position:50% align:middle should be full-time. 00:21:13.470 --> 00:21:17.790 position:50% align:middle Now, I know that sounds low, and we're hoping with more data, 00:21:17.790 --> 00:21:20.130 position:50% align:middle we'll bump that up a bit. 00:21:20.130 --> 00:21:25.320 position:50% align:middle But that does consider all adjunct faculty and all part-time faculty. 00:21:25.320 --> 00:21:31.030 position:50% align:middle If you have a big program, a lot of times, they do have a lot of adjunct faculty. 00:21:31.030 --> 00:21:39.090 position:50% align:middle And then orientation of new faculty, of your adjunct faculty we have again 00:21:39.090 --> 00:21:45.090 position:50% align:middle found in the literature and our other studies that orientation and mentorship 00:21:45.090 --> 00:21:48.030 position:50% align:middle of new faculty is really very important. 00:21:48.030 --> 00:21:53.750 position:50% align:middle So this is another area that you will see on the core data template. 00:21:53.750 --> 00:21:57.850 position:50% align:middle And then the student-faculty ratio, you know, we've always seen that in member 00:21:57.850 --> 00:22:01.940 position:50% align:middle board profiles 1 to 8, 1 to 10 in some cases, 00:22:01.940 --> 00:22:04.810 position:50% align:middle 1 to 12 does it make a difference? 00:22:04.810 --> 00:22:07.000 position:50% align:middle Now, we might be able to look at that. 00:22:07.000 --> 00:22:11.530 position:50% align:middle I've had a lot of calls and emails from faculty in the past saying, you know, 00:22:11.530 --> 00:22:12.920 position:50% align:middle "Do you have any studies on that?" 00:22:12.920 --> 00:22:16.240 position:50% align:middle And we haven't, but we would be able to see that. 00:22:16.240 --> 00:22:21.650 position:50% align:middle And then the education of faculty, we, you know, have...in the past, 00:22:21.650 --> 00:22:28.430 position:50% align:middle there hasn't been much in terms of the education of faculty it wasn't...remember 00:22:28.430 --> 00:22:30.670 position:50% align:middle in the literature, it was non-research. 00:22:30.670 --> 00:22:35.480 position:50% align:middle But we are finally finding some evidence in terms of faculty 00:22:35.480 --> 00:22:38.030 position:50% align:middle being graduate prepared. 00:22:38.030 --> 00:22:45.540 position:50% align:middle I remember I was giving a talk once and, you know, it was to a lot of program 00:22:45.540 --> 00:22:49.120 position:50% align:middle owners and the like, attorneys and the audience. 00:22:49.120 --> 00:22:52.460 position:50% align:middle And now model rules do say a graduate degree. 00:22:52.460 --> 00:22:56.080 position:50% align:middle So I was saying graduate degree and, you know, somebody stood up and said, 00:22:56.080 --> 00:23:02.590 position:50% align:middle "Well, do you have evidence that a graduate degree will make a difference? 00:23:02.590 --> 00:23:05.410 position:50% align:middle Is it a significant difference? 00:23:05.410 --> 00:23:11.400 position:50% align:middle And I started off the way, you know, well, you know, on and on. 00:23:11.400 --> 00:23:15.740 position:50% align:middle And when I got done, he said, "That was a yes or no question." 00:23:15.740 --> 00:23:18.320 position:50% align:middle And so I kind of had to say no at the time. 00:23:18.320 --> 00:23:23.500 position:50% align:middle Now, we can say yes. 00:23:23.500 --> 00:23:26.990 position:50% align:middle Looking at students, you know, some of the demographics, 00:23:26.990 --> 00:23:30.760 position:50% align:middle I'm looking at students, the numbers enrolled interestingly, 00:23:30.760 --> 00:23:33.660 position:50% align:middle in some of those studies have shown that when there are more students, 00:23:33.660 --> 00:23:35.690 position:50% align:middle the programs do better. 00:23:35.690 --> 00:23:40.530 position:50% align:middle And then looking at attrition, I know we said from the literature that 00:23:40.530 --> 00:23:44.150 position:50% align:middle that doesn't really make that much of a difference, and it's not that reliable, 00:23:44.150 --> 00:23:45.220 position:50% align:middle and it's hard to measure. 00:23:45.220 --> 00:23:50.320 position:50% align:middle And it is, but we are going to have questions about that on the core data 00:23:50.320 --> 00:23:54.430 position:50% align:middle template because we're trying to find out once and for all, does that 00:23:54.430 --> 00:23:55.450 position:50% align:middle make a difference? 00:23:55.450 --> 00:24:02.800 position:50% align:middle Now how we decided the question Brandan, Mariann, many of us got together and tried 00:24:02.800 --> 00:24:05.140 position:50% align:middle to come up with the best way. 00:24:05.140 --> 00:24:07.900 position:50% align:middle We went to the IPEDS of the U.S. 00:24:07.900 --> 00:24:13.310 position:50% align:middle Department of Education and we're simply going to ask what is your attrition rate? 00:24:13.310 --> 00:24:15.350 position:50% align:middle And then we're going to define attrition. 00:24:15.350 --> 00:24:17.210 position:50% align:middle So we'll see what we get. 00:24:17.210 --> 00:24:24.100 position:50% align:middle But again, that will be on there. 00:24:24.100 --> 00:24:31.400 position:50% align:middle So what will this form look like that, you know, we will be providing you? 00:24:31.400 --> 00:24:35.910 position:50% align:middle Again, evidence-based, I just highlighted some of the areas that 00:24:35.910 --> 00:24:36.950 position:50% align:middle will be there. 00:24:36.950 --> 00:24:41.560 position:50% align:middle It'll be very important we've heard from boards that you're able to add 00:24:41.560 --> 00:24:42.910 position:50% align:middle your own questions. 00:24:42.910 --> 00:24:47.310 position:50% align:middle We have those 50 questions they're the core questions that would be consistent 00:24:47.310 --> 00:24:51.520 position:50% align:middle across all boards, but there's probably other questions you want to add. 00:24:51.520 --> 00:24:57.240 position:50% align:middle And by the way, we are going to send it out before we build it into Qualtrics 00:24:57.240 --> 00:25:02.060 position:50% align:middle as a survey, we're going to send it out to some of the education consultants 00:25:02.060 --> 00:25:07.410 position:50% align:middle to review to make sure we, you know, have worded everything in a good way. 00:25:07.410 --> 00:25:11.150 position:50% align:middle And that there might not be something that crosses upwards that we 00:25:11.150 --> 00:25:12.110 position:50% align:middle should be including. 00:25:12.110 --> 00:25:15.530 position:50% align:middle So we will be sending that out. 00:25:15.530 --> 00:25:16.970 position:50% align:middle When should it be given? 00:25:16.970 --> 00:25:19.830 position:50% align:middle I'm also going to send out another survey and this will be to all 00:25:19.830 --> 00:25:21.650 position:50% align:middle the education consultants. 00:25:21.650 --> 00:25:24.880 position:50% align:middle When do you send out your annual reports? 00:25:24.880 --> 00:25:28.750 position:50% align:middle Generally, we've seen that it's September and January. 00:25:28.750 --> 00:25:30.640 position:50% align:middle Would we be able to do it once a year? 00:25:30.640 --> 00:25:32.440 position:50% align:middle That would certainly help us out. 00:25:32.440 --> 00:25:39.090 position:50% align:middle But maybe not, we would choose dates or a date that would best meet your needs. 00:25:39.090 --> 00:25:44.730 position:50% align:middle And then we'll put the NCSBN logo on it because it'll be coming from us, 00:25:44.730 --> 00:25:49.820 position:50% align:middle but we'll put your board of nursing logo on it too so when faculty get it, 00:25:49.820 --> 00:25:52.620 position:50% align:middle they'll know that it's coming from you. 00:25:52.620 --> 00:25:56.840 position:50% align:middle And then if you remember, we will give you a report 00:25:56.840 --> 00:25:58.690 position:50% align:middle of your descriptive data. 00:25:58.690 --> 00:26:03.360 position:50% align:middle So you know, I know you do year reports, a lot of times for your boards, 00:26:03.360 --> 00:26:06.770 position:50% align:middle it'll be a report of all of the descriptive data. 00:26:06.770 --> 00:26:14.210 position:50% align:middle And then annually, Brendan will do an annual aggregate report of all of the core 00:26:14.210 --> 00:26:16.800 position:50% align:middle data that will be statistically analyzed. 00:26:16.800 --> 00:26:19.600 position:50% align:middle So does something else come out significant? 00:26:19.600 --> 00:26:22.310 position:50% align:middle Do hybrid programs come out significant? 00:26:22.310 --> 00:26:28.880 position:50% align:middle Or should you have 600 hours as a minimum in your program? 00:26:28.880 --> 00:26:35.630 position:50% align:middle You can look at that as well as compare the aggregate data to the data that you 00:26:35.630 --> 00:26:41.360 position:50% align:middle have in your, you know, annual report. 00:26:41.360 --> 00:26:47.950 position:50% align:middle And lastly, what is really exciting is this new performance indicator that I keep 00:26:47.950 --> 00:26:55.280 position:50% align:middle alluding to you about and it is something new that we have come up with. 00:26:55.280 --> 00:27:00.830 position:50% align:middle And I think you know if you take part in the annual report data collection and 00:27:00.830 --> 00:27:04.320 position:50% align:middle you'll get this performance indicator, I think you'll be very, 00:27:04.320 --> 00:27:05.590 position:50% align:middle very happy to do that. 00:27:05.590 --> 00:27:09.930 position:50% align:middle And now I'm going to turn it back over to Brendan, and he's going to go 00:27:09.930 --> 00:27:15.840 position:50% align:middle through his computer, Googly go to show you how that will work. 00:27:15.840 --> 00:27:18.940 position:50% align:middle Thank you, Brendan. 00:27:18.940 --> 00:27:21.170 position:50% align:middle - Thank you, Nancy. 00:27:21.170 --> 00:27:25.700 position:50% align:middle And you know, this is an opportunity for me to re-enter my natural habitat so 00:27:25.700 --> 00:27:27.280 position:50% align:middle that's always nice. 00:27:27.280 --> 00:27:32.040 position:50% align:middle So let's see, I will look to share my screen a little bit so that we can leave 00:27:32.040 --> 00:27:34.090 position:50% align:middle ample time for questions at the end. 00:27:34.090 --> 00:27:36.610 position:50% align:middle But I just really want to reiterate what Nancy said. 00:27:36.610 --> 00:27:40.690 position:50% align:middle You know, as I mentioned in the presentation regarding the annual 00:27:40.690 --> 00:27:46.470 position:50% align:middle report data, we ran into many limitations given the lack of uniform tracking 00:27:46.470 --> 00:27:48.390 position:50% align:middle across jurisdictions in this analysis. 00:27:48.390 --> 00:27:52.450 position:50% align:middle So what we wanted to do a little bit just to kind of conclude segment two before we 00:27:52.450 --> 00:27:55.550 position:50% align:middle open it up to some questions, is to give you a sense of what are the 00:27:55.550 --> 00:27:59.730 position:50% align:middle benefits or what are the advantages of standardized tracking moving forward. 00:27:59.730 --> 00:28:03.240 position:50% align:middle So to do that, I'm going to go back to the data. 00:28:03.240 --> 00:28:07.640 position:50% align:middle And what you will see here is an example of an Excel document. 00:28:07.640 --> 00:28:11.010 position:50% align:middle And the first thing that I want to make completely apparent is that this is 00:28:11.010 --> 00:28:12.140 position:50% align:middle all dummy data. 00:28:12.140 --> 00:28:16.140 position:50% align:middle I have fabricated this data it's only for the purposes of today's presentation. 00:28:16.140 --> 00:28:20.670 position:50% align:middle But I wanted to give you a sense of what the data would look like if all of the 00:28:20.670 --> 00:28:23.790 position:50% align:middle boards essentially were to track the annual report data 00:28:23.790 --> 00:28:25.760 position:50% align:middle in a standardized format. 00:28:25.760 --> 00:28:29.920 position:50% align:middle So as you can see here, track the this is a form only of the core data 00:28:29.920 --> 00:28:31.840 position:50% align:middle Template that Nancy was just presenting. 00:28:31.840 --> 00:28:35.840 position:50% align:middle This only has about five or so substantive columns. 00:28:35.840 --> 00:28:39.770 position:50% align:middle Obviously, the template itself will have many, many more columns of data. 00:28:39.770 --> 00:28:43.760 position:50% align:middle But again, this is just for the purposes of facilitating today's demonstration, 00:28:43.760 --> 00:28:46.620 position:50% align:middle so willing suspension of disbelief, if you will. 00:28:46.620 --> 00:28:49.500 position:50% align:middle But what you can see here is in this column let's say we have three 00:28:49.500 --> 00:28:53.180 position:50% align:middle jurisdictions reporting into our centralized data repository. 00:28:53.180 --> 00:28:56.760 position:50% align:middle And what they're reporting on are metrics like proportion full-time faculty, 00:28:56.760 --> 00:29:00.200 position:50% align:middle number of directors in the past five years, so on and so forth. 00:29:00.200 --> 00:29:06.530 position:50% align:middle What we can do with the standardized core data is we can import that data directly 00:29:06.530 --> 00:29:08.350 position:50% align:middle into a statistical software program. 00:29:08.350 --> 00:29:13.460 position:50% align:middle And I promise that we won't rest on this view for too long I know many people can 00:29:13.460 --> 00:29:16.960 position:50% align:middle sometimes develop allergies to statistical coding. 00:29:16.960 --> 00:29:19.470 position:50% align:middle But I wanted to show you how seamless this can be. 00:29:19.470 --> 00:29:23.600 position:50% align:middle So if you were to provide your data using the standardized template, 00:29:23.600 --> 00:29:27.370 position:50% align:middle to the centralized data repository for the Qualtrics platform that Nancy's team will 00:29:27.370 --> 00:29:31.890 position:50% align:middle be administering, we can actually export the raw data and import that 00:29:31.890 --> 00:29:33.630 position:50% align:middle directly into SaaS. 00:29:33.630 --> 00:29:37.070 position:50% align:middle So this is not going to work immediately. 00:29:37.070 --> 00:29:41.520 position:50% align:middle But what I will tell you is...I can actually show you just with the code. 00:29:41.520 --> 00:29:45.490 position:50% align:middle When we import that the view is actually going to look like this. 00:29:45.490 --> 00:29:47.820 position:50% align:middle It's just going to look like the Excel it's the same thing. 00:29:47.820 --> 00:29:51.270 position:50% align:middle So the database view within the statistical software package will 00:29:51.270 --> 00:29:55.600 position:50% align:middle effectively reflect only what you have tracked in the actual 00:29:55.600 --> 00:29:57.120 position:50% align:middle Qualtrics survey platform. 00:29:57.120 --> 00:30:02.270 position:50% align:middle What we can then do though, using coding is we can start to create 00:30:02.270 --> 00:30:07.210 position:50% align:middle an algorithm that will effectively identify for us in an automated fashion 00:30:07.210 --> 00:30:13.230 position:50% align:middle what programs are effectively throwing flags for potential deficiencies. 00:30:13.230 --> 00:30:15.730 position:50% align:middle So in this instance, you can see just based on the evidence 00:30:15.730 --> 00:30:19.770 position:50% align:middle that we've run to date, we're looking specifically at program age, 00:30:19.770 --> 00:30:23.320 position:50% align:middle we're looking at NCLEX pass rates, we're looking at national accreditation, 00:30:23.320 --> 00:30:24.920 position:50% align:middle so on and so forth. 00:30:24.920 --> 00:30:29.440 position:50% align:middle And so what you're getting a sense of is we can literally with the data in a matter 00:30:29.440 --> 00:30:33.970 position:50% align:middle of seconds, import it into our software, which just happens to be SaaS. 00:30:33.970 --> 00:30:36.530 position:50% align:middle And then what we can do is we can run that data. 00:30:36.530 --> 00:30:40.330 position:50% align:middle And what we'll do is we'll be able to append information to your 00:30:40.330 --> 00:30:42.000 position:50% align:middle raw data template. 00:30:42.000 --> 00:30:46.190 position:50% align:middle So kind of just cutting straight to the chase so that we can allow a few more 00:30:46.190 --> 00:30:47.620 position:50% align:middle minutes for questions. 00:30:47.620 --> 00:30:51.470 position:50% align:middle What we can do is in addition to the timely delivery of all the raw data 00:30:51.470 --> 00:30:54.560 position:50% align:middle elements that you're used to, to all the information that you've 00:30:54.560 --> 00:31:00.340 position:50% align:middle historically had access to, we can append to that raw data any 00:31:00.340 --> 00:31:01.320 position:50% align:middle criteria that we want. 00:31:01.320 --> 00:31:05.690 position:50% align:middle So in this particular instance, we have just kept it at the high level. 00:31:05.690 --> 00:31:11.030 position:50% align:middle So if you look back at the coding, we have defined as our performance 00:31:11.030 --> 00:31:16.330 position:50% align:middle indicator tier, a one being essentially a low-risk program. 00:31:16.330 --> 00:31:19.720 position:50% align:middle So that's a program that based on our statistical results, 00:31:19.720 --> 00:31:23.200 position:50% align:middle doesn't appear to have any potential deficiencies. 00:31:23.200 --> 00:31:28.300 position:50% align:middle A two or medium risk program falls into that category where the program might have 00:31:28.300 --> 00:31:31.470 position:50% align:middle evidence of one or two programmatic deficiencies. 00:31:31.470 --> 00:31:35.430 position:50% align:middle And then three, that high-risk tier is currently defined as anything 00:31:35.430 --> 00:31:36.840 position:50% align:middle greater than two. 00:31:36.840 --> 00:31:40.230 position:50% align:middle Again, I just did this for the purposes of today's presentation. 00:31:40.230 --> 00:31:43.850 position:50% align:middle The real takeaway here is that none of this coding is static. 00:31:43.850 --> 00:31:48.010 position:50% align:middle This can be updated, it is very flexible, it is evidence-based, 00:31:48.010 --> 00:31:52.330 position:50% align:middle and it's something that we would actually look to update quite regularly so that you 00:31:52.330 --> 00:31:56.750 position:50% align:middle are working with the most evidence-based information possible. 00:31:56.750 --> 00:32:00.790 position:50% align:middle What we then did is we took that one, two, three coding, and all we did is we put it 00:32:00.790 --> 00:32:04.840 position:50% align:middle into human speak, we said one equals low-risk, two equals medium, 00:32:04.840 --> 00:32:07.500 position:50% align:middle and three equals high. 00:32:07.500 --> 00:32:10.240 position:50% align:middle Then when we export the data, you can see that that's what 00:32:10.240 --> 00:32:11.440 position:50% align:middle you would see. 00:32:11.440 --> 00:32:14.840 position:50% align:middle If you determine at the board level that you want to see those individual 00:32:14.840 --> 00:32:19.950 position:50% align:middle characteristics and where it was exactly based on the evidence that it was, 00:32:19.950 --> 00:32:22.760 position:50% align:middle you know, throwing a flag, we can retain these 00:32:22.760 --> 00:32:24.350 position:50% align:middle additional columns too. 00:32:24.350 --> 00:32:29.850 position:50% align:middle So those columns will come in as a simple binary one or zero column, and it'll say, 00:32:29.850 --> 00:32:33.140 position:50% align:middle was there an issue based on the longevity of the program? 00:32:33.140 --> 00:32:36.830 position:50% align:middle Was there an issue based on the NCLEX, pass rate, etc. 00:32:36.830 --> 00:32:40.030 position:50% align:middle But for the purposes of the presentation, we just wanted to show you how clean 00:32:40.030 --> 00:32:41.250 position:50% align:middle this could look. 00:32:41.250 --> 00:32:44.960 position:50% align:middle So in this particular view, all you're seeing is essentially the 00:32:44.960 --> 00:32:48.920 position:50% align:middle information related to all of your raw data and then with this extra 00:32:48.920 --> 00:32:50.670 position:50% align:middle bit of criteria. 00:32:50.670 --> 00:32:55.150 position:50% align:middle And our thinking was that you know, personnel at the nursing regulatory boards 00:32:55.150 --> 00:32:59.060 position:50% align:middle are very busy and often have limited resources to really dive into the 00:32:59.060 --> 00:33:03.090 position:50% align:middle information and we thought to ourselves, what would be the most appropriate 00:33:03.090 --> 00:33:08.340 position:50% align:middle mechanism for giving you that high level, that 50,000-foot view to really 00:33:08.340 --> 00:33:13.020 position:50% align:middle proactively conduct outreach with some of these programs that you might think 00:33:13.020 --> 00:33:14.700 position:50% align:middle warrant further attention. 00:33:14.700 --> 00:33:18.570 position:50% align:middle So in this particular instance, I would show you in SaaS, 00:33:18.570 --> 00:33:23.040 position:50% align:middle but what I can do is, conversely, I can actually show you what this would 00:33:23.040 --> 00:33:26.770 position:50% align:middle look like a little bit, just using a pivot table. 00:33:26.770 --> 00:33:30.810 position:50% align:middle So in this particular instance, you can just do essentially, 00:33:30.810 --> 00:33:34.160 position:50% align:middle the state by the performance indicator. 00:33:34.160 --> 00:33:37.470 position:50% align:middle And then in this instance, we're just going to do a simple count. 00:33:37.470 --> 00:33:41.660 position:50% align:middle So you can see that for instance, for Illinois, for example, 00:33:41.660 --> 00:33:46.060 position:50% align:middle there are four programs in our data set in this demonstration data set, 00:33:46.060 --> 00:33:50.190 position:50% align:middle three of which would be identified based on our current statistical model 00:33:50.190 --> 00:33:53.740 position:50% align:middle as medium risk institutions. 00:33:53.740 --> 00:33:56.020 position:50% align:middle When you look at Indiana, this is where things get a little 00:33:56.020 --> 00:33:56.850 position:50% align:middle bit more interesting. 00:33:56.850 --> 00:33:59.090 position:50% align:middle And again, this is all made up no cause for alarm. 00:33:59.090 --> 00:34:03.820 position:50% align:middle There are four programs associated with Indiana as a jurisdiction, 00:34:03.820 --> 00:34:09.000 position:50% align:middle of those two kind of raise the flag a little bit one would be that medium risk. 00:34:09.000 --> 00:34:11.840 position:50% align:middle But now we get into the interesting scenario where one of them is a 00:34:11.840 --> 00:34:13.850 position:50% align:middle high risk program. 00:34:13.850 --> 00:34:17.660 position:50% align:middle Because we have automated all of this, because we have appended this information 00:34:17.660 --> 00:34:23.940 position:50% align:middle to the raw data, we could actually export a list tailored to each jurisdiction. 00:34:23.940 --> 00:34:27.730 position:50% align:middle So you would be the only one with access to your raw data and any supplementary 00:34:27.730 --> 00:34:29.310 position:50% align:middle information that we provide. 00:34:29.310 --> 00:34:32.160 position:50% align:middle But we could tell you, these are the programs, 00:34:32.160 --> 00:34:34.990 position:50% align:middle we could name them for your purposes only. 00:34:34.990 --> 00:34:38.550 position:50% align:middle We could say these are the programs that based on the current evidence are 00:34:38.550 --> 00:34:42.840 position:50% align:middle presenting either as medium or high-risk and might warrant you know, 00:34:42.840 --> 00:34:44.820 position:50% align:middle some attention, some outreach, etc. 00:34:44.820 --> 00:34:49.850 position:50% align:middle The key here is in addition to this being very, very flexible with the coding that 00:34:49.850 --> 00:34:54.860 position:50% align:middle we would have in place and the standard data tracking across the jurisdictions, 00:34:54.860 --> 00:34:56.790 position:50% align:middle this is all at your discretion. 00:34:56.790 --> 00:35:00.930 position:50% align:middle We're not pushing anything in this particular instance we hope that you will 00:35:00.930 --> 00:35:04.110 position:50% align:middle see the advantages of some of this additional insight. 00:35:04.110 --> 00:35:07.550 position:50% align:middle But it's not meant to be prescriptive, this is meant to be informative. 00:35:07.550 --> 00:35:12.990 position:50% align:middle We're really trying to enable you in your day to day knowing all the professional 00:35:12.990 --> 00:35:15.360 position:50% align:middle responsibilities that you have to juggle. 00:35:15.360 --> 00:35:21.090 position:50% align:middle We're really trying to facilitate you doing that 50,000-foot quick overview, 00:35:21.090 --> 00:35:24.520 position:50% align:middle and then from that point, move forward as you see fit. 00:35:24.520 --> 00:35:29.710 position:50% align:middle So with that, I will open it up to any questions that you might have submitted. 00:35:29.710 --> 00:35:34.610 position:50% align:middle So I'll turn to my colleague, Joe, or actually no, sorry, I will yes, 00:35:34.610 --> 00:35:37.960 position:50% align:middle I will turn to my colleague Joe, to see if there's any questions submitted 00:35:37.960 --> 00:35:39.330 position:50% align:middle and I'll invite Nancy back to the podium. 00:35:39.330 --> 00:35:45.910 position:50% align:middle - [Joe] Hi, I just want to reiterate, the PowerPoint will be made available and 00:35:45.910 --> 00:35:50.080 position:50% align:middle also the recording of this virtual conference will be available within a 00:35:50.080 --> 00:35:52.420 position:50% align:middle couple of weeks. 00:35:52.420 --> 00:35:59.910 position:50% align:middle Nancy, there was a question based on your previous presentation before the break. 00:35:59.910 --> 00:36:05.100 position:50% align:middle You mentioned faculty experience in the last five years is this teaching clinical 00:36:05.100 --> 00:36:07.530 position:50% align:middle or working in clinical? 00:36:07.530 --> 00:36:13.980 position:50% align:middle - And we haven't really presented that data yet, but that was from the site visit 00:36:13.980 --> 00:36:17.840 position:50% align:middle study and also, you know, the five years really came from the site 00:36:17.840 --> 00:36:21.710 position:50% align:middle visit study, but it was also from the literature, the current 00:36:21.710 --> 00:36:24.400 position:50% align:middle competency of faculty. 00:36:24.400 --> 00:36:29.800 position:50% align:middle And it was those faculty who worked with students, they should have been 00:36:29.800 --> 00:36:32.970 position:50% align:middle in clinical, at least in the last five years. 00:36:32.970 --> 00:36:39.290 position:50% align:middle Now, to me, that's a long time if you're teaching, especially in this day and age, 00:36:39.290 --> 00:36:45.370 position:50% align:middle you know, I'd rather see it currency in clinical, but that was the data point that 00:36:45.370 --> 00:36:47.240 position:50% align:middle they saw in the site visit study. 00:36:47.240 --> 00:36:50.430 position:50% align:middle And remember, in the site visit study, they only looked 00:36:50.430 --> 00:36:52.500 position:50% align:middle at those failing programs. 00:36:52.500 --> 00:36:58.860 position:50% align:middle So those programs, some of the faculty were out more than five years. 00:36:58.860 --> 00:37:03.840 position:50% align:middle And it kind of relates also to what John Kavanagh from Cleveland Clinic, 00:37:03.840 --> 00:37:04.850 position:50% align:middle you know, had said. 00:37:04.850 --> 00:37:08.030 position:50% align:middle Faculty...I certainly didn't realize this. 00:37:08.030 --> 00:37:13.620 position:50% align:middle I used to teach clinical and I certainly would never go into the clinical 00:37:13.620 --> 00:37:17.770 position:50% align:middle with students if I wasn't currently prepared, but apparently that's not 00:37:17.770 --> 00:37:19.430 position:50% align:middle always the case. 00:37:19.430 --> 00:37:25.580 position:50% align:middle - Okay, the next question, one concern that can be foreseen is the 00:37:25.580 --> 00:37:29.970 position:50% align:middle issue of confidentiality, the BON/NLB believes it is important 00:37:29.970 --> 00:37:32.480 position:50% align:middle to protect nursing program information. 00:37:32.480 --> 00:37:37.920 position:50% align:middle Our BON/NLB would like to ensure that this has been taken into account. 00:37:37.920 --> 00:37:41.920 position:50% align:middle - Is this in terms of the annual report template? 00:37:41.920 --> 00:37:43.310 position:50% align:middle - Yes, that's correct. 00:37:43.310 --> 00:37:48.330 position:50% align:middle Well, one thing I'd like to say is, we did have attorneys at the table when we 00:37:48.330 --> 00:37:51.420 position:50% align:middle developed all of this. 00:37:51.420 --> 00:37:57.400 position:50% align:middle And there were questions and there were areas on the approval guidelines that we 00:37:57.400 --> 00:37:59.710 position:50% align:middle took off because of that. 00:37:59.710 --> 00:38:04.490 position:50% align:middle So from our view, I think we're pretty good. 00:38:04.490 --> 00:38:10.960 position:50% align:middle If individual boards have specifics in their rules or regulations you know, 00:38:10.960 --> 00:38:11.420 position:50% align:middle I don't know. 00:38:11.420 --> 00:38:13.090 position:50% align:middle Do you have any thoughts about that Brendan? 00:38:13.090 --> 00:38:15.790 position:50% align:middle - Yeah, the only thing that I would remind...So it's similar to the data 00:38:15.790 --> 00:38:18.060 position:50% align:middle collection for this analysis. 00:38:18.060 --> 00:38:22.720 position:50% align:middle So we would be using an encrypted database that is password-protected, 00:38:22.720 --> 00:38:26.160 position:50% align:middle and only select staff would have access to that information. 00:38:26.160 --> 00:38:28.720 position:50% align:middle In addition to that, most of the metrics...Obviously, 00:38:28.720 --> 00:38:31.830 position:50% align:middle the program will be identified at the jurisdiction level. 00:38:31.830 --> 00:38:36.210 position:50% align:middle But pretty much all of the metrics that we're looking to collect will be summary 00:38:36.210 --> 00:38:38.070 position:50% align:middle at the level of the program. 00:38:38.070 --> 00:38:41.220 position:50% align:middle So we hope that that would build in some additional safeguards in particular 00:38:41.220 --> 00:38:42.860 position:50% align:middle for any of the participants for the programs. 00:38:42.860 --> 00:38:47.410 position:50% align:middle - Thank you, Brendan I believe the question was in the collection and the 00:38:47.410 --> 00:38:48.720 position:50% align:middle housing of the data, yes, thank you. 00:38:48.720 --> 00:38:56.140 position:50% align:middle Also, someone asked, which research finding if any 00:38:56.140 --> 00:38:58.200 position:50% align:middle surprised you ? 00:38:58.200 --> 00:39:05.210 position:50% align:middle - That's an interesting question. 00:39:05.210 --> 00:39:12.090 position:50% align:middle You know, I was surprised at how strongly turnover of directors came out. 00:39:12.090 --> 00:39:15.910 position:50% align:middle I knew that that was a problem, I'd heard it over the time 00:39:15.910 --> 00:39:17.170 position:50% align:middle with boards of nursing. 00:39:17.170 --> 00:39:22.600 position:50% align:middle But it just appeared in everything, as did faculty and that 00:39:22.600 --> 00:39:24.610 position:50% align:middle really surprised me. 00:39:24.610 --> 00:39:27.320 position:50% align:middle Because I thought, well, maybe we don't have a lot 00:39:27.320 --> 00:39:30.620 position:50% align:middle of good guidelines, because we don't have the evidence. 00:39:30.620 --> 00:39:32.430 position:50% align:middle But clearly, we do have the evidence. 00:39:32.430 --> 00:39:34.110 position:50% align:middle Did anything surprise you, Brendan? 00:39:34.110 --> 00:39:39.040 position:50% align:middle - Yeah, you know, the only thing...I think a lot of the evidence that emerged 00:39:39.040 --> 00:39:42.500 position:50% align:middle from the quantitative analysis really dovetails nicely with the Delphi and some 00:39:42.500 --> 00:39:44.500 position:50% align:middle of the elements that we found in the literature view. 00:39:44.500 --> 00:39:49.040 position:50% align:middle One of the things that did kind of emerge, at least kind of in the beginning to me, 00:39:49.040 --> 00:39:50.910 position:50% align:middle was the enrollment capacity. 00:39:50.910 --> 00:39:54.290 position:50% align:middle So I kind of always think of enrollment as more of a personal touch, 00:39:54.290 --> 00:39:56.320 position:50% align:middle the smaller the program is. 00:39:56.320 --> 00:40:00.540 position:50% align:middle So when a larger enrollment capacity kind of emerged aligned with full 00:40:00.540 --> 00:40:03.350 position:50% align:middle program approved, I was a little surprised initially. 00:40:03.350 --> 00:40:07.530 position:50% align:middle But what I would say is because we did have...I'll make another plug for the 00:40:07.530 --> 00:40:08.530 position:50% align:middle core data template. 00:40:08.530 --> 00:40:12.750 position:50% align:middle Because we did have standardized data elements available to us, 00:40:12.750 --> 00:40:15.780 position:50% align:middle we could look at how that related to things like program status, 00:40:15.780 --> 00:40:19.680 position:50% align:middle whether or not it was public, for-profit, the number of programs administered. 00:40:19.680 --> 00:40:23.160 position:50% align:middle And so that really did help us out a little bit undercover some of the drivers 00:40:23.160 --> 00:40:24.290 position:50% align:middle potentially of that. 00:40:24.290 --> 00:40:27.020 position:50% align:middle And so again, it's another reason why if we had standardized data, 00:40:27.020 --> 00:40:29.040 position:50% align:middle we could assess all that simultaneously. 00:40:29.040 --> 00:40:32.560 position:50% align:middle And maybe higher enrollment capacity wouldn't come out as strongly because we 00:40:32.560 --> 00:40:34.920 position:50% align:middle would see really what took the day was the public status. 00:40:34.920 --> 00:40:38.090 position:50% align:middle - That's exactly right, or maybe public wouldn't because 00:40:38.090 --> 00:40:39.030 position:50% align:middle of something else. 00:40:39.030 --> 00:40:39.980 position:50% align:middle - Precisely. 00:40:39.980 --> 00:40:43.720 position:50% align:middle - And you know, one more thing, and you haven't heard this yet, 00:40:43.720 --> 00:40:47.390 position:50% align:middle but in the site visit study, it came out that the director should be 00:40:47.390 --> 00:40:50.870 position:50% align:middle an RN, that hadn't come out in anything else. 00:40:50.870 --> 00:40:54.270 position:50% align:middle But remember, in that study, they looked at all the low performing 00:40:54.270 --> 00:40:59.490 position:50% align:middle programs and apparently some of the programs that didn't have RNs the 00:40:59.490 --> 00:41:01.720 position:50% align:middle administration just didn't think that was important. 00:41:01.720 --> 00:41:05.010 position:50% align:middle It's not like they had looked a long time and couldn't find one, 00:41:05.010 --> 00:41:06.730 position:50% align:middle they just didn't think it was important. 00:41:06.730 --> 00:41:11.130 position:50% align:middle So I think that surprised me and it might be something...you know, 00:41:11.130 --> 00:41:14.430 position:50% align:middle we certainly put that on the core data collection, and we would look 00:41:14.430 --> 00:41:15.360 position:50% align:middle at in the future. 00:41:15.360 --> 00:41:22.050 position:50% align:middle - Thank you to verify the raw program data will be available to boards in Excel 00:41:22.050 --> 00:41:24.590 position:50% align:middle correct by both program and by state? 00:41:24.590 --> 00:41:30.810 position:50% align:middle - Yes, so essentially, program is one of the criteria that we 00:41:30.810 --> 00:41:32.030 position:50% align:middle would have in the Excel. 00:41:32.030 --> 00:41:35.180 position:50% align:middle So what we could do tailored again to each jurisdiction. 00:41:35.180 --> 00:41:39.790 position:50% align:middle So one jurisdiction will have access to another jurisdiction's data, for instance. 00:41:39.790 --> 00:41:44.450 position:50% align:middle We would not only have access to the raw data, so all the raw data that we're 00:41:44.450 --> 00:41:47.340 position:50% align:middle collecting in the template, including the program name, 00:41:47.340 --> 00:41:50.290 position:50% align:middle so that could be broken however you saw. 00:41:50.290 --> 00:41:54.540 position:50% align:middle But then, as long as we have like strong standardized data collection the majority 00:41:54.540 --> 00:41:59.180 position:50% align:middle of the boards, pursue this moving forward, then you would also have that program 00:41:59.180 --> 00:42:02.180 position:50% align:middle indicator tier information appended to it. 00:42:02.180 --> 00:42:05.150 position:50% align:middle So yes, completely confirm you would have access to all the raw data and 00:42:05.150 --> 00:42:07.020 position:50% align:middle in a timely fashion. 00:42:07.020 --> 00:42:11.420 position:50% align:middle And then, because it's so seamless internally, we would be able to put that 00:42:11.420 --> 00:42:14.040 position:50% align:middle performance indicator tier on there quite quickly and deliver that 00:42:14.040 --> 00:42:16.600 position:50% align:middle with the raw data. 00:42:16.600 --> 00:42:21.270 position:50% align:middle - Nancy, can boards elect to eliminate or not use certain questions 00:42:21.270 --> 00:42:23.890 position:50% align:middle from the annual report? 00:42:23.890 --> 00:42:29.100 position:50% align:middle - Well, it was devised as a core data annual report, you know, 00:42:29.100 --> 00:42:34.860 position:50% align:middle only 50 questions, it could be something we could take back to our panel if you 00:42:34.860 --> 00:42:37.840 position:50% align:middle really wanted to do it and there's one question you hate. 00:42:37.840 --> 00:42:43.050 position:50% align:middle But we would love to have all of the questions included, if at all possible. 00:42:43.050 --> 00:42:44.060 position:50% align:middle What do you think, Brendan? 00:42:44.060 --> 00:42:46.280 position:50% align:middle - I would just echo your comments. 00:42:46.280 --> 00:42:50.610 position:50% align:middle We view this as a core data set, so to facilitate that national-level 00:42:50.610 --> 00:42:54.730 position:50% align:middle analysis to really ultimately inform on things like the performance indicator we 00:42:54.730 --> 00:42:57.720 position:50% align:middle do need as complete information as possible. 00:42:57.720 --> 00:43:01.240 position:50% align:middle If we have gaps again, we're kind of left back to square one. 00:43:01.240 --> 00:43:03.990 position:50% align:middle What I would say though, is on the flip side, 00:43:03.990 --> 00:43:07.630 position:50% align:middle we are absolutely open to you supplementing the questionnaire if 00:43:07.630 --> 00:43:08.170 position:50% align:middle there are other... 00:43:08.170 --> 00:43:08.990 position:50% align:middle - Oh yeah, right. 00:43:08.990 --> 00:43:12.060 position:50% align:middle - If there are other metrics or elements that you think are important to collect 00:43:12.060 --> 00:43:14.440 position:50% align:middle you can certainly supplement, but we would hope at a minimum...this is 00:43:14.440 --> 00:43:17.750 position:50% align:middle like a minimum kind of standard data set. 00:43:17.750 --> 00:43:22.960 position:50% align:middle - Thank you for reiterating that several questions have come in asking if they can 00:43:22.960 --> 00:43:28.280 position:50% align:middle add additional questions, one of them including demographic data. 00:43:28.280 --> 00:43:35.620 position:50% align:middle Also, someone had a very specific question about is an accredited simulation lab, 00:43:35.620 --> 00:43:39.110 position:50% align:middle a separate process from regular nursing accreditation? 00:43:39.110 --> 00:43:46.310 position:50% align:middle - The process for accrediting simulation labs, you know, that might be one of those 00:43:46.310 --> 00:43:50.140 position:50% align:middle questions I'll have to look into and get back to all of you later. 00:43:50.140 --> 00:43:52.320 position:50% align:middle I'm not exactly sure about that. 00:43:52.320 --> 00:43:56.640 position:50% align:middle That came from certainly our national simulation study, as well as some of the 00:43:56.640 --> 00:43:59.700 position:50% align:middle other studies out there on simulation, that that's really important. 00:43:59.700 --> 00:44:01.160 position:50% align:middle I it's an axle. 00:44:01.160 --> 00:44:03.900 position:50% align:middle But let me look into that further I'm sure you don't know anything 00:44:03.900 --> 00:44:05.060 position:50% align:middle about that either, right, Brendan? 00:44:05.060 --> 00:44:05.940 position:50% align:middle - I do not. 00:44:05.940 --> 00:44:06.580 position:50% align:middle - Thank you. 00:44:06.580 --> 00:44:09.510 position:50% align:middle - So actually, Joe with that, I think we're going to have to call time 00:44:09.510 --> 00:44:11.910 position:50% align:middle may be time for one more question. 00:44:11.910 --> 00:44:12.710 position:50% align:middle - Yes. 00:44:12.710 --> 00:44:14.500 position:50% align:middle There was one question. 00:44:14.500 --> 00:44:20.560 position:50% align:middle There weren't any specific questions that were presented one person wanted to know 00:44:20.560 --> 00:44:27.360 position:50% align:middle about on-time graduation as an indicator was that included in the annual 00:44:27.360 --> 00:44:27.980 position:50% align:middle core data template. 00:44:27.980 --> 00:44:28.280 position:50% align:middle - On-time? 00:44:28.280 --> 00:44:28.950 position:50% align:middle - On-time graduation. 00:44:28.950 --> 00:44:35.130 position:50% align:middle - Oh, on-time graduation, you know, that was considered and that was 00:44:35.130 --> 00:44:36.640 position:50% align:middle definitely shown in the literature. 00:44:36.640 --> 00:44:43.590 position:50% align:middle Some had you know, just on-time and then others, such as the IPEDS the U.S. 00:44:43.590 --> 00:44:49.210 position:50% align:middle Department of Education has in six years they graduate from a four-year program. 00:44:49.210 --> 00:44:54.010 position:50% align:middle We looked at on-time graduation and I believe our question, 00:44:54.010 --> 00:44:56.250 position:50% align:middle our attrition question probably should focus on that. 00:44:56.250 --> 00:45:00.170 position:50% align:middle Because in a nursing program, it's very hard to get out of that cohort 00:45:00.170 --> 00:45:01.030 position:50% align:middle that you're in. 00:45:01.030 --> 00:45:03.360 position:50% align:middle - Yeah, and I think that's what I would echo. 00:45:03.360 --> 00:45:06.540 position:50% align:middle I think almost attrition is like the substitute for that. 00:45:06.540 --> 00:45:11.190 position:50% align:middle So I will say...I know we might have some more questions, we do have another 15 00:45:11.190 --> 00:45:14.510 position:50% align:middle minutes set aside for questions later in the presentation so we'll look 00:45:14.510 --> 00:45:15.730 position:50% align:middle to address those there. 00:45:15.730 --> 00:45:19.930 position:50% align:middle Any questions that we don't get to today, though, we will provide substantive 00:45:19.930 --> 00:45:22.110 position:50% align:middle feedback and share that with the group as well. 00:45:22.110 --> 00:45:24.700 position:50% align:middle So I know we're actually pretty much just at time. 00:45:24.700 --> 00:45:28.210 position:50% align:middle So I just wanted to say that you're free to take a break now and we'll 00:45:28.210 --> 00:45:29.580 position:50% align:middle reconvene at 2 p.m. 00:45:29.580 --> 00:45:39.000 position:50% align:middle Central Standard Time for segment three .