Transcribed from the recorded session and lightly edited for readability. The speaker's views are his own and do not represent ARC-PA; always confirm specifics against the current ARC-PA Standards and Compliance Manual.
0:00 — Introduction: reading C1.02 beneath the surface
Okay. What are we going to try to accomplish today? This is going to be a little bit of a different type of webinar. Maybe a little bit more insightful in some areas that in the past. But we're going to look at dynamics involved with each of the questions.
There's only two. But one of the things I'm hoping to do is do a little bit of a dive beneath the surface. And I think that those those of you that have been involved with for a number of years, you know, that many things happen below the surface and programs that are impacted by multiple variables. We also are looking at the institutional role, which that that will oftentimes impact many of these components of C1.02, including workload, among other things. I'm going to briefly look at the application, how it's configured, and I'm going to kind of walk you through some examples of some data of how the narrative kind of comes together in the scope of this webinar.
I really can't get into showing you a complete narrative, but at least gives you the foundation. So obviously the C1 framework is consequential. I will say that requirements evolve. One of the things that we, all of us do at MACS is we're always looking at the Padlet. We're always attending office hours.
Some of us are going to the ARC-PA workshop or conference next month. It's it's an ever evolving process to determine that you are on the same page with ARC-PA, right? So critical analysis, you demonstrate sufficiency adequate faculty and staff. And you also surface evidence of insufficient workload in your program as well. And it's not quite that easy.
I mean FTE ratios, that's just one piece. But we're going to dive in just a bit deeper. We're also going to look at some decision making process about regarding the required data sets and how to choose wisely. So obviously I need to say that I don't work for the ARC-PA. I don't I'm not involved with the ARC-PA.
So anything I say is based on my experience in my experience alone. So that's something I have to say in every single webinar in the last four years.
2:36 — The two C1.02 questions: faculty, staff, capacity & workload
So we have two questions that we're going to address today. The first one is does the program a sufficient principal faculty. Now notice that they're in parentheses. There's no capacity and workload. We're going to dive into all three of those and how they interact with each other.
And all of those are about operating the program and fulfilling obligations. So I also want to say that, I mean, it makes perfect sense that the impact of variables and data in C100 two will spill over into C1.01. And I think I mentioned that last time during the C1.01 discussion, that if there is sufficiency issues, that's going to there's going to be impact in other places. The other piece of that is sufficient administrative staff. So I'll kind of look at how the dominoes fall when you don't have staff as well.
So let's talk about critical analysis. So for those that went through a complete review in the fifth standards, as you recall, many of the narratives in 14 were very long. They they had very iterative types of sections. And everything was about analyzing a whole ton of data and then trying to make sense of it and then trying to say, is there a triangulation point? And things are a little bit different.
So I want to I want to walk you through critical analysis and that's what you are judged or, you know, basically that's what you have to demonstrate during your visit as well as in your narrative. So things like trend analysis over time. So looking at relationships between different variables and trend analysis right. So comparisons of data points is another emphasis right. Like how does one data point interact with another.
Okay. Contextualization is also influences and contributing factors. So again it's not black and white. But it's as you look at how one variable impacts another, you realize that it may be several different components that are coming together. And you have to look at all of them to make to make your decision.
So you draw conclusions. And the interpretation basically is determine whether the program meets expectations or needs improvement. Right. And that's really the the million dollar question whether the pitfalls choosing the right combination of data. Now C1.02 is a little bit easier because there are less data points.
But you can you can actually use more data points than what is simply placed in your template. You can include up to three additional data points to make your case right. So you look at basically the, you know, the predetermined templates and then basically do it looking at a definition of what qualifies as an ANI, and I'm going to go into that some more. But it was a little bit more clear in the fifth. It was three data points or two data points must triangulate.
They must go in the same direction. Things are not quite the same now, because you have to look at synthesizing the data and saying, what does that say to me? There's a say that there's an issue that's deeper beneath the surface. That's something we're going to explore. So like I mentioned, ARC-PA does not provide the exact definition of an ANI triangulation.
We're all used to that. Right. Declining trends and precipitous drop. So if you look at a what I call a precipitous drop in outcomes like for a first-time taker pass rate dropping from 90 to 70. You got to pay attention to that.
And you say to yourself, if there's a precipitous drop in one variable, there must be some other variables that have some relationship with that. So I'm going to go over some language that is part of this of the six standards. And again
6:56 — Benchmarks, data sets & triangulation under the Sixth Edition
I think you all know this, but let's go into this from a different lens benchmark. Now in the C1.01 and C1.02 templates, you have to determine what your benchmark is for every data set in those templates, there are no strength benchmarks, and you can choose to retain strength benchmarks in your program, but they are not part of this report. Okay. Then you have data sets. And in the templates as you go through in the application, it will give you a number of choices.
In in all of them they have some required ones, but you have to choose at least three. And I've seen I've seen five data points used to to, you know, basically draw the conclusions. So it does not have to be just three. It can be 4 or 5. You might have to dive deeper.
And then data summaries. These are charts with response rates and legends. And everything goes into the C1.01 and C1.02 templates. And then the analysis process. So we are looking at data below benchmark trends over time and triangulation.
So when you analyze the data what how do you come to conclusions. So you arrive at conclusions. You know, you think about how did the program reach the conclusions in the application to to say that there is an area meeting improvement or there was none. What is this? What is the supporting evidence?
How does the data analysis support those conclusions? Any data plans you have to. In other words, you got to think of it as a thesis statement and you have to have the data to back it up. And then just kind of like three more lenses for the new assessment language. What's really been been emphasizes comparisons of data points side by side, looking at similarities.
And as you look at your three data points, or maybe your fourth or fifth data point, you start making those comparisons. Is there a similar trend? Right. Long term like again, long term direction of trends upward downward or flat. And again programs can determine what your benchmark is for trends.
It doesn't mean that it has to be below your benchmark. It might be trending down below your benchmark, but you have to decide that exactly how you're going to use that. And we talked about what triangulation is multiple times. I don't need to tell you that, but it is one element of what you're going to be using. So then arriving at conclusions and then what the supporting evidence is.
So we'll get into each of these in some detail. I think I just went forward and backward. Sorry about that. So in looking at the approach to data evaluation and diagnosis for C1.02. First you do an initial review of all the data sets, including the essential items first.
So the ones that state required look at those first okay. So you're going to analyze those and you're going to annotate some basically some some notes within those little boxes. And you can say for example, you know above benchmark for three years below benchmark for three years, something that triggers you to say, I'm going to come back to that, right, at least annotate it. And then you look at benchmark identification. This is something that has to be done well in advance.
And so if there's anything I would say is that you better have your benchmarks all put together. Now, even if you have a review three or 4 or 5 years from now, because you have to be able to track the data in real time using your benchmarks. Okay. And then the the evidence triangulation, like we talked about before, which is the same thing as the fifth. But again, you may use additional data sets to reinforce.
But also what is the root cause diagnosis. You can kind of look at quantitative qualitative data signals in the data you're trying to determine, like if there is a problem, what is the diagnosis, what is the problem. And then we're not talking about patients here, but I'm talking about what is the root cause issue that's taking place within that specific question.
11:38 — Compliance vs. an area needing improvement (ANI)
So compliance determination. So if you are lucky and no data falls below benchmark and everything looks fine and dandy copacetic. The program is in compliance. So in all likelihood in your narrative you're going to say the program analyze the data. The program did not determine that there was any benchmark.
The program triangulated the data and determined that there was no areas needing improvement. So right, you're going to you're going to defend that conclusion. They're still going to be critical assessment. But again, when you get to data below benchmark, assess whether it's truly a program level need of improvement. You're going to see what I would say a smattering of data throughout your entire C1.01 and C1.02.
And it may or may not be meaningful. Like for example, you know, is attrition of faculty meaningful? Absolutely. Okay. Is the questionnaire with the questions by itself meaningful?
It is. But if you if you take it in isolation with nothing else that reinforces it, it may not be significant. Okay. So the evidence based strategy makes sure that you follow every question, every step. There's six steps.
Make sure that you have satisfied the description of those steps and what you're going to do in terms of an action plan. So lastly I want to say that look at every question on its own terms. So I think it's hard to do with staff and faculty. But again I'm going to get into that. You know, if you have enough faculty but not enough staff, it's going to impact the faculty, right?
So there's going to be overlap no matter what. And you have to, with your program, look through your lens and decide how you contextualize that data.
13:38 — Question 1: Sufficient principal faculty?
So let's look at the question number one. You know, does the program have sufficient principal faculty. So this element really looks at if if you look at the sum of its part it's looking at evaluation of sufficiency. And we'll get into the questions and those types of things. So you're looking at you know students evaluated faculty evaluate it and staff evaluate it.
And you're looking at FTE and attrition okay. Understand that you're looking at the number of full time equivalents, vacancies, movement. Like for example, if you have an IPD in your program that's going to cause a vacant position okay. Looking at attrition rates and how that impacts with, you know, basically FTEs and then workload, understand that workload calculations. In a perfect world, you know, you have a number and you reach the number and you're 100%.
But what happens when you lose three faculty members? All of a sudden you're all in overload, most likely because you're going to have to pick up the slack. Okay? Workload calculations is a big challenge for many programs because as I'll get into, it just doesn't fit into the square box of many institutions. So the new assessment methods, the questions, I think you've all seen those, you know, are there enough faculty and then asking faculty are enough faculty to meet.
Remember teaching, research, advising and service expectations for any of you that have been in tenure track positions. And again, I'm one of them. I had to deal with not only teaching service, but I had to have a research agenda, a publishing agenda, and then also applying for advancement in rank and tenure. And when there's not enough faculty, that's really difficult to pull off. Okay.
So if you have an institution that is imposing tenure track on faculty, but not fully, I would say resourcing them, it's almost impossible to achieve. And then does the faculty size allow for a fair, balanced workload distribution? We'll get more into that. But again, there's something called capacity. I'll get into two.
And then you you ask the staff. the staff know before you even do that they see the stress on your face. They they see you running around constantly. Right. So the staff, you know, they're watching and they understand when that happens.
So this is an exemplar showing basically scores. And again this is the benchmark. This is the response rate. And so if you look at this. This does raise a few concerns in some of these areas right there below benchmark.
And so again you gather those these survey questions each year you can add more survey questions. And remember that you have the new surveys from C1.01. You may actually combine surveys and have, you know, the sufficiency component of the survey and have the faculty effectiveness part of the survey. So basically they're separated, but you're getting that information altogether. So let me talk about this a second.
17:09 — How many faculty do you actually need?
So how many faculty do you need? I think it's one of the most difficult questions to answer. Having lived this for so many years, there's no one variable that's going to answer your question. Student faculty ratios. Although we lean on that a lot, it's not going to give you the answer.
Not always okay. Because it depends on your program. It depends on your complexity and enrollment. A larger program. And I'm going to say, exponentially speaking, if you have a program of 40 students and you have X number of faculty, you can have a proportional increase in faculty if you go up to 80 students and the complexity is going to rapidly increase and impact everybody's workload.
The other one is a realistic workload policy, which sadly speaking, many institutions do not have a realistic workload policy. And then, you know, either a pedagogical model. So a lot of you like to have small group sessions, you like to break off into sections. So the question is do you get workload for that if that's the way you teach, if you're doing PBL, if you're doing case based, or are you basically are you only getting a certain amount of credit hours for a class, even though you have to break it into multiple sections, right. Administrative support, staffing, institutional support A1, A1.02.
This all factors into this, and sometimes it's just a lack of knowledge. It's a lack of understanding about why there's a problem. And and so universally another factor is do they help with admissions and also the collective faculty teaching experience. Let's say you have a faculty population of ten and all of you have more than five years. You're going to have some smooth sailing even if you lose someone.
Well, what about a faculty of seven where only three have experience and the others are being mentored? That's going to be a very fragile type of dynamic when you start losing faculty.
19:27 — The workload calculator: defining a true full FTE
What about the workload calculator? And again, I'm speaking about a perfect world here defines a full FTE. There needs to be a number that basically defines that, not just the number of credit hours you're teaching that is that is totally inadequate to be able to use. There needs to be teaching by role. So again, if you're doing unit workload units for course and labs and many of you are in labs and you're not getting enough workload credit for those labs, right?
So if only the primary instructors getting their workload for that class, then you're not getting that teaching load. What about also program operations accreditation related committee work. That's another one that needs to be recognized, as is release time for leaders having, you know, a fair release time that lowers the teaching load. I have seen programs where the program director has to teach the same number of credit hours as faculty, and that is totally inadequate, and that's going to be cited and then recognize student success work. What about students that need more attention?
What about the dynamics that are occurring around the country with more and more students struggling? What about the possibility of loading someone as a student success coordinator? That's just an example. And this is going to gives you an exemplar calculator that shows you like what some of these credit equivalents might look like. Again, just an example.
You all have different ones. But you know, I've negotiated multiple workload calculations through my career. And when the institution is enlightened, they realize that PA is not the same as sociology. They're not the same as physical therapy. So this this is some data that shows basically I'm going to come to the to the main premise of this.
But if you look at the projections, you've got a lot of people. They're on overload 120 130. That continues for a couple of years. Okay. So if you look at all these faculty on overload, why do you think that is?
Okay. I mean, there's a lot of reasons for it, but it could be because, number one, there's not enough faculty to actually teach the courses. And that's where there needs to be a number that defines being full time. Okay. But this takes a toll and this causes burnout.
And when ARC-PA looks at this they're going to ask you why are you on overload for three years.
22:16 — The institution's role: teaching loads, tenure & support
So let's go back to the institution. And I don't mean to pick on the institution, but I have to say this, that, you know, you got to ask the questions that as far as the strain within your program or if you're looking at program, are there enough faculty. And again, it has to be about other things other than teaching. Right? Teaching is one part.
But if you're going to have a productive faculty that are going to make an impact nationally, they need to be able to be involved with committees nationally. They need to be able to be involved with research posters, you know, advise students service, okay. And in programs where they're under-resourced, you see people that are siloed and they can't get there with that. Okay. The other one is how the university views teaching loads.
In my consulting work, oftentimes at the very beginning, we start with, you got to all have 15 credit hours of semester, whatever it might be, and eventually, with some education, the administration realizes that's just not going to going to work for PA, right? Sometimes it's a big it's a bit of a struggle, but you'll get there. And then I already mentioned about research requirement as far as promotion and tenure. I lived it. And, you know, again, I mean, I'm not complaining, but I spent my weekends doing research, publishing papers, doing clinical practice.
And that's just the way it was. The this is a big one in the six standards, though, is a senior administration support program assessment. It's no longer basically are they involved with it or are they overseeing it. They have to support it. And it's a big deal.
If the faculty are buried in data tabulation and collection, that means that they cannot achieve the other components. And so ministration has to look at how they're going to support it. It can't just be having somebody from IR on your committee. It might have to be financial support, additional manpower coming in. And I'll say that that's that's going to be one that's going to be looked at very carefully in regards to workload.
Okay. And I mentioned about advising student success activities. So I'm going to talk about the institutional traditions. Right. So you've got the borrowed credit expectations.
They expect you to teach the same credit load as undergraduates. That one's laughable because it's just impossible. Right. Teaching weighted models where the model that over weights teaching credits and ignores program capacity. So in other words, like I've seen workload policies where 100% of the workload policy is teaching activities, everything else is just simply ignored.
And then we have the human cost. Faculty are stretched. And if I know faculty around the country, you are working hard for your students because you care about your students. You love your students, but you just don't have enough bandwidth to get everything done. So.
So basically that you start to see the the burnout, the turnover, etc.. Okay. So I'm talking about this is that, you know, a two or a 1 or 2 be the sponsoring institutions responsible for supporting faculty in program self assessment. And again that's the balance about some of these components as well.
25:59 — Capacity, burnout & reading faculty attrition
Okay. What about capacity. This has been brought up in a lot of conversations. It's in the compliance manual on page 137 where it talks about capacity is the amount of work a person can perform at their level of effort. I mean, no one person is exactly the same.
I mean, some people have been in for ten years. They may not have the same level of capacity as someone in three, right? But they're wanting you to look at that and say, you know, does a new employee have lower capacity than a season one? And then looking at in now, you throw in new, inexperienced faculty into a heavy teaching load without resources and the ability to mentor them. And now you have disaster waiting, right.
And burnout. Okay, so this is real data modified somewhat to protect the innocent. But and looking at a program. What do you see when you see 40% principal faculty attrition and 25% staff attrition? What you see is big trouble, right?
So it depends on the program. But I have seen enough situations where I saw one situation where 70% of the principal faculty left in one year. Okay. How do you recover from that? The ripple effect will go on for 2 or 3 years.
Okay. So this is something that must warrant critical analysis. And it's going to have signal and connection to other variables. I trust me when I say that if you have that kind of attrition you've got problems. So the impact of that widespread significant attrition can have a deleterious effect on students.
And it does okay. So outcomes drop. So then you start seeing basically the connection between C1 and one. And you're trying to recruit replacements. The curriculum has to continue.
I mean you may be down 2 or 3 faculty members, but you got to teach the curriculum. And that means that you're teaching an extra class or you're bringing in an adjunct that maybe is not as well prepared, and then that class is quality. You might suffer. Okay, the remaining faculty are going to pay the price. They're going to they're going to work.
They're going to absorb that workload. Right. And so on top of that, the perception of insufficiency fall. And that will persist sometimes for two or more years until it starts to equilibrate. Okay.
So is attrition significant? It's widespread. It's widespread and long reaching. Right. The question would be is that you got to ask the question, why would the program see 40% attrition in one year?
And I will say what I've seen is oftentimes there's problems in the program for a period of time. And all of a sudden, people, the dominoes fall, right? They're burned out there, under-resourced. They administration is not listened to them about workload. And they all leave.
They can go to a different program and they can get potentially higher salaries, a better position, less work. Okay. And so this is something that happens in far too many programs around our country, under-resourced programs, the dominoes fall.
29:43 — Question 2: Sufficient administrative staff
So I'm going to go on to question number two now and which is about sufficient administrative staff. And again, there's questions there that you have to ask. And this is not the only questions that you can ask. But again you have to ask those questions annually. You know, develop your benchmark and determine basically you know, what is your benchmark.
If it's a 3.5 out of five, whatever it might be, right? Students will review it, faculty will review it, staff will review it, and then staff will review it. Are there enough faculty? So it's kind of like a to some degree it's a 360. So think about how staff protects you okay.
I mean if staff all of a sudden go away there are a lot of issues. They can protect your teaching time by avoiding clerical tasks. So all of a sudden you go from four staff members to two and now you're having a dual clerical tasks. So now that erodes your student focused teaching time. Staff can take on advanced responsibilities.
They can do data analytics. They can do administrative responsibilities within the clinical year. They may manage admissions. They may triage the students. I mean, they are they're going to be your first line of defense when students all come to your office at the same time wanting to ask you about question number 16 on your clinical medicine test.
Let's take a look at this situation. The the data was was slightly modified to protect the innocent okay. What what happens if the program director leaves? I mean, so you have a program director that leaves, and either they're lucky and somebody steps in. You have a good you have a good succession plan, IPD, or you have to go on a national search.
So again the IPD will step in. Their position will become vacated. And so now you're down a faculty member right? Then you have 30% principal faculty members that leave, and then you got 50% of the staff. Think about the impact of like I've already talked about on all aspects of program operations as well as the quality of education.
Okay. And you can see the recovery in 2026. But again, it takes at least two years for the recovery to complete. And so you're going to see low scores on these surveys the following year. And students again depending upon when you survey them, they may say two years later that they didn't have enough sufficient faculty because their experience in the program, especially if you're surveying them upon graduation, was not sufficient.
Just a few things about reviewing
32:41 — Writing the C1.02 narrative (no more Appendix 14)
the application, you know, parade no more appendix 14. I mean, I can't tell you how many times I've seen appendix 14 that have so many pages that they would probably be one relatively moderate sized tree. Right? So the narratives go into the application of record. The data is aggregated into the template.
So that's simplified. Each question is followed by predetermined data set placeholders okay. Some mandatory right. And for each question the program selects the most appropriate data sets. So again only two questions.
So not quite as complex. Perhaps unlike C1.01. Let's talk about the analysis a little bit. The analysis. And first I want to say that regardless of whether or not your data is all normal or not, you still have to analyze it with the same level of rigor.
Okay, clear logical link between the data reviewed conclusions. So you're triangulating data. You're looking at data that's above benchmark. Looking at data that's below benchmark. You have to provide enough detail to follow the program's reasoning.
One caveat is that in the application, there's these little templates that you can put information into that helps to lower your word count. By the way, if you put information in there that helps you to summarize it in the narrative, the 1500 word tickets a little easier when you do that. So the question number one, it's really about workload perceptions attrition. So again the program analyzes all data sets in the template basically. And then again multiple sources allow the program to synthesize the patterns and sufficiency.
So I've talked about some of these that are data driven. Some of them they're not some of them that are basically I'm going to call them contextualization. The mandated surveys, the benchmark comparisons. And I mentioned this already. A couple of things I want to say about this is that the purpose of the analysis to determine whether these, these all these trends, benchmarks signal concerns about sufficiency, which leads you to an action plan.
So again, it lets you contextualize your performance in. It's not quite as formulaic as the the fifth edition standards. I'll be curious to see whether there's any major Let Me See reveals at the conference next month about this. The review follows the program's established methodology. Okay.
Chosen for relevance. Right. And then they're triangulated to identify the patterns. So again, each data point gets its own analysis. And I'm just going to give you a little bit of a touch of this.
So like in your narrative you have each data point. There's a header. And then you go ahead and you put in information. This is basically some FTE attrition principle faculty measures. So you look at, you know, some FTE some student faculty ratio attrition clinical sufficiency.
So it's not just, you know, you look at all the the sufficiency that you can look at. So if you don't have enough clinical slots and maybe because there's sufficiency issues to and then national comparison and context. Right. So looking at basically the number of faculty and the cautionary note there is that the data is aging out. The is some of it's three or 4 or 5 years old.
Okay. There's only so much you can do with the average data, because it doesn't take into account how many faculty you need for your program and how many students you have. In this case, the program had 11 FTEs. And this is the inexact science like I talked about before. And this is why ARC-PA would get their dander up about, you know, overemphasizing the ratios.
I agree. I mean, you got to look at all the complexity and everything we've talked about to be able to come to those conclusions and that basically in this case, in this in this analysis, there was more faculty being hired but not included. So kind of summarizing your comparisons, trends and triangulations, really, it brings everything together. It connects the benchmarks, it connects the trends together. And then basically it makes the point whether or not there's a more systemic issue.
37:31 — Single data point or systemic issue? Declaring an ANI
What if there's a singular data point? It's probably not going to be an ANI, right? But if you see that there is a precipitous issue and you have enough data, then that probably the burden of proof goes on declaring an ANI. And I've heard these differences in opinion. I'm not going to say names, but among the commission, among the staff members, I think they're still finding their way about what, you know, like, for example, if a program declares an ANI, because of a sudden drop in PANCE rates, only has like 1 or 2 other data points, are they going to say that there wasn't three data points?
I don't believe that's the case, but we have to see, right. And then again, select the conclusion that follows from the analysis. Efficiency you know, versus area needing improvement. And so having been involved with a number of these now I have I have seen sufficiency when even when there is some some issues in the program. But it may be the issues are not adequate enough to say that that, that it's that there's a sufficiency issue.
It might be peripheral, it might not be really related. You got to look at it deeper. Right. And then these are the points you got to make the action plan and the same as the fifth standards. Make sure that you include the outcomes how it's going to be measured, who's responsible.
And then this is the application of results in all of that. So everything has to be very very specific. Make sure you fill all that out exactly the way it's stated and you should be good to go. So I'm going to finish up today with again
39:27 — The story beneath the surface: final thoughts
going back to the story beneath the surface. I think to me this is what I take away from this, you know, being in for 31 years myself and now, you know, having the, I guess, the, the honor and the ability to see inside programs in my consulting component. I see this all the time. You got to look deeper. First of all, faculty perceive sufficiency problems long before students because they work beyond sustainable levels.
So I've seen that play out right. The students will say everything's fine, and then the faculty will are saying they're having trouble. You might see a few qualitative comments like the faculty seem a little stressed, but you don't see it in the numbers. But eventually the students are going to see it, and eventually the students are going to be impacted by it because they're not going to get their needs met. Okay.
Remember that you carry complex, multifaceted responsibilities, okay. And being even a seasoned academic, okay. I mean, it took I mean, I don't know how many years it took me to become not just a PA that became a faculty member. Right? Because that's what I was.
It was a PA that became a faculty member. I think it was 5 to 7 years in where I saw myself as a faculty member, as an academic. And it wasn't until like 15 years in, but I published my first article, and at that point I was an academic, right? It takes time, and even the most seasoned academic person is going to be challenged if they're overloaded like that. Okay.
And sadly speaking, there's a growing number of programs that cannot recruit experienced faculty member because the the pool is being diluted and the number of new programs is starting to impact that. I'm not saying that there shouldn't be new programs, because we have to meet the needs of health care in this country. But I think that this is the issue that many new programs face is they get some new, some experienced people in some inexperienced people. And the question therein lies, can they provide the mentoring is necessary? That's the question.
And the sponsoring institution, if they ignore that, they can threaten the very existence of programs. So if you if you look at patterns of probation action, you can go back several years and see the same thing. If you get hit with C1.02, okay. And then you get hit with a 2.09 and then you get hit with the Cs, then you get hit with, you know, A1.07. Again, I'm speaking, I think fifth edition standards.
But what I'm saying is that institutions not supporting you, the program director unfortunately gets the brunt of that because the program director doesn't have the, the the support. Right. And then you don't have enough sufficiency. And then, of course, the SSR isn't done adequately because nobody has the time to do it. And now they have a program now that's at risk of being in an adverse adverse action.
So so it's really on the institution to if they if they ignore those warning signs, their program could either cease to exist or be have a real serious issue on their hands. Boy I got real passionate there. Okay. So my final thoughts and this is you know, for those of you in the audience, I'm going to say I'm dedicating this to you, the hardworking, committed faculty across the country. We do the work with an adequate resources and manpower.
All of you work your butts off. And I think that that's common. The challenges, you stay hidden and but again, they can only stay hidden so long. You can only compensate so long it's going to erode their students educational quality. And last thing I'm going to say is that this can be prevented.
It starts with diligent education at the top. And when we engage with the program, we start with that. We talk with the administration about this. We talk about how the administration has to view it, how they have to view sufficiency, and how much more help they need to provide the program, because they don't often know. And when they started a program, they may have been insulated by that.
So I'm not just I'm not blaming them. I'm saying they may not have the education behind it.