Summer Series: Research 101- Part One

Well, the long-awaited summer series Research 101 is finally here. I know you have heard me talking about it quite frequently. I just had to schedule some time with my guest, Chris Patty and once we sat down it flowed very nicely. 

Let me just bring you up to speed on Chris. You may remember that Chris was my guest for the 4th episode of this podcast and for the 100th episode he turned the tables and interviewed me and it's always a pleasure sitting down with Chris. But way back in the early 80’s, he started his career as a surgical technician and quickly discovered he didn’t want to work that hard, so he became an OR nurse. In the 40 years in between, he’s become a doctorally-prepared nurse leader in clinical, quality improvement, research, and education and is living his best life.

You may find this hard to believe but Chris did not need a single PowerPoint slide, he had no script, just a couple of notes scribbled on his paper because as you will find out he knows this topic like the back of his hand.

This is part one of three in the series and for part one, we started with some history of research, he plainly and succinctly defined research, told us where we can find nursing literature, and shamelessly admitted that nursing research needs an overhaul. But, I think one of the most inspiring things that Chris said is that any nurse at any level can do research. 

Now, you hear the topic of chat GPT come up frequently during our conversation because as Chris tells us he is an evangelist for Chat GPT. We will save that for another episode. For now,  let's dive into Research 101 Part One.  

Be sure to check out my CE Library on RNegade.pro because, good news, this series is available for CE’S. 

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Transcript

[00:01] Michelle: Well, the long awaited Summer Series Research 101 is finally here.

[00:06] I know you've heard me talk about it quite frequently.

[00:09] I just had to schedule some time with my guest, Chris Patty, and once we sat down, it flowed very nicely.

[00:16] Let me just bring you up to speed on Chris.

[00:19] You may remember that Chris was my guest for the fourth episode of this podcast,

[00:25] and for the 100th episode he turned the tables and interviewed me.

[00:30] And it's always a pleasure sitting down with Chris.

[00:34] But way back in the early 1980s, he started his career as a surgical technician and quickly discovered he didn't want to work that hard.

[00:44] So he became an OR nurse.

[00:47] In the 40 years in between, he's become a doctorally-prepared nurse leader in clinical quality improvement research and education and is living his best life.

[00:59] You may find this hard to believe, but Chris did not need a single PowerPoint slide.

[01:05] He had no script, just a couple of notes scribbled on his paper.

[01:10] Because as you will find out, he knows this topic like the back of his hand.

[01:15] This is part one of three in this series, and for part one we started with some history of research.

[01:22] He plainly and succinctly defined research,

[01:26] told us where we can find nursing literature, and shamelessly admitted that nursing research needs an overhaul.

[01:33] But I think one of the most inspiring things that Chris said is that any nurse at any level can do research.

[01:42] Now you hear the topic of Chat GPT come up frequently during our conversation because as Chris tells us,

[01:49] he is an evangelist for Chat GPT.

[01:53] We will save that for another episode.

[01:56] For now, let's dive into Research 101, Part 1 and be sure to check out my CE library on Renegade Pro, because good news,

[02:06] this series is available for CEs.

[02:09] That's rnegade.Pro.

[02:13] The link will be in the show notes.

[02:16] Here is Chris Patty

[02:31] Well,

[02:32] hello listeners.

[02:36] Chris Patty and I are in the closet. You guys might remember Chris was my fourth guest on this podcast. If you haven't listened to his episode, please go back and do so.

[02:49] He's a researcher,

[02:51] a DNP-prepared nurse,

[02:53] and Chris and I are here today to talk about research.

[02:59] That's his specialty. He's the expert.

[03:02] And this is for all nurses.

[03:05] And really any medical professionals that read research,

[03:13] they want to know, what am I reading? What does this mean? Is it all just a bunch of gobbledygook? Is it a bunch of numbers that I don't really care about?

[03:21] How do I know if this study is even valid?

[03:25] Why should I care about the number of participants in this study?

[03:32] So the burning questions that you want to know about research,

[03:39] I think this is going to be a several part series. I don't know how many yet,

[03:44] but that's what we're here today to talk about.

[03:48] I'm going to hand it over to Chris and he has an outline and we'll just go and see what happens.

[03:58] Chris: And I'm going to open up my ChatGPT app on my phone here because I hardly do anything now without consulting ChatGPT.

[04:11] I don't know if you've heard me talk about my evangelism of ChatGPT.

[04:17] Michelle: And now I'm telling other people that my brother is a huge believer in ChatGPT.

[04:23] Chris: I'm an evangelist, in fact, and I say that and I, the little anecdote that I share with folks is about our late nurse brother Joe.

[04:33] Michelle: Yes.

[04:34] Chris: And so probably some 25 years ago,

[04:38] the organization Joe was working for was hosting Billy Graham,

[04:42] who's passed away since then. The old man, Billy Graham, not Franklin or the grandkid, the OG Billy Graham,

[04:52] as the story is told to me by my brother,

[04:55] you know, got off the plane and Joe was there to pick him up with the limo.

[05:00] And Billy Graham introduced himself, and Joe introduced himself and Billy Graham says to him,

[05:07] nice to meet you, Joe.

[05:09] How are you with Jesus?

[05:11] Right? And I say, okay, well now that's an evangelist. I mean, he's cutting through the ****. He's getting right to the bottom line, right to the meat. And so when I meet people,

[05:22] I say, hi, my name's Chris.

[05:24] How are you doing with ChatGPT?

[05:27] This is like my approximate level of evangelism.

[05:30] Michelle: Wow.

[05:30] Chris: But but that actually brings or segues up a point that will probably get a little bit into this episode. And if not then, then later for sure. Probably every episode is how are we using the contemporary tools before us like AI to make nursing better and make nurses better and better?

[05:58] Enjoy our work and eliminate the **** that we've had to deal with for decades and centuries.

[06:07] Give the **** to the machine to deal with and then hopefully we'll be dealing with the higher order knowledge and skill applications that we're,

[06:18] you know, trained to do. We can use the, the top part of our brain for a change.

[06:24] Not just the fight or flight response.

[06:28] Right, so. So we'll probably get into that.

[06:31] Okay,

[06:33] I want to say, I want to temper expectations. I am not an expert in anything.

[06:39] I am a seeker.

[06:42] And AI helps me seek, it helps me find and seek,

[06:46] but I'm not really an expert. You know, I've been involved with nursing research for about probably 25 years now, beginning as a graduate student.

[06:56] And I taught nursing research at the undergrad and graduate level for 10 years. I've been teaching research methods to graduate medical education physicians now for about seven years.

[07:13] So I've heard a lot of questions, I've asked a lot of questions, I've solved a lot of problems, or at least not made them worse.

[07:20] So whatever expertise that confers,

[07:23] I'll claim that. But otherwise,

[07:26] if you really want an expert,

[07:29] you know, you gotta go to your chat GPT and say, who are the shining stars?

[07:35] You know, who are the, you know, the Elon Musks of nursing research?

[07:40] And then you might want to contact one of them.

[07:42] Okay,

[07:44] you could go to UC San Francisco. They got a pretty deep bench of nurse researchers over there.

[07:50] So that's a qualification.

[07:53] Michelle: Okay.

[07:54] Chris: All right.

[07:56] When I have been thinking about what I'm going to say on this talk,

[08:00] as I told you in our preparations,

[08:04] one thing I don't want to do is read the book,

[08:09] right? If you want the book read to you,

[08:12] get your favorite audiobook platform, device,

[08:16] tune it to the voice you want,

[08:18] you know, put the,

[08:20] I have the, you know, the sort of bible of nursing research, Poet and Beck.

[08:25] I happen to have the 9th edition in front of me.

[08:28] We're probably on the 12th or so addition now. Get the audiobook,

[08:35] listen to that for about 500 hours,

[08:37] you know, and then call me and tell me how you feel afterwards.

[08:41] Michelle: I think nurses are rejoicing everywhere just hearing you say that, because that's one of the things I'll say as a nurse, I really hated anybody narrating a PowerPoint presentation to me.

[08:54] Chris: Oh, those are the worst.

[08:55] Michelle: Or reading a book to me,

[08:57] it's like, just give me the book. I can read it myself. You have the slides right in front of me.

[09:02] I know how to read, just summarize.

[09:05] So I think that's great.

[09:07] Great approach.

[09:08] Chris: Thank you. I was putting on a

[09:10] We call it our research day symposium for the medical residents last Thursday night.

[09:18] And so we,

[09:19] Part of their graduation requirement for scholarship is to do something scholarly and then to give a public presentation in at least regional venue.

[09:33] So we want them to be able to, you know, to do some research or research connected activities like,

[09:40] you know, medical case reports or clinical practice guidelines to do these.

[09:45] And then we want them to be able to get in front of people and communicate,

[09:49] you know, what they've done. Right. So I think we had about 28 presenters and we have about five or six judges now, when someone puts a PowerPoint slide up.

[10:03] And our format was very brief podium presentations,

[10:07] so we limit the presenter to five slides,

[10:11] five minutes with one question from the audience, because we got 30 people to get through in three and a half hours.

[10:20] Michelle: That's a tough one.

[10:21] Chris: So it's tough, but they get a couple months to prepare, so they should maybe do it in front of a mirror or whatever. But when someone walks up with five slides and there's 850 words on the first slide, that's horrible.

[10:36] And they start reading them. I just start marking 1, 1, 1, 1, 1. Because I know what's going to happen is they're going to be getting progressively faster speaking and more anxious,

[10:51] and the thing's going to just fall flat on its face.

[10:54] Michelle: Wow.

[10:55] Chris: And the people, well, of course, have that, you know, intuitive grasp of what they're trying to say anyway. They probably don't even need the slides.

[11:04] Michelle: Right.

[11:05] Chris: But, you know, they have them there for the audience, but not for them. It's for the audience.

[11:10] Michelle: Yes.

[11:11] Chris: So I want to start out just with maybe offering a little hope and inspiration to the nurses who are listening, because maybe they have an interest that's unfulfilled in research. It's one of the things that they want to do in life that they haven't been able to get to yet because there are a lot of obstacles.

[11:31] I want to make a hole in one before I die,

[11:33] you know. Now, my grandson, when I golf with him,

[11:37] when I, every putt I make,

[11:39] he says, grandpa, you made a hole in one.

[11:42] Right? So it's like two feet out and I hit it. He goes, you made a hole in one.

[11:47] So, you know, I want to make it a real hole in one.

[11:51] So some inspiration for your listeners who may just want to get started is you can do it.

[11:59] A nurse at any level.

[12:03] And I'm talking about any level. I'm talking all the way,

[12:06] you know, from Linda Aiken,

[12:08] you know, and you can read about her A I K E N. Prominent nurse researcher, Florence Nightingale.

[12:15] Right. So contemporary Linda Aiken.

[12:18] From Linda on down to the six weekend LVN online program student.

[12:28] Okay.

[12:29] Michelle: Wow.

[12:29] Chris: Okay. So any nurse at any level can become meaningfully engaged in their own directed project.

[12:42] Right. Of course, they could join Linda Akin's project,

[12:45] you know, and if I joined Linda's project. My role would probably be something like,

[12:49] you know,

[12:50] going to Starbucks and getting the coffee and the treats for the prominent researchers, maybe carrying books and cases of water and stuff like that.

[13:02] Michelle: Advancing the PowerPoint slides.

[13:05] Chris: You can actually use your AI, use your resources. Right.

[13:10] AI can be making nurses and nursing better.

[13:17] It is making me more effective in nursing right now to a level actually that's actually shocking to some of my peers.

[13:27] Right. So now I would say a quarter of my work is just mentoring people on the use of AI to improve their effectiveness of their work and their enjoy enjoyment of their work.

[13:42] Right. And I'll give you some examples for that.

[13:45] So you can do it. You can start a study.

[13:47] You can use tools like chat GPT to say,

[13:52] oh, Chatbot, guide me,

[13:55] I am a lamb.

[13:57] I know nothing.

[13:59] I want to get started.

[14:01] I heard about this study that was done somewhere. I saw it in a magazine.

[14:07] Could it, do you think I could do it too?

[14:10] How do I get started? Right.

[14:12] So let me give you a little case study on a,

[14:16] a beginner project.

[14:18] Okay?

[14:18] Michelle: Okay.

[14:19] Chris: So everybody knows at work what a good day looks like.

[14:24] Every nurse,

[14:26] every nurse knows what a bad day looks like.

[14:29] Right. I read a study some years ago about good days and bad days and someone thought to actually,

[14:40] you know, use research methods to answer the question.

[14:44] Because research is fundamentally about answering a question.

[14:50] Right. So we will talk about the formatting of the research question in our common language and all that, but it's about answering a question with data.

[15:02] So it supposes that you have a question that's answerable by data.

[15:07] Right. That's what we call a researchable question.

[15:10] If we don't have the data,

[15:12] then we can't really ask that question.

[15:14] Right? We can ask the question, but we can't call it research.

[15:17] Michelle: Yeah.

[15:17] Chris: We have to call it something else, dreaming or whatever.

[15:20] Right. Like I'd like to know what the effect of differential planetary gravitational forces is on the ability to meet nurse patient staffing ratios. But there's no data on this. This is just,

[15:38] you know, drinking game question. Right. Okay. So back to the,

[15:43] back to where you could get started at any point in your career.

[15:48] So this study interviewed,

[15:51] sat down and interviewed just like you and I are doing.

[15:54] Some nurses and say,

[15:56] have you had good day in nursing?

[15:59] What did that look like?

[16:00] Walk me through why you were able to leave at the end of the day and remember that last Tuesday was a good day.

[16:11] So talk to me. How did it start?

[16:13] What did the middle look like,

[16:15] did they bring you free lunch from the drug company? You know, that would improve it. All the things that,

[16:21] you know that you know happened to you.

[16:24] What did you pick out and said that was a good day?

[16:28] I like to have a lot more of that. Okay, so you're going to talk to me about this and I'm going to record and I'm going to code out keywords and I'm going to group those into themes and I'm going to count the themes and I'm going to organize the.

[16:46] Well, this theme came up 18 times with 25 people.

[16:50] Right. And this theme never came up.

[16:53] Okay, so we're going to, you know, look at the data we collect and answer our question. What does a good day look like?

[17:00] Well, a good day means when I left I still had some energy,

[17:05] you know, true. Or whatever it is because all my days are good days now.

[17:10] So I don't even have bad days anymore.

[17:12] Michelle: Wow.

[17:13] Chris: But I used to, back when I worked in, you know,

[17:16] as a perioperative nurse.

[17:19] Oh, I had some bad days.

[17:22] I had a real bad day on my 30th birthday and 

[17:27] I swore at that time,

[17:30] and I've since gone back on it, that I would never come to work on my birthday again.

[17:35] Michelle: Me too.

[17:35] Chris: And I kept that for about 20 years. But then as my work became more fulfilling and easier, I go, it doesn't matter what day I work, actually I work some part of every day.

[17:47] I had a meeting at 3 o' clock in the afternoon with a resident yesterday. Saturday.

[17:52] Michelle: On a Saturday.

[17:53] Chris: On a Saturday.

[17:54] Michelle: And look, you're here doing a podcast.

[17:55] Chris: I'm doing a podcast on a Sunday. So I work some part of every day and I like that.

[18:00] So then there's the bad days. Right.

[18:03] And so the interviewers, which could be the nurse that's leading this study,

[18:08] and they said any novice student can go to a nurse and say, tell me about the bad day.

[18:15] Why? What was bad about it?

[18:18] Right. What are the things? Let's start,

[18:21] you know, at the beginning.

[18:23] You clock in at 6am

[18:26] Michelle: No, a bad day, you'd be clocking in late.

[18:28] Chris: Yeah. Okay, so.

[18:30] So things like this. And you could probably,

[18:32] you know, find this study in 10 seconds using chat GPT.

[18:38] Chat GPT is my go to search tool for anything now.

[18:44] I, when I'm searching for nursing literature,

[18:47] I don't go to Medline, I don't go to Cinahl,

[18:50] I go to Chat GPT when I'm searching for,

[18:54] you know,

[18:55] help on changing an air filter. Same thing. I don't go to YouTube. I go to chat GPT.

[19:01] Because, you know,

[19:02] I don't know if you ever sat down and tried to learn how to search Medline.

[19:07] Right. This is pretty tedious, but we gotta, we gotta talk about,

[19:11] you know, medical subject headings and we gotta talk about keywords and we gotta talk about boolean operators and parenthetical,

[19:23] you know,

[19:24] and parentheses plus,

[19:27] now no parentheses.

[19:29] That gives you one set and then it got. So you just go natural language.

[19:35] Chat GPT.

[19:36] Hey, I heard there's an article about nurses,

[19:39] you know, good days and bad days.

[19:42] Can you find some of that stuff for me?

[19:44] Ding, ding, ding, ding, ding. Okay, now it's. It. All right.

[19:47] Michelle: Okay. You're gonna have to do a whole episode on Chat GPT.

[19:51] Chris: Oh, yeah, Well, like I said, I'm an evangelist. I can't talk without talking about it. Okay, but, but you get the idea, right?

[19:59] Every nurse knows about bad days and good days.

[20:04] It's still a thing,

[20:06] right?

[20:07] What do we want to study in nursing? What are our questions that we need answers?

[20:12] What are our problems needing solved? Those should be the kind of high level directional elements that help us to figure out what to study.

[20:24] What questions do I need to have answered? What problems do I need to have solved?

[20:31] My customer has primacy.

[20:34] Right. I work because people need.

[20:37] Right. I work because there's a job to do. If there was no job to do, I wouldn't have a job either. Right. But as we know,

[20:45] without a robustly empowered and equipped workforce,

[20:53] we also can't get the work done.

[20:54] Michelle: Right?

[20:55] Chris: Right. So,

[20:56] you know, this leads me to an observation where we'll talk about, you know, intro to research. What's nursing research look like?

[21:04] I remember when I first started in nursing research,

[21:07] and this was about 25 years ago when we had our intro to nursing research in our master's program.

[21:15] And at that time,

[21:17] professors being what they are, they want to stimulate your thought. They want to change the world through their inculcation of their belief systems on you so that you can. Because they're all old,

[21:30] right? So they got to get to the young minds and they gotta, they gotta plant the seeds,

[21:36] Right. So that the young mind someday, when mature and when appropriately funded and with the appropriate time to do the research.

[21:46] Right.

[21:47] Will carry on their, their legacy, their, you know, un unrequited work.

[21:54] Michelle: Yeah.

[21:54] Chris: Okay, so.

[21:56] So let's look at nursing research right now and say, okay, what's in nursing research? What's the state of the art? What is going on?

[22:04] And I will just say, and I'm not bearish on nursing research.

[22:11] I'm not dismissive of its power,

[22:14] but it needs to be overhauled like every other thing that you can think of in this world, right?

[22:20] K through 12, education,

[22:22] public education, does it need an overhaul?

[22:25] Yes.

[22:27] Right. Health care,

[22:28] does Medicare need an overhaul?

[22:31] Right.

[22:31] Name me something of an entity that people care about, especially that's funded by taxpayers, that doesn't need an overhaul.

[22:40] So it would be.

[22:42] It would be wildly speculative,

[22:46] irresponsibly so, to believe that nursing research didn't need an overhaul.

[22:52] Okay, so here's a research design for you.

[22:56] A scoping review,

[22:58] okay? A scoping review asks the review question,

[23:02] what is the scope of literature on this topic?

[23:08] Michelle: Oh, okay, okay.

[23:09] Chris: So that would be a good. A good nursing study is a scoping review.

[23:16] Now, you have had about 18 years and about $20 million to do this, right?

[23:21] But we can, we could scope it down to,

[23:24] you know, something doable by your,

[23:27] you know, novice LVN.

[23:29] But a scoping review on nursing research would.

[23:35] Would bucketize the major,

[23:38] you know, sectors of what nurses are studying,

[23:43] right? So, for example,

[23:45] you know, there would be professionalism in there somewhere,

[23:50] and there would be education in there somewhere, the various levels.

[23:55] There would be effectiveness in there, and there would be satisfaction in there.

[24:00] And there would be,

[24:01] you know, for the advanced practice nurses,

[24:05] there would be all the technical care delivery,

[24:09] you know, of what, nurse anesthetists.

[24:13] And you've talked to many nurse anesthetists or even now, as they're sometimes calling themselves, nurse anesthesiologists,

[24:22] And so you get those buckets, right? And probably a more familiar one would be,

[24:26] be, you know, Covid. Now, no one's studying Covid anymore,

[24:30] but there's a couple hundred thousand studies out there, right, about COVID Right? And so there were many scoping reviews done on the pandemic,

[24:41] right? And so if you look at the buckets of literature on the pandemic,

[24:46] there would be epidemiological buckets, there would be virological buckets,

[24:51] there would be vaccine buckets,

[24:54] there would be

[24:56] economic impact buckets,

[24:58] there would be political,

[25:00] impact buckets, there would be all these different buckets, right?

[25:03] And then because you don't have unlimited, you know, resources to do these studies,

[25:09] you might say, well, I gotta chop this down. I'm gonna do a scoping review on vaccination in the COVID pandemic.

[25:19] Okay, what would be the buckets for that if you were going to go and do a scoping review? Well,

[25:24] you'd have,

[25:26] you know, MRNA buckets. You'd have,

[25:29] you know, the non MRNA vaccines, like the Russian, you know, vaccine and the Chinese sinovac vaccine and all those kind of things.

[25:42] Then you'd have probably,

[25:44] you know, vaccine hesitancy buckets because you'd be wanting to study vaccine hesitancy too.

[25:51] You'd have adverse reaction bucket.

[25:56] Could be, you know, political,

[25:58] you know, handling of vaccination,

[26:00] public health. So you'd have all those buckets.

[26:02] Michelle: So you're really breaking it down, right? Yeah.

[26:05] Chris: So when you get into the major, if you're talking about maybe three or four buckets of nursing research as a whole, what would you find?

[26:15] Well, you would say, okay, well, let me just, you know, do a random sample of a thousand randomly selected pieces of nursing research.

[26:26] And there would be ways you could do that, right? You could put PMID tags and cut them out and put them in a hat,

[26:36] you know, and then wrestle them up a little bit and you pull it out. 340269.

[26:41] All right, give me that PMID.

[26:43] Right. And then you'd have the, you know, the study there, however you want to randomize these things. And here's what you'd find.

[26:51] You would find that the biggest bucket of nursing research would have as its study subject how nurses feel about being nurses.

[27:05] Michelle: Wow. Really?

[27:06] Chris: These would be qualitative studies.

[27:09] And qualitative studies would collect data.

[27:14] Remember,

[27:15] ask a question, answer it with data. They would collect data on attitudes, beliefs, perceptions,

[27:23] feelings.

[27:25] And they would collect it by means of focus groups, interviews,

[27:30] surveys.

[27:32] And the meat of the thing they would be studying is how nurses feel about doing their work and how nurses feel about being nurses.

[27:42] And When I started 25 years ago, that would probably be 75% of nursing research.

[27:50] And we know the reasons for this. Right now it's probably 50% of research,

[27:57] right? Because getting better, because it's getting more toward a results focus,

[28:06] more of an outcomes focus.

[28:09] So the camera or the lens or whatever you want to call it is starting to turn outward rather than,

[28:19] rather than turn in on itself and say,

[28:21] you know,

[28:22] like I just told you, right? Good days and bad days.

[28:25] Michelle: Right.

[28:26] Chris: Right now, how many do you think, how many metallurgists or aviation engineers would be studying their good days and bad days as a research topic?

[28:38] Michelle: Well, maybe like air traffic Controllers.

[28:40] Chris: They certainly could. And they probably, they probably do. And you know how you could find.

[28:44] Michelle: Those things and bad days, right?

[28:46] Chris: Yeah, absolutely.

[28:48] I'll tell you. Yeah, real bad day.

[28:50] Well, I just helped this guy augur it into the ground and leave a smoking hole. Right,  okay.

[28:57] So probably if you went to your Chat GPT,

[29:00] which is not just about healthcare science, it's about every science, everything. And you could probably find a study that would be qualitative about good days and bad days.

[29:11] And we need that because as I hear we don't have enough air traffic controllers.

[29:17] Right. And they're, you know, I think nurses are spread thin.

[29:20] Michelle: Yeah, right.

[29:21] Chris: They must be spread like, you know, Nightingale thin.

[29:25] Michelle: Yeah, right.

[29:26] Chris: Because, you know, we get to like when we talk about nurses and the buckets, right? How we feel about doing our job.

[29:32] Michelle: Yeah.

[29:33] Chris: And then there's studies about the workforce.

[29:36] Right. So, and these are mostly descriptive studies.

[29:40] Well, how many nurses are in the workforce?

[29:42] Well, how many are in,

[29:44] you know, hospital nursing? Well, how many are in public nursing? And how many doctorally prepared nurses do we have? And you know, how many,

[29:53] you know, what's their intent to leave the profession within five years.

[29:58] Right. So you'd have another bucket on the nursing workforce.

[30:02] Right. You'd have another bucket on nursing education.

[30:06] Right. What is the, what are the best and the cheapest and the most effective ways of taking a non nurse and making them a nurse? How does that work?

[30:17] Right? How does it work in didactics? How does it work in practical training like nurse residencies? Are they worth doing?

[30:27] You know, are nurses any better?

[30:30] Right. By whatever metrics we use to say, well, that nurse is better than that nurse. Right, Right, right.

[30:36] That would be a good, a good, a good study too. You know,

[30:40] think of the person that you always call a good nurse.

[30:44] What characteristics does that person display or want?

[30:50] Well, they never look like they're busy no matter how effed up of a day they're having. Right. They always got a,

[30:58] a poised look on their face like they're,

[31:02] they can handle anything.

[31:04] I have way more capacity than you can give me. I can solve any problem here, make it look easy.

[31:10] Michelle: Right.

[31:11] Chris: All right,

[31:12] so that's just a little bit about that.

[31:15] All right, then, you know, as part of the prep of this show to, to further sort of put a, a little bit of a spotlight on the problems with nursing research, the things that need the overhaul, the one of the big problems is the same problem with any,

[31:31] any research.

[31:32] Right, research writ large as a whole.

[31:35] Okay,

[31:36] so what are the big problems with research writ large?

[31:40] Or maybe we'll just talk about research in health and health science,

[31:44] which includes all the medicine and nursing and pharmacy, all that kind of stuff.

[31:50] So one of the big problems with that, as with any other research, is non reproducibility.

[31:58] Okay?

[31:59] So the whole,

[32:00] theory about science as a pursuit is you have to be able to discover the causes for results.

[32:12] Right. And so, you know, if you look at, go to your Chat GPT and search for groundbreaking medical research.

[32:22] Michelle: I don't find much.

[32:24] Chris: Oh no, you'll find a lot of groundbreaking medical research.

[32:29] Michelle: Okay? Medical, not nursing.

[32:31] Chris: Not nursing. You will be, I will just be present. You said this before, you will be disappointed.

[32:38] Michelle: Yes.

[32:40] Chris: When you search for give me the 10 most important nursing research studies,

[32:47] Give me groundbreaking nursing research.

[32:51] Give me 10 ways where nursing research has changed nursing for the better.

[32:59] You will be very disappointed. I will just let you go do that.

[33:03] But I will tell you, I'll just give you a

[33:06] Michelle: I'm going to run it through chat GPT.

[33:07] Chris: You run it through chat GPT and you know what you're going to find? You're going to find that staffing ratio research,

[33:14] okay, has primacy.

[33:17] It's up in the top one or two or three things that, that are said to be the important discoveries of nursing research that a 4 to 1 ratio and a 5 to 1 ratio have us living in different world now.

[33:39] I wish you could all see my face.

[33:40] Michelle: I wish you could too. That was a great face.

[33:43] Chris: Right. And so, and, and yeah,

[33:46] not to take anything away from,

[33:48] because I love all nursing researchers,

[33:51] you know, but you know, just off the cuff opinion that nurses and legislators really stepped in it with politically mandated nurse to patient ratios.

[34:05] So, and I'll, you know, if we get a chance, we'll talk more about that.

[34:08] Michelle: But if you talk about that the same things are being studied, like ad nauseam.

[34:15] Chris: I'm saying that the focus is too much in the bucket of,

[34:21] you know, how we feel about being professionals, okay. And what nursing means to us. And you know, how we, how we think we could be more satisfied with our job outcomes of our,

[34:34] of our care.

[34:36] Michelle: Yes, we do talk a lot about that.

[34:38] I don't know if it was your initial,

[34:41] initial episode or if it was the one that we did, the hundredth episode where you said the problem with nurses is they only talk to other nurses.

[34:53] Chris: That is one of the problems.

[34:55] Michelle: And so we like studying each other, we like studying nurses. 

[35:03] Chris: There's like cops and judges and so, so that's not senators.

[35:08] Michelle: So that's not particular to nursing.

[35:10] Chris: No, that's what people like to talk.

[35:11] Michelle: To, talking to their own.

[35:13] Chris: Yeah, the, the flock likes to talk to its flock.

[35:16] Yeah, it's tribal. It's, it's absolutely tribal. And, and that is a,

[35:21] that is a focus of, of research,

[35:24] of healthcare research in particular.

[35:27] But probably every other type is to build interdisciplinary or interprofessional, inter research teams.

[35:34] And so this is actually codified in like NIH grant language now.

[35:41] Right. Where you can't,

[35:43] you're not going to get an NIH grant to study falls or pressure ulcers or,

[35:50] you know,

[35:51] whatever it is that nurses think they own,

[35:55] you know, in the care environment.

[35:58] Unless you have.

[36:00] Also,

[36:00] do physicians care about falls?

[36:03] Do dietitians care about falls? Do pharmacists care about falls?

[36:08] Michelle: I don't know because I haven't seen the research. Are they researching falls?

[36:12] Chris: Oh, absolutely. I mean, I can tell you that in the pharmacy literature they keep trying to tell nursing that Zolpidem, Ambien is an independent predictor of falls in elderly,

[36:28] hospitalized or community dwelling adults.

[36:33] Right. So they don't want you to give it.

[36:35] Michelle: Yeah, right.

[36:36] Chris: But if you get, if you're going to get a grant from NIH,

[36:40] which is where most of the, the biggest bucket of grant money comes from,

[36:45] you're going to have to have an interdisciplinary team.

[36:49] If the phenomenon that you're studying goes beyond one discipline.

[36:57] Chris: And there aren't any that don't. I'll just cut to the cheese.

[37:00] Michelle: So. Yeah, if you're, for example, if you're studying falls in elderly, taking Ambien,

[37:06] you're including the prescriber, which is a physician or an advanced practice nurse or a PA,

[37:13] you're involving the nurse, you're involving the pharmaceutical,

[37:18] you know, the pharmacists.

[37:20] Chris: So that's an improvement over research funding of a quarter century ago.

[37:26] Right. Where you know,

[37:28] anybody who had previously received some money could get some more money. But now, you know, there are stipulations that,

[37:37] you know, if it's something that everyone cares about, then everyone needs to be on the study team.

[37:43] Michelle: Yeah.

[37:44] Chris: Right. And now, you know, in,

[37:46] you know, in a research,

[37:49] so we call the style a methodology and you know,

[37:53] we even have going to the extreme like in community based participatory research that we have to have someone in the community that we're studying on the investigation team.

[38:08] Michelle: Okay.

[38:09] Chris: Right.

[38:10] And so if we're studying the community as the unit of analysis in the research study,

[38:17] and the community are farm laborers in the Central Valley of California, and we're going to get an NIH grant to study that, we need farm worker on study team.

[38:30] Michelle: Yeah.

[38:31] Chris: Doesn't mean that the farm worker needs to go out and collect the data in the same way that others do. But they have to advise the study team on where the community is.

[38:42] Michelle: Right.

[38:43] Chris: How the community is going to respond to your inquiries.

[38:45] Michelle: Yeah.

[38:46] Chris: What's the best way to approach a community?

[38:48] Michelle: And did you do a study like this?

[38:49] Chris: I did, as a matter of fact, yes, I did. I proudly participated in a.

[38:57] In a study of farm workers in the Central Valley of California. Two years ago,

[39:01] we published a paper on that in the Journal of Rural Health.

[39:06] It was led by a couple very experienced researchers in that domain out of UC Berkeley.

[39:14] We had about 1400 farm workers from four regions.

[39:18] I think we contributed 200 farm workers to that study.

[39:23] And it actually turned out to be the first study to ever look at long Covid in the farm worker community.

[39:34] Michelle: Wow.

[39:35] Chris: But we had an advisory board that included farm workers on the board. And some of the farm workers didn't speak English.

[39:42] But fortunately,

[39:43] All the principal investigators spoke Spanish,

[39:50] but they were part of the deal.

[39:52] Michelle: And then if you have that link to that study too, I'd like to put it in the show notes.

[39:57] Chris: Yeah, sure, will do.

[39:58] Michelle: I'll make a note.

[40:00] Chris: Since it was published as an open access publication, meaning that the researchers.

[40:08] Michelle: Free open access medical education.

[40:11] Chris: Yeah, yeah.

[40:12] Michelle: Best thing ever.

[40:13] Chris: Okay.

[40:14] Well, you know, there's also a thought that you get what you pay for.

[40:19] But when I'm talking about open access, because we'll get to this when we talk about publication.

[40:23] Michelle: Okay.

[40:24] Chris: You know, the traditional publication style was that the journal got its money from people who subscribed to the journal.

[40:34] Right. And then when you as an author submitted a paper to them,

[40:40] they paid for their processing of that article with the money that they get from their subscribers.

[40:47] Well, that's flipped now, totally. We won't get.

[40:50] Michelle: Where do they get their money?

[40:51] Chris: They get the money from the researcher,

[40:53] from the person that wants to publish the paper.

[40:56] They used to call this pay-to-publish,

[40:58] but then that had sort of a pejorative tone to it.

[41:04] And so it quickly evolved into what is now called open access publishing.

[41:10] So in the open access model, which every, almost every journal has now, and it's really probably the dominant model,

[41:18] I do a study, I write a paper, I go to the Journal, I pay him $4,000,

[41:24] and they publish my paper.

[41:26] And you go, well, ****. Well, what's in it for me? Besides, you know, you get your name on my

[41:32] I get my,

[41:33] But I got that with traditional publishing and I didn't have to pay a cent.

[41:37] Right. And so what you get is you get, you know,

[41:41] you get a little bit lighter in the wallet,

[41:44] but you also get,

[41:47] because you paid for it and you tend to retain some rights for it,

[41:53] you get to put that publication in front of everybody on the planet for free.

[42:00] So you get a much wider potential readership.

[42:04] Michelle: Yes.

[42:04] Chris: Whereas with the old method,

[42:06] the only people that read it, you had to pay to subscribe to the journal. Right.

[42:11] So people like to put it out there. It's altruistic and they like to think, well, I wrote something that anyone will read,

[42:18] and so that that's good.

[42:20] And the other thing that you get is because money always greases the wheels in any process,

[42:26] generally because you're paying,

[42:28] then things go faster and smoother.

[42:31] So the editorial, peer review, copyright, all that stuff goes quicker. And you can get your paper out into the public in three months or four months or two months sometimes, where it might take 12 or 18 in the traditional model.

[42:49] Michelle: And your paper still, you're not just paying for them to publish your paper or are you, do they do any type of review of it and make sure it's like legitimate?

[43:03] I don't know if that's the right word for publishing.

[43:06] Chris: Yes, if you pick the right journals.

[43:09] Right. So there,

[43:11] there are things called predatory journals.

[43:14] And so you want to stay away from those journals. And there are tools that you can sort,

[43:20] you know, the legitimate from the predatory. And basically,

[43:25] you know, a quick and dirty.

[43:27] Or as you know,

[43:29] our new relative Alex would say from Texas, a fast and nasty method for discerning, you know, legitimate from predatory is like, where is the journal indexed?

[43:43] You know, to whom is the journal available to?

[43:47] Right. From what database? Like so, so Medline.

[43:52] Medline has in its Medical Branch PubMed, 4,500 journals indexed.

[44:00] Okay. Probably all of those journals are probably legitimate.

[44:05] But you can go to a journal that's not indexed anywhere anybody can start. You could start your own Journal of Nursing.

[44:13] Michelle: Okay.

[44:15] Chris: And you could charge people to say, to publish open access.

[44:21] And when someone goes to your website, your journal website,

[44:24] you know, the Harris Journal of Nursing Science,

[44:28] whatever,

[44:30] and if they found that website,

[44:32] then they could go get your article for free. Right?

[44:36] But they wouldn't just happen upon it.

[44:39] With a casual search Right. They'd have to know what they were looking for.

[44:43] Michelle: Yeah.

[44:43] Chris: And so we might call that a predatory journal because predation in this sense means that you paid for readership,

[44:54] but there's no readership. It just went into a black hole and it's just sitting on somebody's webpage somewhere.

[45:02] Whereas if you pay for it to go into Medline or Cinahl,

[45:06] someone's gonna.

[45:07] Michelle: Read it or find it, you know.

[45:09] Chris: Or Eric or whatever. Then the casual person that walks in and says, good day, bad day, nursing keywords, they could find that stuff.

[45:20] Michelle: Okay.

[45:20] Chris: Yeah. So something to keep in mind.

[45:22] Michelle: So here's a question for you.

[45:24] Back to the good day, bad day nursing and Chat GPT.

[45:30] Can I take,

[45:31] hey,

[45:33] I want to learn about what good days and bad days are in nursing,

[45:39] and put that into Google.

[45:41] How is that different than putting that into Chat GPT?

[45:47] Chris: Well, it is a lot different. And I would say if you really want a good answer,

[45:53] ask Chat GPT that question. Okay.

[45:56] But I, I sort of think of it this way,

[45:59] that you know the universe of things out there that are to be known through Google and Chat GPT inquiries.

[46:09] Searching. Google. Searching Chat GPT.

[46:12] Right. I think of them as books in a library.

[46:15] Okay. So when you use Google now, I, I'll have to qualify that and say you can't use Google Straight anymore because it comes with a Google AI overlay.

[46:27] Michelle: Yeah.

[46:28] Chris: On every Google question.

[46:29] Michelle: Right.

[46:29] Chris: But it's still not like Chat GPT in that it doesn't save all of your searched conversations with the question. The answer like Chat GPT was where I can go back to something.

[46:42] I had a conversation with Chad GPT two years ago.

[46:46] Search. I know I talked about walnuts. I search walnut. There's my conversation. And I can build on it now and do that. You can't do that with a Google search.

[46:55] But, but back to the analogy.

[46:58] So Google searches for a book in the library.

[47:01] Okay.

[47:02] Chat GPT reads all the books in the library and then gives you the answer to your question based on knowledge acquired and synthesized from all the books in the library.

[47:17] Michelle: No, that's a good analogy.

[47:19] Chris: That's why it takes a lot more energy.

[47:21] Michelle: Yeah.

[47:21] Chris: That's why we're building new generation nuclear power plants the size of a van and putting them out in Sunnyvale to power Chat GPT, Google, whatever.

[47:36] That's why we're edging toward annexing the province of Alberta as the 51st state.

[47:46] And I'm not getting into anything Trumpian, but I'm saying that state,

[47:51] that Province is killing it in energy production.

[47:56] And so they are oil and gas. And the reason they're making oil and gas is,

[48:02] Is to create data centers in Alberta where it's freaking cold. Northern Alberta's arctic. Right.

[48:12] Why would you want to create a data center in the Arctic?

[48:16] Because it takes a lot of juice to cool a data center.

[48:19] Right.

[48:20] Michelle: Oh, wow.

[48:20] Chris: And if you do it in, you know, Phoenix, Arizona,

[48:24] then it's going to cost you a lot more to keep those processors cool than if you just open the window in Alberta.

[48:32] Cheap cooling.

[48:34] Right. So it takes a lot of energy.

[48:37] But ChatGPT, Grok, whatever,

[48:41] they read every book in the library and they give you the answer from that. Okay,

[48:46] Google takes it to the book.

[48:48] Michelle: Okay.

[48:49] Chris: Yeah.

[48:50] Michelle: And real quick, what's your take on Google Scholar?

[48:55] Chris: Oh, Google Scholar is good.

[48:57] Michelle: Okay.

[48:58] Chris: I mean, it's a repository. You know, when you do. Yeah, it's a repository. I use it.

[49:04] Michelle: Okay.

[49:04] Chris: It's not as user friendly as Chat GPT, but I haven't. I mean, honestly, I haven't evaluated the AI interface for Google Scholar.

[49:18] Michelle: Oh, yeah. I haven't tried it since.

[49:21] Chris: Because now you can

[49:22] Michelle: I mean, you can drill down as far as, like the studies that you want from the years, you know, you can go between 2017 and 2018.

[49:33] Chris: Well, I'll give you an example.

[49:35] I had a doc last week doing a presentation, a case report on a specific type of tumor which is called a leiomyoma.

[49:48] And you may know that a leiomyoma is a tumor type that arises from the uterus.

[49:57] Michelle: I didn't know that.

[49:58] Chris: Yeah. Okay,

[49:59] You'd suppose that it more commonly occurs in women than men,

[50:05] if you still appreciate that there's a distinction between women and men.

[50:09] So I gotta throw a little bit of that in. Okay. So anyway,

[50:13] this person did a case report on a patellar tumor in a male that when histologically examined, turned out to be a leiomyoma. Okay. So we got a little bit of uterine ectopic neoplasm in the patellar tendon.

[50:34] Michelle: Wow.

[50:35] Chris: And the person who had done the research,

[50:40] background introduction for the case report,

[50:43] done the searching,

[50:44] probably through PubMed,

[50:46] probably using two or three keywords,

[50:49] said, this is the first and only case of a leiomyoma in a male.

[50:56] Now I thought, wait a minute. When someone tells me it's the first and only,

[51:01] they must not be looking hard enough because there ain't too darn many first and only is out there anymore.

[51:08] Michelle: So then did you ask?

[51:10] Chris: So I was actually on chat GPT as she was talking.

[51:15] And my chat is called male leiomyoma literature.

[51:22] Right? And it says below is a summary. I just said, find literature on leiomyoma in males.

[51:30] And it says, below is a summary of notable cases in literature on leiomyoma occurrences in male patients.

[51:39] It gives me a long list.

[51:42] Michelle: Oh, wow. So obviously not the first and only.

[51:45] Chris: Nipple and genital leomyomas.

[51:49] Right. So a long list of,

[51:52] you know,

[51:53] the thing in males.

[51:55] Right. And I said to her, I said, oh, I'm gonna send you, she just sat down from giving her presentation.

[52:02] I said, I'm gonna, I'm gonna send you my link to my conversation on this.

[52:07] She looks at it, she looks at me and just me.

[52:11] But that's, that's the power of,

[52:14] you know, a little AI.

[52:16] Michelle: Yeah.

[52:17] Chris: AI is making me a better nurse. Evidence based practice. Something we all want to do.

[52:23] Michelle: Yes.

[52:24] Chris: Now, just like the definition of nursing,

[52:27] I would challenge any nurse. I would give them a hundred bucks if they can tell me what the NLN definition of nursing is. And I still have that. A hundred bucks.

[52:38] I carry it around with me and ask people.

[52:40] Ask people what evidence based practice is. Same hundred bucks you'll never spend it. It'll be the best. A hundred bucks you never spend. Talk to me about evidence based practice.

[52:49] What is it? What isn't it?

[52:52] That would be my question.

[52:54] So we can approach that later. But here's how it's making me a better nurse and how. Any nurse could use this with ChatGPT.

[53:01] Any nurse at any level.

[53:03] There was a guy who was a CDPH surveyor that made a big impression on me probably 20, 30 years ago. One of the things he told me is because they look at your policies,

[53:16] right? Because they think that policy drives every action.

[53:20] Right. Which would be nice if it would.

[53:22] Michelle: Right.

[53:22] Chris: But it doesn't. But anyway.

[53:24] But you still gotta have good policies,

[53:26] even though practice we know is really floppily connected, you know, to the policy.

[53:32] But anyway, policies. He says your policies are too long.

[53:36] Your policy on medication administration is 51 pages long.

[53:42] He said your policies shouldn't be any more than three pages long.

[53:47] Your policy has to be understandable and usable to your newest nurse on her worst night.

[53:58] He said, what in the hell are they going to do with a 51 page policy if they're your newest nurse on their worst night?

[54:04] They want to know how to act.

[54:06] Michelle: Well, they won't find it in three pages for medication administration.

[54:10] Chris: Well, let me tell you, okay, so maybe there has to be more. Maybe we have to break down the buckets of administration.

[54:16] Michelle: Yeah, right.

[54:17] Chris: And have more policies, but fewer. Or something like that.

[54:21] But here's how Chat GPT can help you. And here's what I do.

[54:24] So someone gives me a policy. And you could do this too, even at night, at work,

[54:28] on a bad night,

[54:30] you take your PDF version of the policy,  falls policy,

[54:35] whatever,

[54:36] and you drop it into the search field of Chat GPT.

[54:40] That's called an upload. It's safe to do even with a,

[54:44] you know, organizational policy with their name on it. Okay, we can talk about why it's safe to do.

[54:50] You drop it in there. Then you say,

[54:53] tell me the degree to which the actions that are directed in this policy. And I'm using flowery language. You don't have to be as flowery. Are based in evidence.

[55:07] Okay? And then it will actually find an evidence grading scale.

[55:13] It will use like grade or Oxford or something like that.

[55:18] If you want to use something like grade, you could, you could just say use grade.

[55:23] And it'll pick out eight policy directed actions or interventions. And it'll say,

[55:29] number one,

[55:31] you know,

[55:32] this, this is a level one,

[55:35] okay? Level one evidence is supported by multiple well designed studies.

[55:42] They'll say, this is a level five.

[55:44] It's based on a consensus opinion by two experts from,

[55:49] you know, Dubai.

[55:51] Right? So this would be considered low level evidence,

[55:54] low strength of evidence. Like stuff Chris Patty says,

[55:58] low strength of evidence, you don't want to use that for practice.

[56:02] So it gives you a little page, it gives you six things and it gives you the levels of evidence and maybe they're one through five, I don't know. Then you say

[56:13] how can I strengthen the policy to produce the highest level of evidence for every one of these interventions? What different interventions would be needed? It'll say, boom, you can turn a 5 into a 3 by doing this differently.

[56:30] Okay,

[56:31] then I can ask it. And any nurse could do this.

[56:34] Then I can ask it.

[56:35] Here's what I'm doing,

[56:37] here's what's going on on my unit.

[56:39] Is that evidence based? Is that policy directed? And it'll say, well, here's the good news.

[56:46] You're following the policy, but there's no evidence behind it.

[56:49] Or you know, you're doing what's evidence based, but it's against the policy.

[56:55] Right? It can sort that out in five minutes.

[56:58] For the most complicated policy,

[57:00] by your worst,

[57:01] I would say by your worst nurse, I don't know who your worst nurse is. But that'd be a good study too,

[57:07] because there is one.

[57:08] Right. Somewhere in that workforce of 1200 RNs is the worst nurse.

[57:13] Michelle: It sounds like the name of like a children's book.

[57:16] Chris: Yeah. And somewhere's the best nurse.

[57:18] Right. But it would be, you know, you could,

[57:21] your newest nurse on the worst night could spend three minutes and do this.

[57:27] Michelle: Okay.

[57:28] Chris: And it might help them get a little bit oriented.

[57:30] Michelle: I'm picturing night shift nurses 

[57:33] Chris: give them some to take to their

[57:37] Michelle: Unit-based counsel, unit based council, shared governance.

[57:40] Chris: Shared governance

[57:41] journal clubs, Journal club,

[57:44] you know,

[57:45] informal discussion,

[57:47] you know, team huddle. Hey guys, I don't think we're doing this right.

[57:51] I think, you know, what we've been doing is very low level strength of evidence on the hierarchy.

[57:58] Right. So we look at the hierarchy. We talked about this in our, you know, outline for the first thing we're supposed to do today.

[58:04] Look at the hierarchy of evidence.

[58:06] I'm looking at a pyramid,

[58:08] right. With seven cuts in it. Seven levels. You can Google this, put evidence pyramid and every, every.

[58:16] This should be like a tattoo on everybody's,

[58:19] you know, arm. I can see it.

[58:21] Right. So.

[58:22] Michelle: Or at least on every unit.

[58:24] Chris: So you always want to be working with level one evidence where you can find it.

[58:28] Right. This comes from systematic reviews of randomized controlled trials.

[58:35] Level 2 evidence is a single randomized controlled trial.

[58:40] Level 3 is a systematic review of correlation or observational studies.

[58:47] Michelle: So this is the best going down.

[58:49] Chris: To the, going down to the worst,

[58:51] worst, meaning the lowest, lowest strength of evidence for clinical practice.

[58:56] So you want the strength of evidence to be high.

[59:00] So this is where you want to take your policy directives from.

[59:05] And, and chat GPT can do that for you real easily.

[59:08] Michelle: Can they rewrite that policy?

[59:10] Chris: Absolutely.

[59:10] Michelle: Reflect the evidence?

[59:12] Chris: Absolutely. And they can even put your logo and colors and your brand marketing on it.

[59:18] And as you use ChatGPT as opposed to Google,

[59:23] it will develop a memory of how you use it.

[59:26] So it knows that almost everything I ask it to do,

[59:30] I want a six slide PowerPoint presentation because I need to teach it to somebody.

[59:37] Michelle: Wow.

[59:38] Chris: So it'll say, do you want the six slide PowerPoint presentation?

[59:41] Yes. Do you want me to brand this with the Kaweah Health colors and scheme of logo? Yeah, go ahead or I don't care. Right.

[59:50] Level 4 single correlational observational study.

[59:54] Now we're getting to the weak stuff.

[59:56] Michelle: It's pretty weak.

[59:57] Chris: Right.

[59:58] Level five, systematic review of descriptive or qualitative or physiologic studies,

[01:00:05] blood pressure, whatever.

[01:00:07] Level six,

[01:00:08] A single descriptive qualitative study. Physiologic study. Like good, good shift, bad shift.

[01:00:15] Oh, okay, that would be about right here.

[01:00:18] Michelle: Level four.

[01:00:19] Chris: Level six. A single qualitative study. Right. So we wouldn't want to.

[01:00:25] Michelle: I'll put that in the show notes too.

[01:00:26] Chris: Yeah, we wouldn't want to write policy based on this.

[01:00:31] Michelle: No.

[01:00:32] Chris: Right. Or make,

[01:00:33] you know, hiring decisions or workforce allocations whatever.

[01:00:38] Level 7. Here's where we get to me,

[01:00:41] right? Opinions of authorities or expert committees.

[01:00:46] Okay, now do we have five minutes where I can talk about a case study?

[01:00:49] Michelle: Sure.

[01:00:50] Chris: Okay. You might have all forgotten about this, but I don't forget.

[01:00:54] I remember when people screw me.

[01:00:59] So you remember back in the pandemic when we started masking people, particularly masking kids, was a topic that was kind of controversial.

[01:01:09] We had at least two opinions.

[01:01:12] We had two actually practice statements or directives that got picked up by our government and mandated.

[01:01:20] Right. And about masking of kids.

[01:01:23] And one came from the American Academy of Pediatrics, the AAP.

[01:01:29] The AAP, right. And this became enforceable law.

[01:01:33] Okay. The other one came from the World Health Organization.

[01:01:38] Right. And this became enforceable law. And we had these two competing forces. Right? Okay.

[01:01:45] Well, the opinion of the World Health Association Organization,

[01:01:50] which is a body that's pretty large compared to the AAP and was multinational and multidisciplinary,

[01:01:59] matter of fact, it's even led by a non medical person.

[01:02:02] Michelle: Whereas the AAP is, the AAP is.

[01:02:04] Chris: A bunch of pediatricians. They probably do all wine clubs together and it's American and all drive the same Prius.

[01:02:13] Michelle: Right.

[01:02:14] Chris: And I kid my pediatrician friends,

[01:02:16] Michelle: We love our pediatrician friends.

[01:02:17] Chris: Because I have pediatrician friends that I love.

[01:02:19] But anyway, here's what happened.

[01:02:21] So the World Health Organization took the approach,

[01:02:26] let's find the highest level of evidence that we can to make this recommendation.

[01:02:31] So they actually looked at the evidence.

[01:02:33] Now there's crappy evidence to support or refute masking in children to prevent disease,

[01:02:42] transmission and infection in a pandemic.

[01:02:45] So there's no good research on it. Not like you find with Lipitor and cholesterol. Sure, it's not that kind of connection,

[01:02:52] but what they did, they looked at the evidence and they said, okay, you know what?

[01:02:56] There not the evidence that we like to be able to say mask kids at this age.

[01:03:03] But what there was,

[01:03:05] some pretty good evidence that,

[01:03:08] and this maybe wasn't well understood at the time, unless you're a parent,

[01:03:13] that you can actually do harm to kids by masking them.

[01:03:16] Particularly if they have communicative disorders.

[01:03:20] Michelle: Yes.

[01:03:21] Chris: Right. If they're autistic or they have speech problems or comprehension problems and you put a mask on the other person.

[01:03:28] Michelle: Yeah.

[01:03:29] Chris: And now they can't see the face and they, you know, this becomes a real problem for them. So anyway,

[01:03:34] When they had their fights and the African dude fought with the Swiss dude and the chick from Norway fought with the guy from,

[01:03:43] you know, Swaziland or whatever, and they worked it all out,

[01:03:46] they said,

[01:03:47] here's what the evidence supports below 6 years old,

[01:03:51] don't put a mask on a kid,

[01:03:54] they won't wear it. Right.

[01:03:55] And it could harm them.

[01:03:57] And for anybody that you're calling a minor,

[01:04:00] like 18 and under,

[01:04:02] if they have any kind of communication disorder,

[01:04:05] developmental delay,

[01:04:07] never put a mask on. Never.

[01:04:09] I don't care how bad you think you're going to get Covid,

[01:04:13] don't put a mask on.

[01:04:15] Michelle: And then what did we do and.

[01:04:16] Chris: What did the AAP do?

[01:04:17] Michelle: They said, mask everyone.

[01:04:19] Chris: So the AAP took the approach that the best thing we can all do is agree.

[01:04:25] Agree on something.

[01:04:27] Okay? We call that group think.

[01:04:29] They also call it a consensus opinion.

[01:04:32] So they went straight to the bottom level of that pyramid, to the level seven. Right. And they even said it in their recommendation committee, the expert committee. Yes, they even said it.

[01:04:43] This is a consensus opinion. It's not based on evidence.

[01:04:48] Michelle: Okay.

[01:04:48] Chris: But our friendly government picked it up and codified it as law.

[01:04:55] And my little 2 year old grandson was wearing a Paw Patrol mask.

[01:05:01] Okay?

[01:05:02] And I'm going to tell you, I didn't do anything but get that mask dirty. That's all it did.

[01:05:09] Now, it was cute.

[01:05:10] Michelle: And he still got Covid. And he spread Covid.

[01:05:12] Chris: And he still got Covid and he spread Covid. Because what 2 year old is going to practice mask hygiene? 

[01:05:19] Michelle: A lot of adults don't practice mask.

[01:05:21] Chris: I wore a mask every day for 20 years, all day. Because I was an operating room nurse. Right and I would say we had reasonable mask hygiene.

[01:05:30] Michelle: You probably had pretty good.

[01:05:31] Chris: We did not walk around with a mask below your nose, you know, untied, flopping in the breeze.

[01:05:38] We didn't wear the same mask, but we violated all of that stuff.

[01:05:43] And then we even expected a two year old,

[01:05:47] you know,

[01:05:48] to observe that. So anyway,

[01:05:50] that's a great case study. Yeah, that's a, that's a good case study.

[01:05:54] All right, so,

[01:05:55] so how are we doing. Where do we want to go now?

[01:05:58] Michelle: So I think this was a really good like review of why we do research.

[01:06:05] Definitely an introduction into AI and research.

[01:06:10] And we want to talk more about that.

[01:06:12] Chris: Talked a little bit about how we're going to go to like research protocols, institutional review boards and ethical considerations.

[01:06:22] Michelle: Okay.

[01:06:22] Chris: Because to get any piece of research done

[01:06:24] Michelle: You gotta go through all those.

[01:06:26] Chris: Hoops, you gotta go through those things before you even get to lay your hands on the first subject.

[01:06:32] Michelle: Okay.

[01:06:33] Chris: I can't even propose a nursing research study about good days and bad days and go to the nurse and ask them a question without getting an IRB approval.

[01:06:45] Michelle: Okay. Yeah, we should go there next.

[01:06:47] Chris: Right? And I will tell you that everybody that's done one piece of research in their life is always terrified of contact with an IRB because they're so bureaucratic and they waste, they just blow your time up and all that.

[01:07:02] Michelle: They're harmless.

[01:07:03] Chris: I will just pre-sage just a bit. AI helps here.

[01:07:07] Used to be that when I asked you to write the protocol, your plan to carry out that good day, bad day study,

[01:07:16] I would give you a six page template with 19 sections and I would say fill out each applicable section in about seven pages.

[01:07:26] That was the directions on top of the page.

[01:07:29] Now what I do is I say take that template,

[01:07:34] drop it into chat GPT.

[01:07:36] Okay.

[01:07:37] And now insert in chatGPT your research question and say fill out this form.

[01:07:46] Michelle: Wow.

[01:07:47] Chris: Now that brings us,

[01:07:49] that'll bring us back to one thing that we probably might want to start with next time is developing the research question in the format and using the language that we can all understand and talk to each other in.

[01:08:05] Right? So I will say,

[01:08:07] okay, if you want to fill out this seven page form,

[01:08:10] drop the blank form in there and then using the pico format for your question,

[01:08:17] state your research question.

[01:08:19] So you're going to tell me the population you're intending to study,

[01:08:23] the intervention you're intending to study,

[01:08:26] whether or not there's going to be a comparison to that intervention and what kind of outcome you're looking for,

[01:08:33] and maybe even if you want to gild that lily a little bit,

[01:08:37] what time frame we're looking at and what study design we're looking at. But you don't need to go beyond straight PICO. And I will tell you, you put that PICO in there and you hit go and it will fill the form out for you.

[01:08:50] And because it knows your PICO question,

[01:08:53] it knows that here's how you're going to address informed consent.

[01:08:57] It knows here's how you're going to address,

[01:09:00] you know, data protection.

[01:09:02] It knows here's how you're going to address risks and benefits to the.

[01:09:06] Michelle: Which is all needed before we start. Which is all needed for the IRB.

[01:09:09] Chris: Before we can start interviewing a nurse about their good day and bad day.

[01:09:14] Michelle: Yeah. So we should do that next because that's kind of in order. You have to know your question first before.

[01:09:21] So then you can take that to the IRB. This is the question I'm going to ask.

[01:09:26] Chris: And how that fits with the whole thing is remember the research is asking a question that's answerable with data.

[01:09:33] Michelle: With data.

[01:09:34] Chris: Right. And so before we get to collect those data.

[01:09:39] Got the question.

[01:09:40] Before we get to touch the people who have the data,

[01:09:44] we need approval from our IRB.

[01:09:45] Michelle: Yeah.

[01:09:46] Chris: And I'll go through a brief history of IRBs.

[01:09:48] Michelle: I did that once with, who was the research person?

[01:09:54] Chris: Lenora Cook

[01:09:55] Michelle: Lenora. Yeah, I did a very small study.

[01:09:58] Chris: Lenora taught me a lot about research.

[01:10:01] Michelle: We did a study in the NICU because so many studies have been done.

[01:10:08] The NICU seems to do the same studies over and over.

[01:10:11] At least just reading through hundreds of

[01:10:14] Chris: Because it's still a question, it's still a problem. It's still an issue.

[01:10:17] Michelle: It's still an issue.

[01:10:19] But one of the questions was do we had just implemented a cue based feeding protocol in our NICU and was this going to lead to better weight gain by discharge rather than feeding babies the traditional way every three hours, which.

[01:10:41] Chris: Is a clock based.

[01:10:43] Michelle: Yes. A certain volume.

[01:10:44] Chris: It's three o' clock time for feeding.

[01:10:46] Michelle: Yes. Whether they're showing cues or not. And so she helped me through that whole thing and it was very interesting procedure.

[01:10:55] Chris: Now you can ask ChatGPT.

[01:10:58] Michelle: But the interesting thing was my results were different. They did not show that the babies gained more weight.

[01:11:09] Chris: Well, here we have what we talked about at the beginning. The reproducibility of results problem.

[01:11:14] Michelle: Yes.

[01:11:15] Chris: Right. So half of all the research out there can't be replicated because either the methods were inappropriately and incompletely described or erroneous or the primary researcher didn't adhere to their method or the secondary researcher who tried to replicate it didn't read the methods.

[01:11:39] Or if they did, they,

[01:11:41] they didn't adhere to them.

[01:11:43] Michelle: Yeah.

[01:11:43] Chris: Or if they adhered to them, it was a bad methodology.

[01:11:47] Michelle: Yeah. And I think,

[01:11:49] I think what happened in the case of this study was the cue-based feeding regimen had only been in place not even one year before we did the study.

[01:12:05] So I personally don't think it was enough time because there were still instances of the babies being fed by traditional schedule.

[01:12:18] So it wasn't being, like you said, it wasn't being adhered to.

[01:12:22] So. Yeah, really interesting, though. It's all interesting stuff, you know,

[01:12:27] and that was just done by me like a basically,  a bedside nurse.

[01:12:32] Chris: Right. This would be called a practice innovation.

[01:12:36] Michelle: There you go.

[01:12:36] Chris: Right.

[01:12:37] Michelle: Oh, and that year I got the award for new innovation.

[01:12:43] Chris: Yeah, well, innovation, we should probably talk about innovation too, because actually, you know, that's where we're really seeing groundbreaking changes in health.

[01:12:56] Michelle: And AI is a new innovation.

[01:12:59] Chris: Right. So you think of the innovations you got probably dancing around in your head right now. You know Neuralink?

[01:13:04] Michelle: Yeah.

[01:13:05] Chris: Elon Musk's company.

[01:13:06] Michelle: Yes.

[01:13:07] Chris: People who are blind are seeing. People who are deaf are hearing, people who are paralyzed or are moving. Right. That's an innovation.

[01:13:15] Michelle: Yeah.

[01:13:16] Chris: Right. We cured some kid of deafness with a gene editing therapy.

[01:13:21] Michelle: Yes.

[01:13:22] Chris: Right. We're going to be using AI now, which is going to be owned by the drug companies. They're building their own AI. Okay.

[01:13:30] Michelle: Okay.

[01:13:31] Chris: Because their stuff is hooked to money and they don't want anybody looking at it but them.

[01:13:38] Right, but the drug companies are already in small trials with animals. Okay.

[01:13:45] They're already taking the rat genome,

[01:13:48] which we know it's all been analyzed,

[01:13:52] and they're uploading the rat genome parameters,

[01:13:56] and then they're taking the rat stomach tumor parameters and they're uploading that.

[01:14:02] And then they're saying,

[01:14:04] build me a molecule that will kill this tumor

[01:14:10] Michelle: In this rat, but not kill the rat.

[01:14:13] Chris: Yeah, it's not, it's not for rat B. It's only for rat A. Oh, because you and I want personalized concierge. No, not concierge. We're talking about DNA level.

[01:14:25] Michelle: Yeah.

[01:14:26] Chris: That the drug we make for you is for that tumor in you. It's not for any other tumor in any other person.

[01:14:34] Michelle: What's the word for that?

[01:14:36] Chris: Personalization.

[01:14:36] Michelle: No, there's a word for that.

[01:14:38] Chris: Yeah. I don't know. That's chatgpt.

[01:14:40] Michelle: It's escaping me.

[01:14:41] Chris: But we'll get into a little bit of that.

[01:14:42] Michelle: Okay, so next time we will start with developing the question.

[01:14:50] Chris: Developing the question.

[01:14:51] Michelle: And then we're going to go to

[01:14:54] Chris: Protocols, IRB and Ethics, and we'll go from there.

[01:14:58] Michelle: Okay, Sounds good. Okay, thank you, Chris.

[01:15:03] Chris: Of course. A pleasure.