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Uh good afternoon, everyone. Uh and a very warm welcome to the latest webinar in our getting started series um which is put on by the incubator for clinical education researcher. So the idea of the incubator is to encourage um uh clinicians and others um to get involved in clinical education research. And there's several strands and there'll be a um a link in the chat box to find out more about the clinical, educate the, the clinical incubator and the various different webinars sessions, training courses that we do. Um We've got courses, webinars for beginners, which is what this very much is. Um We have those for, you know, moderately experienced and then for very experienced researchers. So one. So hopefully there's something for everyone. If you've come along to this one and you are a relatively experienced uh educational researcher, then you're very welcome to stay. Um But this is very much focused on those just, just starting out or with a little bit of experience um to help you get started. And this month's webinar is on qualitative analysis. So we've done, we're about half. Well, we are halfway through that academic year. Uh We've already done sessions on an instruction to clinic, education, writing research questions, methodology, some of the practicalities like ethics, et cetera. So do look on meal and you can get the, see the recordings uh for those. And then the next session next month, it's always the third Wednesday of the month is on quantitative research. So we won't be focusing on that today. And then there's future ones on writing up publications, et cetera. So do have, do have a look and you're very welcome to the future sessions. Uh My name's uh Hugh Alberti. I'm a GP in Middlesbrough, but also professor of General Practice education at Newcastle University. Uh And with me today is Alastair dogs who I'll allow to introduce himself. Hi, everybody. My name is Alistair Dilworth. I'm an academic GP based up in Northumberland and I'm also a phd student at Newcastle. Uh Yeah, doing something called education research. That's fabulous. I wanna hear from a Alastair a bit later on. Um I think we've got people from various locations and various professions with us. Um I hope and we, we normally do um it's very much focused on ed um clinical education, but it can be from any sort of background because my mind happens to be medical education. That will be some of the examples I will give. Um but very much this, this should be transferrable to all types of clinical education. I know we'll have lots of different people here So I wonder whether um everyone will be happy just in the chat box uh just to put up um who you are, where you work. Um And what and what you do, um just partly out of nosiness for the rest of us. Um But also you may find, oh, there's a colleague around the corner who's also interested. Um And so some people have managed to link up through knowing who else was here. Um The first half an hour of the session, I is very much di didactic um teaching which I hate but is necessary. Um We've had various sizes and numbers of people attending, which is, which is great. Um But it's much easier with larger groups just to present some slides, present some information. If you do have uh questions, then feel free to put them in the chat box. Um or we could keep an eye on them and there will be an opportunity for Q and AQ and a session halfway through. Um So when we finish presenting, we will then go into small groups for those who are able to stay obviously very aware that many of you are clinicians and some people would rather just come and hear the stuff and go and that's, that's absolutely fine. But those who are interested um and have the time to stay will just then go into some small groups to discuss a bit of implication how we put some of what we hear about into practice um and hear from others in your group and then there'll be a, a bit of Q and A at the end again, after the small, small groups. So there will be an opportunity right now. Um I think with the me, all the um videos and microphones are switched off, but when we get into small groups opportunities hopefully to your video and your mics and to share um some of your thoughts and ideas, I usually forget something, but I think that's everything that I'm meant to say in my uh introductory blurb. Um And so I will now start sharing some slides. Uh So that's the blurb that I've already um shared about. Just particularly thanks to Megan Brown who actually did this session. Um She couldn't make it this time. She did the session last year. So many of the slides are um are from Megan. So she's a colleague of ours at Newcastle. So thank you very much. Indeed. To Megan, we're gonna cover a bit of what qualitative analysis is and some of the principles behind it. Um We mention, yes, please do altair. Have you moved on to the second slide? I have it didn't move for us that's helpful to know the present of you as well. Yeah. So is that not unpresented for you now? No, we can still see the uh your coming slides, right? Apologies, everyone. Um We have been using um medal for a few months now, but it's slightly different. Uh um what I've been used to. So I'm just gonna try a PDF version instead. Is that now showing the whole slide, you can see the whole slide and that's slide. Number one. Fantastic and slide number two. That's the one. Yeah. Fantastic. There you go. PDF is always a bit safer. Um Apologies, everyone. Um So yeah, so, so we'll be covering some of the common types but then talking particularly about um thematic analysis, which is the um the the commonest type um that we tend to use uh talk a little bit about quality and rigor. And then as I said before, there will be a time, a bit of Q and a some small group work and then a bit more Q and A and a couple of final slides. So qualitative analysis is basically once we've collected our data from whatever qualitative source that is um and if you want to know about methodologies, then do have a look back at the slides about the various methodologies, the commonest ones in in qualitative work, uh interviews and foc focus groups, maybe some obser observation, maybe some researchers, notes and a few, few other forms. Um But generally speaking, you then have some written data uh which you then move that data into some sort of explanation or understanding or indeed an interpretation of what is going on, either the phenomenon or the phenomena that you're looking at maybe the situations, the context or the interactions that you're looking at. And it's very much a process of deconstructing the data you have then reconstructing it into another form and then interpreting that reconstruction here is another um definition which I'll allow you to read. And I II like this one and the, the way that I tend to explain it to, to, to folk who not used to doing this before is that, yeah, you have the data in the form of the transcripts of interviews, focus groups, et cetera that you're then gonna break down that data into small parts. Now how small they are does vary, but you're gonna um uh break that down into very small parts. You're then gonna construct those small sections or codes or nodes, whatever you might wanna call them um back into themes or into another way of telling the story that you're trying to answer the question of. So it's deconstructing and then reconstructing that data into a different form. And many people then ask, well, how many codes, how do you work out the themes? What's the overall message? Well, you're basically trying to as much as you can keep true to the data, but to present it and give it in a form that is helping you to answer the research question. So it can be used to describe a phenomenon or phenomenon. It can be used to generate the theory. Often qualitative work might be done before quantitative work, where you then generate a hypothesis and then um test the hypothesis with, with quantitative work. Alternatively, quant work may have given you a yes, no answer or a list of different things. Um But then you want to explore those further or actually start to understand explanations for why you might do do.