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Uh Good, good afternoon, everyone. Um My name is uh Hugh Alberti and a very warm welcome to this getting started session for the incubator for clinical education research. Um I'll mention uh myself because sometimes I forget to explain who I am. So I'm a GP and Professor of General Practice Education uh at Newcastle University. And the incubator is funded by the NIH R that fund a lot of research in this country. And the reason for it was to encourage more clinicians to be involved in education research. So there's lots of different things that incubator does. Um Some of it is involved with training and there's lots of levels of training, there's training for experienced um med ed researchers, there's training for intermediate um or trainee educational researchers and then there's trainees, there's training for novices and this is very much what this session is. So the getting started session is for those with little or no experience in clinical education research. And we know there's lots of clinicians out there who may be very keen uh and well educated, train trainers and educators and just dappling with a little bit of research. Um We know there's lots of really good departments and supervision going on around the country. But we also know that there's quite a lot um outside of medicine in other clinical fields where there maybe just aren't the supervisors and the training available. So this was meant to um help for those in those sorts of situations. Uh This is, we're about halfway through the, the academic year, halfway through the webinars we're offering, but don't worry, they are all available on the med website uh as, as videos. So do have a look back. The last session was on qualitative research. This is on quantitative research in med ed, but we've done, we've done stuff in writing and research, question, writing proposals and then there's some more sessions. Next session is on the practicalities of, of research such as ethics and PPI involvement, et cetera. So do have a look on the middle list of options. Um And then we'll pop in the chat box just the website to the uh the incubator as well. So I think that's probably enough blurb and the, the format. Um it, it's gonna be about a half an hour presentation um from Doctor Bain who will introduce him in a second. Uh As I said, the slides um and the recording will be available on the website shortly after the session. Uh And then after that, for those who can stay, we'll, we'll go into some small groups um or have a, a one group Q and A session and we'll do a bit more discussion just about putting some of the theory that we'll hear about now into uh implementation with projects that maybe you're doing um or would like to do or other projects that people would like to discuss. So over to Doctor Bain. Thank you very much indeed for your input today. No, and thank and thank you very much for having me. So I'm er Robbie, one of the um S FP two s up in Newcastle. Um Apologies if I'm looking up a lot when I'm presenting, it's cos the screen I'm presenting off is um above me. So er hopefully we can all see these slidess and hopefully these will look like um slides I'm supposed to be presenting. Um See, I'm probably one of the SF PS in Newcastle and thank you very much for that very kind introduction. Um What I'm going to try and talk about today is a little bit about my experience within quantitative work um and quantitative research. A little bit of my mind background as well. And then a bit about the methodologies very broadly that are commonly used within clinical education and medical education research to gather quantitative data and then give a few examples of work that I and others have done in Newcastle to try and um explain things. Um what this practically isn't a session on is a deep dive into statistical methodology, which I think everyone's probably relieved about. Um And this is certainly not a comprehensive description of every method of how to do everything in the world. Um And I mean, you're really going to talk about two methodologies collecting quantitative data, but there are certainly more. Um but I hope that this is useful as just a general overview of what you can do. And really the thing I want people to take away from the session is what is possible um in clinical education research with quantitative data and what you can use it as a tool to help you explain and help you answer questions on um a little bit about my background. So I'm um an academic F two in Newcastle um with um I suppose a clinical interest in oncology and cancer genetics. My I've got two academic strands to what to the work I do and two areas of interest, I suppose the first is within medical education research around who chooses what careers specifically, why do people enter academic careers? And certainly examples I'll talk about today are looking at why do people get involved with research and c like academic careers from an early stage within training. Um The second area um with I have some interest in is within bioinformatics and cancer research specifically using something called long read whole genome sequencing in childhood cancers predominantly. Um But the only reason I'm mentioning this is because actually it's fairly helpful because it means a um the sort of skill set to do quantitative research and med um actually overacts quite a lot with the skill set to do the sort of bioinformatics stuff. Um And then there's some s from funders below. So a very brief introduction or a a Robbie Bain definition of um quantitative research which have slightly stolen from the U Kri. Um As an idea is it, is it the idea of using numerical numbers, data collecting it, analyzing it in a systematic approach to measure, describe or explain a relationship within a population. Ie you get, you get some numbers for multiple observations or multiple groups of um people. And you use that to try and explain a phenomenon that you may, may have an idea about, you may have a theory on as kind of A A II in times, I suppose at odds with qualitative research, this is a deductive approach, meaning that you will come up with a research question or an idea or a hypothesis, you will then try and gather data on it and you will then try and test that hypothesis. And certainly most f tests are done with a, with a deductive approach. I ei think this, let's test it as opposed to an inductive approach where you would gather data and then work out what it means afterwards, more in a qualitative approach, I suppose. Um And I suppose very briefly, the, the main thing to think about with quantitative research is that it can tell you what is happening. So it can describe an effect within a population or describe what's happening in the population. But it won't necessarily tell you why. Um an effect is happening now. Certainly, you know, the the inferences that you can gain from data um are currently used are, are often used to try and explain why. Um But directly, the numbers don't tell you what's happening. And that is where particularly in educational research, things like qualitative data comes in to be helpful, but certainly quantitative, quantitative of research can both be its own standalone project, but then also complement qualitative research. Um I'm going to now think a little bit about the sources of data that we can use together. Um and where you can get quantitative data from. So probably the most common um type of da most common data source in quantitative research and medical or clinical education research is survey work. Um And surveys absolutely have a place and they are important and they, and they are a useful way to distribute, you know, potentially thousands of surveys to however many people you want to ask a very simple question to um they're also relatively easy to gather, easy in very inverted commas. Um And you can make sure that you can actually, you can sample a fairly large population or potentially an entire population. Um They can also be a very useful way to gain both qualitative and that of commerce um and quantitative data. But the data that you get out of a survey tends to be fairly descriptive and actually fairly limited unless you do something particularly to make it less descriptive. Um And it doesn't give you loads of richness within your data um because it's usually a single observation. Um and it's based off what a person thinks at that time. So they do ask a person about what they think about a training program and they've had an incredibly bad experience the day before with a supervisor. My, my guess is that their actual thoughts at that time or their rating that they might give on a psychometric scale is going to be different to if you ask them a week before that bad experience, now, that's important, but it only gives you a small snapshot. What you can then think about using is using more sort of longitudinal datasets that might be out there. And these can give you large um sort of scale snapshots um of potentially thousands or tens of thousands of people um in a training program, say, and can tell you about potentially what happened to them in terms of where did they come from? How did they do with this, this within this training program and what happened when they came out the other end? Um Certainly these some large observational datasets can also include multiple surveys. So say, for example, the GMC has the National Training survey. Um And there are other examples in other specialties as well. Um To give examples of how people feel their training program is going over time. Um But 11 problem that can often be is that they are finance.