Transcription
Hey Professor Stuckler here. Today we're going to talk about writing a literature review fast. You're probably here because maybe you've got a deadline coming up in a short period of time—a month, a week, or even the next 48 hours—or you heard because you just feel slow. I've talked to students who have been working on their literature review for seven years. That sounds like endless torture—Purgatory. If I was going to construct a scene of the devil taunting me, it would be the seven-year, never-ending lit review.
So the reason most students are not going fast is because they don't have a system, a system to work from. And so they're just fumbling around, trying to figure things out as they go. And maybe they've had a course at their university or even a helpful supervisor to give them pointers, but eventually they hit a stumbling block. And those stumbling blocks—from our decades of experience working with students, myself as a professor at Harvard, Cambridge, and Oxford—I found there are really four, let's call them four Horsemen of the lit review apocalypse, that you need to tackle and knock down, or you're going to go slow. In fact, when you've got a system and strategy in place, you just have a smooth ride because you kind of bat away these Horsemen; they don't even arrive on your track because you knew how to deal with them.
So these four are: getting your topic right; defining your search strategy; understanding analysis; and writing up and submitting like a pro. We're going to go through each of these, and I'm going to share with you how to use AI tools in the right way to accelerate each of these. Notice, you know, you're going to see a lot out there say, "Use AI to write your literature review." To me, that's just missing the plot because I'm seeing students get AI anxiety. It's like, "Oh, I did some AI stuff; my professor liked it, but now I don't know what to do, and I don't even really understand what the AI did, and I feel like a fraud." No, the correct way to use AI is to understand the system, and then you'll figure out how you can uniquely add in important ways things that machines do better now and are going to continue to do better in the future, while preserving your confidence in those unique human things that you bring to the table.
So let's dive in. We're going to go first to the topic—getting your topic right. Over and over, I've said over 90% of your success comes down to getting the topic right. I remember when I was at Oxford, I had two fantastic postdoctoral researchers, and they were right at the juncture where they're trying to become independent researchers and grow, thrive, maybe get some competitive funding and get their own tenure-track positions. And one was really—I think objectively probably the top person I've ever worked with—and another I would put in maybe the top 20%. Um, but what happened surprisingly is that second one got the tenure-track job faster, got money, competitive grant funding faster, and the difference didn't have to do with ability. In fact, that first student, he was much stronger—strongest student I've ever worked with; he's now a full professor at—the difference was the topic. The second student came up on a hot topic; everybody wanted to focus on and really rode that wave strategically.
And so I encourage you guys to think about the topic where you're going to plant a flag and define yourself on a few dimensions. One is going to be something you're passionate about because if you don't have the passion, you're just going to feel like you're swimming against the tide, against the current, and it's going to leave you feeling a bit deflated over time. And it's going to be feasible. So when I say feasible, it means you can do it in the time that you have. I see a lot of students attracted to learning machine learning right now, and that can be a fantastic investment, but it's a long-time investment. When I was a student, I got rejected for a project that I was trying to get funding for because I said I was going to go learn Russian, and they didn't believe I could learn Russian in the time that I had. And, you know, looking back, they were probably correct. And to this day, I can read some Russian, but don't speak it. So it's got to be feasible with a skill set that you have or you can easily acquire. And for most of you right now, I encourage you to think about low-hanging fruit—things that you can kind of pluck off the tree and get some big wins that are going to catapult your career because those early successes have been shown to predict future successes. And there really is what's called a Matthew effect in science where the rich get richer and the poor fall behind. So you need early, quick-win successes to really propel you on that proverbial fast track.
And the third thing you've got to get on your topic right is a debate. There has to be a lively discussion. I see so many students who are like, "I'm thinking so far ahead of the curve." It's like, "Yeah, well, you're well and good to be ahead of the curve, but you're talking to yourself; there's no conversation yet there." And you're going to have to—you may want to get there, but that can be a life's work; you have to bring everybody there with you, and it's just a high-risk play, and I don't recommend that now, especially for many of you if you're doing a master's, a PhD, early post-doc; that's not the high-risk play. Take that high-risk play when you're in a secure position. Don't have to agree with me; I'm sure I'm going to get slammed in the comments for that, but that's my recommendation—be pragmatic. Some of the students I work with talk about getting a PhD like a driver's license: you can't drive until you get the driver's license, and I think that's a great analogy and a great way to think about it—get to the next step. Nobody wins a Nobel Prize winner here as a grab.
So that said, your topic's got to optimize these dimensions well. Then students say, "Well, how do I do that? That's hard to do." You need to find a research gap. So in that space, you need to go kind of harvest and look for gaps. And what are these? These are things that are just missing from the field. They may be things we think we know but haven't yet been shown or proven. There may be things that have yet to be done. And there are a few sources where you can find these gaps. One is by looking in research papers where they talk about, "Hey, for future research, we suggest doing this." Those are excellent places that you can take off-the-shelf gaps. It's like somebody's just saying, "Hey, you over there, this is a great project for the future; check that out." And often I encourage students when they write a literature review to say, "Roll out the red carpet for the next papers," and say, "Here's what"—the lit review often works kind of as a funnel down here and arrives at the end; it goes very broad, looks at everything, and arrives at the very end by distilling and spitting out some gaps. And here's what we need; here's what's missing in the field. I've looked at everything; here's the next steps; here's low-hanging fruit; here's where we're going to go. That's that's kind of the link point. If you don't know where the gap is, do a lit review; that's just a good thing for you to do anyway to feel confident. If you're choosing a topic and you haven't done a literature review, you could be doing something of very little marginal importance, and you wouldn't even know. I mean, how frustrating would that be to choose a topic, invest tons of time and energy, and it's like it just made a little contribution; that would be terrible. I wouldn't want that for anybody. I mean, most of you are in this biz because you want to make a difference and you want to make an impact. So, you know, doing that leg work upfront is going to save you time.
Now let me show—now that we've got that—now that you know how to comb through research articles, do that and do your own literature review, and we've got step-by-step guides on that, and we're going to take you through those next steps. Let's go over and let me show you how ChatGPT can help with this. All right, I'm here in ChatGPT. Let's say, "Please identify research gaps for a literature review topic that I could write in a short period of time on health inequalities, just picking something random, and lung cancer screening." And not—I've intentionally made this a little bit unfocused to see what ChatGPT comes up with. While this is pulling up—a well-defined literature review topic will have clarity on the outcome. It's looking at what is like that good or bad thing; it's an outcome; it's a result; it's often an impact of something on something else; is that impact on what? Um, and it might also have something that's an exposure, like in this case might want to be looking at—I don't know—the impact of poverty on access to lung cancer screening. That would be a very clear, well-defined topic because it has an exposure defined and an outcome defined. We have a full model called PICO, which I won't get into here, which looks at the four elements of the topic: the population, an intervention, a comparison, and outcome, which you could use to help think through your topic, but I first want to emphasize with you the way to come up with ideas for a topic because you can't really even create a constructive PICO if you're completely lost at sea. So here's where ChatGPT can help; it can give you some ideas here of what you could do on your literature review. You could do a literature review on access to screening programs. So you still might need to take this a step further; this can trigger some ideas. Say, "Ah, maybe I could look at interventions to increase access to lung cancer screenings to help reduce inequalities," or maybe look at what are the—do a literature review on the cultural and social barriers to people going out and getting lung cancer screening.
The other thing I want you to know as I'm talking this strat to you, I want your topic to be so simple that you can explain it. I've had so many students come to me and say, "I want to look at the role of the feminist in the Irish Wars of the 19th century," or or something, and that that's not necessarily bad, but it just—if you start putting in jargon and other things, you can really get yourself confused really, really fast. It's going to make it harder down the road. So I want it to be so simple, almost you can visualize what your topic is about and what it might be showing. And I know that's a bit reductionist, but I want you to reduce some of this; that's the whole point of science is to reduce a complex, messy world into something that's a little more easy to access and make sense for others. So here you go; you can just see ChatGPT can give you some ideas and food for thought. So let's say we want to take one of these. The second thing I recommend you do—remember we talked about those three components that you want to be passionate about; it's got to be feasible; it's going to be a debate—we can go look for the debate. So this is where Google Scholar comes in handy. So let's say we want to look at access interventions around access to lung cancer screening. So here we go—"lung cancer screening access inequalities interventions"—something like this, just in Google Scholar, see what's up there. And lo and behold, I can see—wow, there's there's there's a lot of activity here; there there's lots of—there's recent papers coming out. I can see right away—a high-level statement on this topic here; here's another one—disparities. There's already been some literature reviews, so you do want to do some due diligence and make sure you're not duplicating an existing review. So I always encourage you to find something on your topic called the nearest-neighbor paper: what is the closest paper to yours that's not yours, and make sure you're adding just a little bit over and above that. But the good thing here is I can see that—here's here's one here—"intervention designed to increase the uptake of lung cancer screening equity"—or—endoscopic review. Looks like this one's already been done, but what I—and that's helpful to know, so I don't go then invest all this time and energy, come out the other side like, "Oops, somebody already did it; well, that was kind of a waste of time." So I really encourage you here to go through some of these and identify a topic where you see that discussion and debate from the citations in Google Scholar for your lit review paper to know that there's enough papers, know their citations, but also to see if you can find a gap. If, in the back of your mind, you're hoping to publish this down the road, to say you're not just duplicating an existing lit review. Okay, so now you've got your topic defined; we come to the next fork in the road, and this is where we encounter our next Horsemen of the lit review apocalypse, and that's a search strategy. And I know some of you maybe thinking, "Well, what's a search strategy?" And that's the problem; a lot of people are just kind of willy-nilly hunting around on Google looking for stuff here, there, putting things together, ending up with a mountain of papers, maybe dropping them into a reference manager if they have a reference manager. You need a reference manager! Where have you been? Get Zotero now if you don't have it. All right, I digress. So, um, yeah, you you you need a search strategy. And what is that? Got my topic—great, well and good—where am I going to find articles for this? And what articles am I going to look at? What am I going to include, and what am I also going to exclude? You see, if you don't have this, you're going to take forever; you're going to get overwhelmed; there's just too much mass information out there, and you need to have some filtration system to kick out the stuff you don't want. It's like if you go fishing, right? I'm throwing out a net, and I'm going to get a whole bunch of stuff in there; I'm going to get some sea bass, some tuna, maybe some sardines and anchovies. You've got to know—am I looking for a sardine or an anchovy? What am I going to throw back? So I got a student who is doing a search and getting way too many articles, and I was like, "Well, what what's your search strategy? What your inclusion/exclusion criteria?" She's like, "I don't know." Like, "Oh, okay." Well, her her topic was on the impact of digital feedback on English learning—English as a foreign language learning. It's like, "Okay, well, maybe you want to include only certain types of studies, like only those that looked at an intervention, or maybe you only want to include certain types of digital feedback tools; maybe you specifically don't want AI tools; maybe, right? Maybe you want those that are completely autonomous from the teacher." And so you really have to think hard about the nuts and bolts of your review to make sure you're getting exactly what you want because if not, you're going to feel like you're on the back foot, and you're always finding another article and like, "Oh, look, here's another one," and you just can't go down a rabbit hole and waste so much time and have to restart things, and it changes your analysis and thinking about it. And this idea, you know, right, maybe you've done this before—just kind of working it out as you go along—that works in a contained project, but something of this magnitude—summarizing an entire body of knowledge—you can't just put together this hodgepodge in this way; it's just a recipe for frustration and taking too long. And I'd say the main reason people aren't going fast is not because they can't go fast; it's because they're taking too long. And I know that might sound kind of—well, isn't that the same thing? It it's not because you've got these roadblocks that are stopping you from just continuing to have a smooth, easy ride. So you need to define your search strategy, and you're usually going to fall into one of two camps: you're going to go down the path of Google Scholar and you're going to search there, in which case you still need inclusion and exclusion criteria, and we've got dedicated training on setting those up for your topic. You have to have the clear topic right before you can do that and knowledge of what you want and where you're focusing your review and that incision you're making into the world to say something new. The other type that I strongly recommend—as you know, I'm a big advocate—is a systematic review, and sometimes called a scoping review, integrated review—lots of different names for it; I just call it systematic review—and I aim to do that because that's kind of the highest, most well-respected form of a lit review, and that takes a more formal approach and doesn't search things like Google Scholar, which put you in a filter bubble. You know, if you're scrolling Instagram or YouTube, the algorithm showing you things it thinks you want to see—that's not great; that's not scientific because as scientists, we want to do things that—if I put in search terms, then Divya will get the same search with those same terms, or Jessica will get the same thing with those terms—that's reproducible; that's scientific; and that that's what you want to aspire for. So there you're going to look at databases. So if you search with these keywords on this date, no matter who in the world does that, you're going to get the same thing. Now those searches you'll often build up from keywords iteratively, and again there's there's some training around that. I think it does take a few seconds to learn how to do that, but once you've got it and mastered it, it's a valuable skill to have in your kit, and it forces you to be very clear about what you're looking at, so it's harder to go off and get derailed on the road to finishing your literature review on that systematic path. But you, you then either way, need to have a search strategy; know which databases you're going to search; know what's in or out; and kind of define and articulate something you're going to want to write in your literature review anyway; what keywords you want to put in.
All right, now notice I didn't inject an AI tool here, and that's because AI is not helpful here; AI is partial. And the way AI searches can kind of work is—there's a tool I recommended just recently in another video called SCACE; it does have a lit review search tool, but it kind of snowballs, and it optimizes and gives you top sources, and it can be useful for getting the sense of the lay of the land, but it's searching a partial set of the literature, and it's like, "Well, why would you want to go search for scientific knowledge with only one eye open?" Not really a good way to go; I don't recommend it. But AI is going to enter and make it come back in our next stage, which is the next Horseman of the Apocalypse is knowing how to analyze the data and write up. And I think a lot of students struggle here because they just try to write up. Okay, I got the—I'm I'm going to vomit on the page and write up. That could be useful, but most people don't really vomit very effectively. I mean, I'm I'm only partially joking. You you need to have a sense of what you want to say before you write rather than, "I'm going to figure it out as I write," which is what many of you may have done in assignments before. There's a level of thinking where you could say, "Okay, I found these papers, and I'm just going to summarize what each paper did, block by block." Well, that really is something like a four- or five-year-old could do; that's not higher-order creative thinking. The real power of thinking in a literature review comes through synthesis when you have, say, a set of articles, and suddenly you can see things that nobody else could even imagine before because you start seeing patterns emerge across those articles; you start seeing trends and currents, and that's really helpful; that's the real value because that's where you start to spot gaps. You say, "Hey, all these guys maybe looked at depression with this index that's not that great, and they're not getting good findings, but these few studies that did use a good measure of depression founds—"
A completely different result if you're just kind of looking at the trees one to one, each individual article; you won't see that. But when you zoom out, you see something different. Uh, personal experience: one of the bigger findings that um came in my own personal professional work uh was when a lot of people were having to debate—it was a time of recession—is unemployment bad for your health? And some studies were done, and they were finding: no, unemployment is actually good for your health, paradoxically. How can that be? Well, people were at home; they're going to the gym, or they're cooking at home, and and they're getting healthier. Other people are finding: no, unemployment is terrible; people are are drinking too much, and they're killing themselves, and they're stressed out of their minds, and they're on the couch eating potato chips. And it's like, well, how do you reconcile this? Well, as I look closer at each individual study, I said, well, these are just contradictory. But when I took the whole map of studies out there, I found, wait a second: the studies in Sweden and Finland and places with strong social safety nets and support systems, they're getting completely different patterns than those where people—they lose their job, and they're just poor and completely exposed to their life falling apart, like in some parts of the US, for example, uh where I'm from.
Um, and that's about literature review synthesis. And so I want you to get to those results, and this means really getting deep, intimate, and dirty with your papers. I recommend going so far as extracting knowledge; if you're doing a systematic review, you might extract the core features about your research question into an Excel data sheet. And I'll got some summary templates for you to check out on how you can do that, and you can see that in our live full systematic review step-by-step training course that you're going to find if you go to playlists on my channel. It's about 12, 12 hours of training, uh but it will it will change your life. I'm kind of biased; I think it's great training, but I've seen so many students get great results and publish a literature review in a very short period of time; it's awesome. And uh I'm I'm really proud of that, and uh you're going to love it.
So you need to get that that Clarity. What that involves is something called a strip method: you need to strip information out of these papers—the key points, key things that found in relation to your topic—and and that big overarching theme and question, the impact of what what of your paper—and start lining those up togethers, and you know, twist and spin and perturb the data and look at at different angles until you start seeing some meaty, thick key insights. Now, when you write up, you want to focus the literature review and create a structure around those big themes. Okay, again, notice I haven't talked about ChatGPT in any of this, and none of this. Now what you can do that can be quite powerful. Now we can come back to Scape, which can be quite helpful. I'm going to share my screen again, so here we go. Scape, fastest reach search platform ever, has a function where you can come here, and you don't want citation generators; you don't want to paraphrase; you don't want to have it write for you; you don't want to do an AI lit review; this is all going to get you in trouble, and it's just going to make you feel like a fraud; don't do it. But go to extract data, and uh drop in your PDFs in your inclusion here, and this can help analyze and cluster some themes. I would rather have you identify them on your own. When you drop all these in there, it will give you some ideas about main themes that you can explore. Let me show you an example; this how how you can use these main themes. I've got a set of the articles I use for a literature review on uh the impact of commercial factors on sexual and reproductive health. And so just come here, upload your literature review files. Again, this is not a substitute for you doing this yourself, but it can be an extra check. And here you go; you can engage; you can chat with any individual paper, um and you can pull out specific features, which is doing now: the method used, the results. This could be useful for extracting data, um but you can ask some questions here. And again, you can unlock high quality if you pay for it; um you get more tokens if you pay for it. We've got a discount below: David S 40, 40% off; David S2 for 20% off, uh annual or monthly. Again, I'm not sponsored by them; I find this is a helpful tool; we've brokered that discount for our FastTrack students. But say here, um how new commercial factors impact on sexual and reproductive health, and it will give you some ideas; it will give you some things that maybe these become subheadings for your outline in the structure of describing your literature review. And so I can see here there's some stuff about conflict of interest, so maybe I'm going to summarize some things about conflict of interests. Maybe here there's a problem with uh the or the role of the pharmaceutical industry.
Now, ultimately, the big point is you're going to need a framework for summarizing your key points so that they can really bloom to the fore. Because when you do a literature review, there's going to be so many details, so much information; your job is to highlight the big points, the big messages. And there's no simple magic formula for that, but um by breaking down and analyzing the data, you will see these themes and trends start to erupt to the fore. And you can use AI tools to help give you this check. So here you can see almost as well an idea of how an extraction can help you to see patterns. Now you can see this is methods used; it's not really doing a good job of extracting the methods; the methods might be the method here is case studies. Well, we did a case study on May Mar's Community Based midwives, so it doesn't really extract that well. Let me show you another example of what a formalized extraction table looks like, so so you can understand, and I'll show you one that one of our students has been working on. So here's an example of an extraction sheet where you might strip out specific components: well, what was the study design? Here was a paper looking at inequalities; what racial comparisons were being made? What was the inequality measure? What did they find? And again, it's just forensically going through and pulling out key elements so that when you look across the papers, you—this student could start seeing patterns: oh, well, the East Coast had higher inequalities than the West Coast; the inequalities between black and white were worse than those between Hispanic and white; other insights that you just couldn't see by looking at one paper alone.
A more formal way of doing this, just for your own knowledge as a side note, um is called meta-analysis, when you might go so far as using these articles as data and turn them into a quantitative data set that you fully reanalyze. Most of you, that's more advanced; you could definitely do it, but get the review done, and then let's move on to some of these more advanced techniques where we integrate quantitative tools or other advanced uh analytical instruments. Sometimes you don't even need them to get a big payoff um in terms of what you find. No, again, that's the principle; AI can help, but is it necessary? No. Can it help you go faster? If you don't use it in the right way, you could even feel more lost and lose more time. Finally, with this writing up, you need to have a system for writing. And so many students, maybe they got taught some writing in high school, but by now they've long forgotten it, and they're just writing; they have no idea what they're writing, why they're writing, what goes where. So fundamentals: you need structure; you need an outline, of course; we talked about that, but we recommend a PIER system. And the PIER system just breaks down the most basic unit of an academic paragraph where each paragraph makes one point and is broken into PIER, and we call this like a mini hamburger model because it's kind of like a hamburger with a bun and the meat in between and then the other bun at the end. And so the P of PIER is the one big Point each paragraph should make one big point; then stuffed inside your burger, you're going to have evidence and examples—your Tu, appear—evidence, examples; so um, right, evidence, examples; pack that in there, and you need to explain it; and then you come to an R, which is your repeating or linking point to flow into the next paragraph. I've got other videos where I go through lots of academic text showing this format, showing the system and how it's common again and again and again; makes it really easy for your readers to follow because your big point of the paragraph is going to be right up at the top. So, for example, you might have a paragraph that says, you know, the war in Ukraine has been devastating. Okay, well, you kind of know what that paragraph is going to be about; what's coming next? Well, you can even foreshadow: you know, well, I'm going to give you some evidence and examples of that: over 10 million people have lost their lives—I'm making up a number; no offense to anybody, wherever you stand on this side of the war; this is not a political Channel; I know it's impossible to be completely a political, but I don't want to go through a long discussion about Foucault. All right, so uh yeah, so evidence, examples: 10 million people have died; maybe the explanation is the deaths of come not so much from direct, you know, War casualties, but indirect consequences like damage to sanitation, sewer systems, and associated knock-on consequences like infectious diseases. And if this—now you've got your repeating or linking sentence—and if this war continues, the consequences will likely grow larger if, right, I don't know, NATO doesn't take urgent steps to intervene; and that might flow to your next paragraph then talking about the global response to the war. But notice that keeps things simple; I see so many hamburgers that are just like stuff; I got some bacon and egg and beetroot and mushroom and like just too much stuff in the burger; it's not tasty anymore. Um, so keep it focused; keep it lean and mean: one big Point each paragraph. You need a writing system; this is again where ChatGPT can help you. And in the background, I'm working on creating a custom GPT for all of you that you can use that embeds our PIER system. But take your paper and your draft and upload it to ChatGPT and ask it to follow Professor Stuckler's PIER system to make sure it's following good habits of academic writing; have it edit for style, not substance. So you need to get that prompt right in ChatGPT: style: formal academic writing, not substance; you don't want it to change the content; that's what you need to preserve yourself. When you're changing substance, that's when AI detectors like ding ding ding ding ding, going off, and you get in trouble. By the way, Sapling has an awesome AI detector; go check it out. All of them have false positives; I got a video where I review that below.
All right, then we are coming to our—I know, thanks for sticking with me, guys; this has been a long video, but I hope it's a helpful video. Submitting: those of you who want to submit to a journal, or even you're submitting for a grade: submit like a pro; don't make rookie mistakes. And what are rookie mistakes? When professors like me are reviewing your paper, whether it's for pre-review or as an editor sending it to a journal or for a grade, we're busy; we're going to skim it, and we're looking for indicators quickly of quality. So if you got a spelling error in your title, you probably have done a cursory, not very thorough job, and it's already primed me to make a judgment. The reality of the psychology is of how people engage with these things; this has been studied elsewhere; I won't go into that, is they read quickly and make a relatively snap judgment about where it's at. So for peer review, is it—I'm going to send this for a review or not? Am I going to accept this or reject it? Is this an A paper? Is this a C paper? And then they go look for reasons to justify that decision. So especially make sure your front matter does not have what I call howlers; it's like, oh man, it's so painful; it's just screaming off the page how bad it is. ChatGPT can really help with that; run your paper through it and say, please identify any major grammatical errors or typos; get rid of those; there's no reason this day and age, even if English is not your first language, you can't have a grammar checker. I also highly recommend Grammarly; it's not an AI tool, but it is like a bread-and-butter tool that you should be using, especially if English is not your first language; the free version is more than good enough for what you need and will catch those grammar mistakes.
The second thing, if you're going to submit to a a journal, is to know how to write a cover letter and know how to choose reviewers to recommend and not recommend. Why is this important? I get a lot of students saying that I need somebody famous, and actually, I don't—don't recommend that; that can saddle you with problems of a different kind, having somebody famous on your paper because they have a reputation for better or for worse, and you're just latching yourself onto it. So really the issue comes in submitting like a pro, like you've done it a million times before. And the first thing that the editor is going to look at and really notice—well, it's going to be the title; it's going to be the abstract, uh of course, but it's going to be your cover letter, cuz that's when you're speaking directly to the editor and explaining the value-add of your paper. If you've never written a cover letter, um check out our dedicated training on submitting, which will help you identify the right journal to submit to, how to write a cover letter; we've even got a dedicated video with the executive editor of the quite possibly the most important medical journal in the world, The Lancet; I'm sharing her tips, and then those tips apply across social sciences, across Natural Sciences, across Humanities, across the piece; really fantastic tips on how to stand out and get picked up and increase your chances for success. I mean, what a waste to spend all this time and effort to get to the Finish Line only to like run out of gas and fall flat and get kicked out because you made rookie mistakes; don't let that happen to you. Also, don't forget: use your reference manager; get your references in line; it's just something that you shouldn't mess up; it's just too easy to let that Horseman of the Apocalypse get you—guys, like gotcha at the very end because you didn't get your references right. Click of a button with a reference manager; you're going to format everything to looking beautiful and adjust for whatever you're submitting; many programs will require uh APA, MLA, Harvard, whatever, Vancouver formatting; click a button; you're going to be doing everything just the way it's supposed to be done without wasting endless time and energy formatting, guys. All right, that was a lot of ground we covered in a very short period of time; I am sure you're going to have a bunch of questions. And lately in my Facebook group, FastTrack Grad, we've got lots more master classes that go in dedicated topics with live student examples on each of these topics on how to conquer these four horsemen of the literature review apocalypse, and you can submit video questions, be in direct touch with me, right to our communities of practice; these are professors, researchers, grads all across the board collectively working to try to help each other produce the best quality research possible; it's 100% free and valuable, and again, we can be in direct touch in the DM. So if you are trying to get a paper done fast, that is a tremendous resource for you; click the link below and check out my next videos. If you are at the writing stage, that go in detail of how to write the discussion, how to write the introduction—hints: save the introduction for last—more than that later, um how to write the methods, and will look forward to seeing you there.