All right everybody, what is going on? Welcome back to another video. I want to thank you for being here. I want to say I hope you've enjoyed the list. I don't know exactly what number of the top 10 this one will be because we're not numbering them. But these are the 10 greatest New Zealand All Black Rugby players of all time. Michael Jones. Sir Michael Jones was a player that I didn't get to see personally play a hell of a lot. His career was was blighted by injury but we will get into that. Michael Jones, Michael Niko Jones was born in Auckland New Zealand on 8th April which is my sister's birthday believe it or not. 1965 he was listed at 1.85 meters tall which is 6 foot 1 which is my height believe it or not and 98 kilograms or 216 pounds. Michael Jones was named as All Black number 882 when he made his debut in 1987. Michael Jones played a total of 55 test matches for the All Blacks between 1987 and 1998. Although due to having two serious knee injuries one in 1989 and one in 1997 and a broken jaw in 1993 he only played 55 matches when the team actually played close to 90 during that time. Michael Jones was always considered the top pick during his career although due to those injuries he missed you know almost 40 matches which is tragic really. Michael Jones played as a forward on the team but had the skills and the pace of a back. He scored 13 tries in his 55 matches which was a lot for a forward at the time. In the year 2000 Michael Jones was voted the third-gracest New Zealand All Black to play the game ever behind the great Colin Pine Tree Meads which is also on this list and Sean Fitzpatrick. John Hart, ex All Black coach and ex Auckland coach. When he first selected Michael Jones to the Auckland Provincial team was quoted as saying he was the almost perfect rugby player. Michael Jones as well as Sean Fitzpatrick was a member of the Dynasty Auckland Provincial Rugby team and I'm just going to run through a few of his titles here which he won most of with Sean Fitzpatrick as well as a few other legendary All Blacks. We've got between 1985 and 1999 Auckland won nine provincial titles, five Super Six Championships and defended the Ranfurly Shield a record 61 consecutive times. Now that is absolutely ridiculous that means that Auckland were unbeaten at their home ground for 61 consecutive games between 1985 and 1993 and Michael Jones was an important part of that. Just like a lot of players on this list Michael Jones was more than just a rugby player. He was a pillar of strength especially for the Pacific Island community in New Zealand. He is of Samoan heritage and he also coached the Samoan National Rugby team from 2004 to 2007. Now Michael Jones played for New Zealand between 1987 and 1998 and in 1990 three years into his All Black career he received a New Zealand medal for service to the Pacific Island community so even that early in his career he had a mission outside of rugby. You know he knew that his position in rugby was going to be worth more than just the game. In 1997 nearing the end of his All Blacks career Jones was inducted into the New Zealand Order of Merit and 20 years later he was knighted and became Sir Michael Jones for his contribution to the Pacific Island community in New Zealand and around the world actually. In fact I just had a little story here there wasn't much information on Wikipedia about his knighthood and there's just a quote here from the great Sir Michael Jones and it starts with he felt his work was a calling and said rugby had been a great platform for him that allowed him to give back to his community. When I retired I knew I had to steward the gift of being an All Black and use it wisely and effectively particularly to contribute to my community and in doing that contribute to society as what is good for Māori and Pacific people is good for New Zealand. Having a mother who demonstrated those values of giving back every day of her life was a huge part in shaping that in Jones he said. That was in 2017 only two years ago. Michael Jones has always been one of the greatest rugby players of all time and it seems that didn't just start when he started playing Provincial Rugby or All Blacks Rugby because I read here that his talent for playing was discovered early very early as a 10 year old tackling 15 to 18 year olds. You know that kid in the team that kid in the team that's just bigger than everyone else well he didn't play in that team he actually played in the team above and above and above that to try and I guess test himself. He went to Henderson High School and is credited as being one of the main reasons why they turned that team from being very mediocre high school into one of the most successful high schools in New Zealand rugby. Michael Jones was not just a rugby player he's everything we've talked about but he's also a very very smart man and he has three degrees under his belt. Now how many professional athletes do you know that have three degrees? They might have one well they might have two but they not many have three and so that is just another feather in the cap I guess. This man Michael Jones absolutely legend I don't have any particular I don't have any personal stories on the man but what you will see is some video excerpts from this this DVD that I've managed to acquire it's absolutely fantastic it's going to give you a nice rundown with some nice footage good quality footage which is important of the great man Sir Michael Jones so sit back relax enjoy and I want to say go all blacks for their first match against South Africa in the 2019 Rugby World Cup let's go. Grant Fox kicks out it's 16 points to three and the Pumas have had a good 10 minutes now but here's the New Zealand team are next again ah this is the tactics that beat the French Stonely waits all the way under the posts. Arguably until the arrival of Richie McCall Michael Jones was generally considered to be the greatest ever all black open side flanker certainly Jones matched McCall for pace and athleticism and this combined with a fierce tackle and the ball skills of a midfield player made him one of the most naturally gifted players of all time. I can't I can't blame you can't blame Scotland he's a rugby player but look at the speed the black shirts are approaching oh there's a bit of obstruction went in Jones got the bounce and in with a dive under the posts unfortunately Jones career was marred by serious injury in particular he suffered a knee problem in 1989 which kept him out of the side for over a year however he never lost his strength and commitment and his ability to break a tackle. Coming on the all-black side Shelford up to Kerwin Jones Michael Jones and Michael Jones does it again and it's his second for the day all blacks have scored 10 tries on the test match this afternoon. Here it comes again and Jones was very lucky here he shouldn't really have been bold but great balance too he weaved and dummied and found it very easy to go in getting very easy now for these good forwards. Another headache for the selectors was the fact that Jones refused to play rugby on a Sunday due to his religious beliefs this became an issue especially during a World Cup in 1991 he was unavailable for three matches which led to him not being picked for the 1995 tournament as the quarters and semi-finals were scheduled for a Sunday. Despite not being available for some of the World Cup fixtures Jones still made his mark at the tournament scoring the opening try of the inaugural World Cup against the Italians. And again scoring the opening try in the victory over the French in the final. And to complete the hat-trick Jones scored the opening try of the tournament in 1991 this time against the English. And again scoring the opening try of the tournament in 1991 this time against the Italians.


Okay. So the first group is electronic phenotyping for genomic research. And George is leading us off. And Josh or Marilyn, you're the timekeepers, right? For your group. Okay. Okay. All right. Very good. Good morning. And so I'll be speaking, representing the phenotyping workgroup. And we did review our slides together. And I'm going to, and so thank you, Rex, for doing that excellent introduction. And then actually, Michael did a lot of stuff on machine learning and phenotyping, which also is useful. So thank you for that. So we're going to go straight into, our job is to go straight into the questions that were asked. So how can eMERGE improve upon the current labor-intensive phenotyping towards fully automated phenotyping methods to increase efficiency and validity across EHRs? So first, I'm going to put one thing aside. One thing we're doing, and take the sharing part, because the tail end of phenotyping is about sharing. The context is we have academic medical centers, they each have a clinical data warehouse, they've each already done their abstractions from the EHR, and they have a table or something, a diagnosis, and each of us has picked a different column or row or name to give it. So the thing we're doing now is the easy, the low-hanging fruit is just, since we've already done that or the hard work, let's just put it in a common format so that we can share our phenotype and cut two years off the phenotyping effort. We picked the OMOP common data model just because it was mature at eight years and shared in other efforts, including all of us. As I said, we're putting our current, and then it preserves just a comment. We had a conversation, Sandy actually alluded to it. There's a lot of work to take your data and put it into a standard format, any standard format. 4,000 lab tests is actually representative of what Sean and I also found. We have lab tests both coded in LOINC for autoimmune disease, the tests, the serology you do around them. Our LOINC codes are non-overlapping almost because we've given different names. So that's a process you have to go through, that curation process. The data model doesn't cure it, you need knowledge and skills to put that into a common thing. That's what Michael was referring to, whatever the model is, doesn't matter underneath it. What you can't do is avoid the work of putting it into a common format. OMOP happens to have a data schema and 43 terminologies to map from, but there's still a lot of work getting from your local vocabulary codes to, say, LOINC and even a common LOINC. So now we get to the front end of the eMarch phenotype generation process. And so I was asked not to review all the work, but I just want to point out on, I still want to show on a slide, there's a lot of research has gone on in the eMarch network and how you carry out a phenotype. So what I've done on the next slide is just extracted from this work what were the lessons. So first the challenge of billing codes, the healthcare process affects what we record. It's not a research database and electronic health record. It's good data. I mean, yes, there is noise in it, but it's good data, but it's meant for a different purpose. So the first thing is that we need to, and that's why we have phenotyping. It's going from the raw data to an intermediate state before we do the research. The importance of NLP that we found that in many phenotypes, it was really essential. Sometimes there just is no structured code for the thing we're looking for. The complexity of our phenotype definitions we learn that it's not, that they tend not to be just a simple, you know, use a code or use a code in a med. And you saw some examples earlier in the previous talks. What we're seeing so far is we can get improvement from tools and reusing definitions, but mostly what we're doing today is just slogging it out. As we go to the next definition, just a lot of hard iterative work. It's always iterative to improve the definition and the tools do help. Lastly, I want to point out that there are different goals. Knowledge discovery via GWAS is mainly picking cohorts. So that's getting a high positive predictive value. As you move into other types of studies or delivering results to patients, then the sensitivity also matters. We want to reach out to everyone for whom this knowledge is relevant. So there's two different kinds of phenotyping going on and it's important to keep track of those. So now the main thing I'm going to go into for this is going to be machine learning but since that's the second question I'm going to skip over that and go to a slightly different topic and just talk about high fidelity phenotyping. Okay, so now the other thing I want to say is that we're not showing today is what the future RFA should be. We're just showing the context of what is going on in the future. We should know what's current research today. If we're picking what's going to go on for the next five years, we should know what's state of the art today. So one thing is high fidelity phenotyping and I'm going to go into some examples in a moment. First, encoding degree and severity of a condition, mostly what we've done, mostly, is deciding binary, yes or no, do they belong in a cohort. And I think we'll see our research needs and for patient needs, we're going to have to have different probabilities, severities, degrees, you saw some examples in Mike's talk. Our chronic kidney disease phenotype in the network is five levels, the different stages. So we're moving to more graded phenotypes. Time is essentially important, we're using time but I think there's more we can do, both two halves of that. First, using time to decide, even if it's a binary decision. Or second, expressing the timing of something, the chronicity and also factoring time, accounting for the biases that the healthcare record produces. To give you a concrete example, people are sampled in the health record when they're ill, they're sampled. You don't get a creatinine at three in the morning unless the person is sick. So if you look at creatinines at three in the morning, it looks like the population gets sicker at 3 AM. They're not getting sicker, you're just not sampling healthy patients at 3 AM. That's the kind of thing we have to count for as we do more sophisticated phenotyping. We can use the phenotypes not only not binary, but maybe not even a set of categorical, it may actually be continuous states. Using examples, this is in breast cancer and in diabetes where people had to continue them from one state of a disease to another. Using our physiologic information that we have sitting there in the literature to help us do better phenotyping, don't be purely empirical. Try to incorporate some of the knowledge to build better phenotypes that are more resilient against the noise. I don't have it here, it's a different slide but shows that given physiologic model knowledge, you can learn things from sparse, noisy data sets. And actually learn things like kidney function, renal function, insulin resistance, insulin degradation rates and everything you can estimate. Things that we could be doing with an e-merge with our say diabetes studies. I guess this overlaps the next question, but learning late in states, using modern mechanisms of deep learning. I think I'll go into this a little bit more in the next one, so I'll just state it. And then I guess I've been talking about the whole time accommodating the bias in healthcare records. So these are things we could be doing in the future in phenotyping. How might machine learning and other advanced computational tools be used to improve electronic phenotyping in the e-merge network? First thing, natural language processing has turned out to be an important tool. And there are two directions we need, breath and depth and effect. We need to be able to, first of all, and show the last one with the exclamation point sharing natural language processing. It takes a long time to share a phenotype across the network with each one using a different natural language processing approach. And we do try to share our code across the network, but because the reports going into that code are different, it creates a lot of work. In addition, there's work, modern approaches to natural language processing, which many of the groups have been using. Machine learning research for e-merge research, I'm going to show you some examples that the group sent to me on the following slide, so I'll come back to that. Some other things that aren't so much on the slides, if you're going to do machine learning, and as you saw in the previous presentations, you need a training set. And that's the money in limitation, we have to sit there. Now we already need, say 100 or 200 cases to test the accuracy of our phenotypes. We're already doing manual curation. But once you do machine learning, you tend to need an even bigger training set. And it becomes onerous. So there are advanced methods for helping us do that. Sontag at MIT's anchors, or Shaw's noisy training sets and so on. To reduce, to focus, which cases should you curate? In other words, here's what we know about the algorithm so far. Tell me what cases would be most useful for to have an expert look at instead of doing a broad swath of cases and trying to, say, review a thousand cases. Similarly, active learning, which is a related topic, basically accomplishes the same thing, reducing the training set labor. And then I mentioned deep learning. For example, the stuff being done at Mount Sinai on diabetes. And then the other two I already mentioned. So we've had some success here at Harvard. We achieved over 90% PPV, purely learning our, I'm sorry, rheumatic. R.A. Rheumatoid arthritis phenotype. And that was based on just 300 training cases. So that's getting the number down. That's pretty good performance rate. So it takes a long time for a doctor to review 300 cases. But nevertheless, that's still a relatively known moment, and they achieve good performance. Vanderbilt having carried out similar work and also achieving a PPV and that of what was it, like 0.95, 0.96. So we are having success across the network in using machine learning. This then affects a lot of other things you don't realize. So the ardent syntax has been around for decades now for encoding medical knowledge. But it was focused on rules that we write, and that's what most of our phenotypes that we've produced so far are. As we move to more machine learning, then it's really a matrix of a coefficients that apply to your set. And sharing it means something different. It's not so much tweaking rules now. It may be you creating a small training set to tweak the coefficients or doing no further training. But when you share the thing, it's basically a list of real valued numbers. Further work on, this is Mayo, syntactic interoperability related to natural language processing and phenotyping. Again, I'm just gonna go through quickly so that we don't, the tools, so that we have time for questions and further discussion. The phenotype execution and modeling architecture that is producing sophisticated tools that help us build them, especially if they're built as traditional rules. But also if you do machine learning, that can be a module that then creates coefficients and it's been extended to the models that we're using, like OMAP and I2B2, and incorporating natural language processing and machine learning. And that's been funded for the next cycle, so that's still relevant to us going forward. Further work in, this is Cincinnati and Boston Children's on combining NLP and machine learning. Again, I don't want to go into using standard vocabulary, SNOMED, CT and RXNorm, incorporating a tool that a lot of us use, Ctakes, which is when we try to share NLP using a tool, we often will go to Ctakes. Using advanced machine learning methods, just pulling this one from Marshfield, well, from the group, to show examples of the eMERGE network using advanced machine learning methods, and active machine learning, which I mentioned earlier, again, this example coming from Marshfield, reducing the workload on human beings. How can eMERGE assess phenotype compatibility across diverse patient populations and diverse healthcare settings, academic and county hospitals, community clinics, national healthcare systems? So we can design specific eMERGE experience to do this. And as I read the question, a bunch of thoughts came into my mind of what we could be doing. Then reality set in and I was realizing how many phenotypes we've yet to do in our department just to keep up with the workflow and there's Peggy. So it's hard to do new experiments in this, but I think there is a lot we can do within our existing infrastructure. But then we heard a talk about all of us, and we are using the same OMOP data model between us between eMERGE and all of us making the sharing of things a little bit easier. So all of us is getting up to speed as you saw, but I think that program will allow us to test what we do here in a very diverse environment. Also, we're sharing with the Odyssey collaboration, which already has 400 million patient records. So it doesn't have the genotypes. But if there are any questions we have that are just phenotype related, we can run that on the network today. And there's no reason not to go forward there. So that's what we prepared from the phenotype worker. I'm sorry, are we gonna go straight to Ken or answer any? Okay, good. Unless there's a clarification. If there are clarifying questions, we can go to this. But we'll have discussion after, Ken. You're very clear. This is Terry. I might just mention since these screens are not working, if you guys want to get on the Webex, and if you can't see, it's quite nice on the Webex, so I would suggest that.


The Republican-controlled House passed a bill that would have taken back $72 billion in funding for the hiring of tens of thousands of internal revenue service, IRS, employees over the following 10 years. The White House announced Monday that it will veto the measure. The Inflation Reduction Act, which President Joe Biden signed into law last year, included increased funding for the IRS to hire personnel totaling $80 billion, which was passed by Congress last year. Republicans on Monday passed a package to recoup the majority of this money, which the White House claimed would encourage corporations and affluent Americans to evade taxes. The White House issued a statement stating that far from aiding middle-class families or small companies, HR 23 protects affluent tax cheats at the expense of honest, middle-class taxpayers. With their first piece of economic legislation under the New Congress, House Republicans have made it clear that their top priority is to make it easier for wealthy individuals and multi-billion-dollar corporations to avoid paying their fair share of taxes while making it more difficult for regular middle-class families who do pay their fair share. They have it as their goal, not the cost or tax cutting for Americans who work hard, as President Biden has often argued. The IRS agents are required, according to the White House, to ensure that the wealthy is pay their fair share of taxes, which aids in lowering the government deficit. The IRS received funding with the intention of hiring new agents, auditors, and support employees. The GOP package that was approved this week keeps funds in place for hiring customer service personnel and making technological advancements. The package is not anticipated to succeed in the Senate, where Democrats are expected to maintain their majority, eliminating the need for Biden's threatened veto. The White House issued a statement saying that if the president were presented with HR23, or any other law that allows the richest Americans and biggest corporations to cheat on their taxes while honest and hardworking Americans are left to foot the bill, he would veto it. How do you feel about this recent development? Do you genuinely think that President Biden would veto this bill? Post your ideas in the comments section below. Thanks for watching.


Hello, my name is Tracy Togamaspinosa and this is a video summary of a paper I wrote related to the contributions of neuroscience to early childhood education specifically related to pre-literacy and pre-numeracy skills. What I'd like to do in the short video is discuss the problems that were faced that actually led to the research questions, the methodology that was applied and to conclude with some findings as well as an invitation for your participation in the study in the future. So now for the problem as I perceive it. As someone who's directed early childhood programs, I've been faced with the question of the philosophies that are behind different types of programs. Oftentimes we have extremes, right? There are some people who profess that the philosophy that we really need to have, no structure, you know, open and free play in order to enhance creativity in children and others who push for very structured learning environments to improve self-discipline in children. And there's others that look towards developing the emotional aspect of children's personalities and helping them be more balanced in that sense and working with others in a social context. And there's others that actually push this very strong cognitive or intellectual development. My proposal here is to look for someplace in the middle. Is it possible that we can find this middle space where we can actually meet all of these different types of needs, that we can actually improve student learning outcomes and at the same time create the environment for forming better future citizens who are more balanced and caring for one another? In order to do this, I'd like to approach this from a very multifaceted perspective, which is a transdisciplinary solution that's offered by MindBrain Education Science, which is an area I've been researching for the past 10 years in which I believe holds a lot of the answers to educational problems, especially by looking at contributions from neuroscience to the education field that have as of yet not been integrated into our general teaching formats. So what are the research questions that come out of this particular problem? They're basically two global ones. First, what is out there? What information exists in educational neuroscience that has not been applied in classrooms as of yet? And second, how do our teaching practices stack up against recommended practices if we were to include more information from educational neuroscience? And this begs some several sub-questions, right? In math education, we have to look at really where the standards come from, what type of evidence is sustained, their current structures. And secondly, from neuroscience, what is it that we really know about the brain and math, and especially about little kids, because we know that those types of studies are very few and far between? Then we want to finally look at whether or not the information actually contradicts or complements current practices that we have. And if they do contradict current practices, how do we modify those practices? So the basic running hypothesis here has three different aspects to it. First, this study was inspired by an earlier study I did in 2013 for the Costa Rican government in which they presumed that there was unnecessarily high levels of school failure in grade one in the early years. They estimated as high as 17 percent, basically because there are certain neural pathways that should be primed before kids are ready to do grade one learning, and they aren't being primed in early childhood experiences. Secondly, I make the presumption that there are also just average kids who are not inspired or enthusiastic about our school settings. In part because of the order of introduction of certain types of skill sets that does not allow them to ground the information into real life experiences. So they're missing the big ideas. For example, they can do mathematical procedures, but they don't understand why they work, right? So they don't understand the big concepts behind the execution of certain skill sets. And finally, there's a presumption here that the learning trajectories, if we can actually order the introduction of certain learning skills in the right way, this could actually yield a better approach than we currently have in curriculum design for early childhood education today. So what was the methodology to apply to this study? There were basically five different large pieces to this. First, there was a large scale international view of the early childhood programs around the world and basically what is expected of children zero to six. Second, there was a large scale of a limited study of existing research on the brain and math. The primary came from the United States, France, the UK, Japan, China, and Canada. So it is international, but it doesn't cover very many countries because many countries are just not doing research in this field right now due to the high cost of technology. Third, compare the studies from information from education, information from neuroscience, and then try to see if they aligned on three different levels. One was the order of skills introduction. Second was the estimated age of a student's ability to be able to perform those different tasks. And third, regrouping by competencies, the knowledge skills and attitudes needed in four broad categories. And one was in the physiological field. The second was in cultural, social, emotional aspects of learning. Third was in general cognitive abilities. And the fourth had to do with domain specific, which is limited in this study to math and language. Fourth, I adjusted the existing learning trajectories that are recommended in the educational literature to be modified based on information from neuroscience. And fifth, tried to create a real functioning tool, which ends up being a rubric for observation by early childhood teachers. To understand these steps a little bit better, I'm going to go into a teeny bit of detail trying to compare what is said in education versus what is said in neuroscience. So in early mass education, we have the difficulty that national standards in general begin more or less from about six years of age onward. We don't have the one-year-olds, two-year-olds, three-year-olds. We don't have a lot of specifications of what should happen in those contexts. The information that we have about recommended programs or what are best practices or what is real quality in early childhood education has a lot more to do with affective standards or self-regulation, general cognitive abilities and executive functions, as opposed to specific domain area achievement, for example, in math or in learning how to read or write. What does exist, however, are learning trajectories. And this is something that's built up over the past decade to have quite a lot of literature behind it to explain the hierarchies of learning competencies in distinct areas. For example, in math, some of the most prolific writers are Clemens and Sarama who have come up with a general learning trajectories model. And this includes 13 different aspects of early childhood learning related to math. For example, how a child learns to count over the span of one to eight years old or how they learn to compare and order information or to recognize numbers and subsidizing how do we compose numbers or add or subtract or multiply, divide. How does a child learn how to measure or form geometric shapes or to understand or develop their spatial sense and understanding of motion and then to be able to compare physical shapes. And then more broadly, an understanding of patterning and early algebra and then classifying as well as analyzing data. All of these different things were considered in a learning trajectories approach and this was back in 2004. In 2009, there was a study conducted to actually see the results of such an application and it was found that there was a very significant improvement in learning for children who did follow a learning trajectories models. So what we can find is that conclusions from an educational review of the literature and what is recommended in this example of math, for example, is that basically Piuget was right. It's great that there's still quite a lot of strong evidence in favor of a constructivist design or learning trajectories, which actually create a hierarchy of skill sets that children need to learn. The second big conclusion that can be drawn from this literature is that starting earlier is better and we know that in all areas, not just in this math example that we saw, but there's also substantial and robust literature in language, second language is music and sports that says that the earlier a child begins to rehearse certain skill sets, the better off they are. But this is also true in the other three domains that we talked about. Physiologically speaking, the earlier we can detect problems with hearing, vision or other disabilities, the sooner or the quicker we begin intervention, the less long-term problems that the individual will have. We also know that sensitizing children earlier to cultural, social, emotional aspects of their own learning and of their environments also leads to better learning outcomes from that perspective. And also general cognitive abilities, improving memory, attention and executive functions starting in the earlier years yields better results than beginning to work on these skills after a child has entered schools. We know that these two big ideas, constructivism and early starts, you can say with a lot of confidence this is something that we should be rescuing from existing current structures and then trying to complement them with information from neuroscience. So what do we know from neuroscience? We would hope that they would tell us how math occurs in the brain, but really what they're only showing us is where math is in the brain. So early studies, studying more or less around 2003, we have some great information from Stanis Le Sehani, looked at something called a triple code where he found that the concept of, for example, the number three is actually stored in different pathways depending on whether or not the three is written as an Arabic symbol, as a word, or as an object. So we know that there's different neural pathways related to categorizing numbers in the brain. We also know that mental math processing, for example, adding or subtracting in your mind is located in pretty much a specific area if and when these skill sets are refined and it's done well. You'll see, we'll see a little bit later what happens when it's not done correctly or when there's atypical development of the brain. We also know that there's studies related to magnitude, size estimation, is this set bigger than that set? There's a lot of great studies that have been produced by Kant-Langen colleagues in Piassa. Jacobs have done quite a lot of studies that thankfully these multiple studies are showing similar pathways. So we're not far off and we can have great confidence in the information that's being produced because the studies have been replicated enough that we can actually see that these particular areas of the brain create pathways that are vital to, for example, a magnitude estimation. There's also studies that've been done on numerical cognition, on multiplication, spatial manipulation, and all of these things, the conclusions that can be drawn from these types of studies is, once again, Piaget is still right. We're looking at a different kind of constructivism and this is called neuroconstructivism. One of the studies that actually summarizes this very, very nice and neatly and elegantly is Sir Royce's work in 2008 that basically tells us that, yes, in the brain there are certain types of basic pathways that need to be structured before we can do higher order thinking. So we know that there's an orderly way that the brain is actually craving to learn information and that can actually lead to more efficient learning because we've actually created the fundamental structure upon which we can build more and more complex types of learning. This is very interesting and we can also see this in brain scans and when we look at novice to expert changes in the brain when somebody is learning how to read, their brain is super active all over the place but once they've learned to read it becomes much more refined. Similarly, when they learn a new aspect of mathematical thinking, their brain is very, very disorganized in the way that it's looking for answers but once it's refined itself and has a higher level of understanding of the information where you're able to see more refined areas actually incorporated and not such a haphazard, let's try this angle, let's try this other angle. This is really, really important to consider because this can actually lead to this general presumption that a careful sequential ordering of a hierarchy of skills can actually contribute to a better understanding of information at least in math and language and by better I mean it can be probably more efficient and probably less painful to children. If these core ideas are introduced and those concepts are understood well before a child moves on to higher order learning. This is a real big problem, especially with things like math because there is a very clear hierarchy of order of introduction of concepts and what tends to happen is some kids miss out on some core concepts and then we try to teach higher order concepts and they don't have a strong foundation upon which they can build this new learning. This point is emphasized even further by other types of studies that have to do with the effects of stress on learning. Math anxiety, for example. Math anxiety takes over your brain and the level of stress that children can experience and that will actually cut off their abilities and this has to do with neurotransmitters in the brain, it doesn't have to do with just a psychological idea, it's a physiological thing. It will impede new learning of concepts. So we know also from other types of studies from John Hattie's work, a student's own self perception of himself as a learner has a great influence on whether or not they actually do learn. So if a child experiences failure, for example, in math learning, reinforces idea that, oh, he's no good at this, I'm not able to do this and this is a self-fulfilling prophecy. So we have to get our heads around the idea that there are a lot of things that we have to look at not only in the cognitive realm but also in the affective realm when it comes down to early childhood learning processes. So all of this information is important but it's pure conjecture because while there are a lot of studies about math in the brain, there are very few studies on math in the brain and education. So none of the studies that I just showed you here actually tell us how to teach better. So this is where we're having to make this big leap where we're connecting the two pieces of information. So when we try to bridge the information from education to neuroscience, how can we take what we know and make a better model for them what we have right now? What we tried to do in this particular study is to look at the distinct neural pathways that exist. We know that there are networks and there are pathways and then there are brain areas that are related to those different parts and to look at those different networks and to see how they can be reinforced over time with different types of practice. And so basically the schematic breakdown of the study that I looked into is that whereas in education you might consider language, this big domain area that we presume that children are going to learn within our formal school structures, right? But language is broken down into several different pieces. It can be reading, writing, speaking, listening. There's a lot of sub-pieces there. But if we take one of those sub-pieces which would be a general network and we have reading, then we have several different pathways that are related to that. So in order to be able to read well, you have to have word recognition, you have to know spelling, you have to understand intonation. And each one of those things breaks down into different observable behavior. In order to be able to have clear word recognition, a child can sound at a word based on basic phonemic understanding, that would be an observable behavior, right? What we have here with the pathways then would be actually how our brain is connecting all of those sub-pieces here in a physiological way. We can actually see this in different types of brain scans when we actually see somebody learning a new word. So for example, during word learning there are documented circuits, for example, this is by Davis and colleagues showing how the word is perceived through an auditory pathway and then how it is actually reviewed and checked for semantic understanding and then comes back and a child can actually say, okay, I can read this word, I know what it means. So when we talk about being able to get from this general global idea, what we have typically done in education is talked about these academic domains, these sub-areas, and then we jumped to the conclusion that we can have observable behavior. What this study has tried to do is add on these neural pathways that are important in actually looking at their hierarchy of what needs to come first, second, and third in order to suggest a correct order for introduction of those skill sets. Many of you are familiar with the terminology of a synapse. These are the connections between groups of neurons and the connections that are made. They are create neural pathways, these pathways. Then when they're grouped together we'll create a network in the brain. And this network in the brain is actually very important because when we talk about things that we want kids to do, we want them to be able to read, or we want them to be able to understand quantity, or we want them to understand patterning, it's not as simple as that. It's not a single thing. There are many, many multiple pathways that have to contribute towards the building up of that particular network. And all of those things to make those new connections need different types of activities in our classrooms. So this means then, if we go back and we've revisited our original hypothesis there's an unnecessary level of children's failure and we also see that kids are not interested in the classroom settings many times because they cannot grasp or because we've skipped steps for them, they're not getting those big ideas. So this means that there's a shift in the order of introduction and or in the age of introduction that needs to occur in order for our curriculum to take hold and to be more effective. So how do we do this? We can link the science of education to neuroscience through a trajectories-based or a learning hierarchies model. Then we would have a new way to approach early childhood education in a form that would probably be more efficient. So what did I do? I laid out information in basically what would be a developmental stage related to school ages, what the equivalency is from zero to six years old, the different factors that are involved and then the network that was being described by the pathways here. Now the pathways were basically broken down by neuroscientific evidence, whereas the educational criteria, what is this observable behavior that would match to this? What would that look like? So we looked for multiple areas of the brain that were stimulated in order to define these new pathways and then we looked at multiple sources of information that confirmed that yes, these are descriptors that would be acceptable to say that a child has actually achieved that milestone of learning. So this created changes to the past models that we have related to trajectories of learning in two different ways. Mainly, there were some slight changes that occurred in the order of the skills and also it created a different type of grouping of skill sets that might be more palatable to teachers. In general, the main changes that we could see is there was actually things that we had considered higher order understanding and things we have to do when a child is much older could actually be introduced far earlier because there was a natural sequence of learning that was being ignored in general early childhood classrooms. So the basic monoline proposing here is based on 16 neural networks. This is specifically the mathematical explanation. There's another video related to the literacy explanation. And these were divided into four broad categories. This has to do with the physiological aspects of learning, the cultural, social, emotional aspects of learning, the general cognitive aspects of learning and the cognitive domains. And I'm afraid that we don't have enough time to go into a whole lot of detail, but just to give you a sense of how intricate and how detailed the information is, we know that there are distinct neural pathways for example, for determining pitch, then there are from tempo and tone and prosody and loudness and background of foreground music.  So there's a lot of sub elements to just hearing that we need to take into consideration to ensure that our primed well for children to be able to learn early math skills. In the second category, there are things that are related to cultural, social and emotional aspects of learning that have to do with the greater broader context that a person lives within. And also what's going on inside of an individual as far as the relationships and their contact with other individuals. So there are general cognitive skills that are needed for all learning. If you don't have memory, you can't learn. If you don't have a tangent, you can't learn. So these things are fundamental aspects to learning as are executive functions, which have to do with your ability to inhibit other information and to think more flexibly in a cognitive sense. We know that there are aspects of mathematical learning that have to do with quantity, initial number sense, that have to do with coding of symbols, as well as understanding of symbols in different forms. We also understand that a child needs to understand a concept of order, ordinarily and cardinality before that they can learn higher order math. They need to be able to decipher patterns and understand how patterns are formed. They need to be able to understand categorization and classification of information, as well as the relationships that exist between numerical quantities as well as numerical symbols. All of these different aspects review them in the context of neuroscience, psychology, and education, and confirming them that we had physiological, social, emotional, and cognitive elements taken into consideration. So in reviewing the literature, which was nearly 2,000 different studies, we come up with this idea that hopefully can translate now into usable knowledge. We converted the established neural pathways into observable behavioral indicators. I decided to divide them by age because this is the current situation that we live within, divided by a hierarchy of skills within the age group. So what does this look like? Basically we have a developmental stage, the typical age range. Which of the four areas? Is this physiological? Is it emotional? Is it general cognitive? Or is it domain specific? And then the network that's being stimulated by doing this. And the general description, which would be the pathway that was followed. And then how does this look in a classroom? So the idea would be to decide whether or not a child has, well, there's no real evidence that this is occurring or he has a beginning understanding of this or he seems pretty proficient in this or he really dominates the skillset. He is able to do all of these different things. So I broke down all of the 171 indicators based on these 114 pathways, which were based on the 16 neural networks, and laid them out in a rubric form. This has us at a benefit of being done in a backward design concept. So where we have a clear objective, this is what we're trying to achieve. We want to know if kids can actually classify different characteristics because we know that it's a fundamental base for later learning of different types of mathematical skillsets. So here is my evaluation criteria. And then now it leaves the teacher very free and creative to actually come up with what types of activities then. Can you do in the classroom that actually give you this kind of evidence so that we can measure progress towards that objective? There's an added advantage of using backwards design structure, which is actual differentiation. Once we have those clear objectives designed and the evaluation criteria set, this means that we can differentiate the activities that we do in class in order to measure the way a child reaches that particular objective. So the main benefits of this change in this model have to do with precision. It's based on science. It respects this hierarchy of complexities of the learning trajectories that have been established. It responds apparently to how the brain organizes itself as far as neuro-constructivism is concerned. It appears to be a university applicable. It's based on the union of studies that are found from around the world. So it does not have this cultural bias that it's only gonna work in American classrooms, for example. And it offers improved precision with which one can identify gaps in a child's knowledge. We're able to remediate those skills with a better precision as well. Final benefit that I would say is that rubrics are familiar to teachers. It's not a new tool. This is something any teacher could actually apply and use. And it offers a more precise diagnostic of where a child actually is, permitting a teacher then to create activities that remediate any types of skills that might be missing. The implications of this model at a teacher's level, it actually has us rethink some of the activities that we're doing within our classroom setting. Okay, not everything is beautiful and rosy. There are criticisms of this structure. And there's at least six that I can identify. One is that as the focus was on specific math and pre-literacy skills, the general cognitive abilities were not pulled apart as far as they needed to be. And so they were clumped into general age groupings. We know that those things can actually be refined further and they need to be refined further. Second, we know that there are different types of tools. So we're mixing and matching the tools that are used, for example, in neuroscientific studies. We don't know if other types of imaging would yield different types of results. So that's something I'll also explore. Third, the vocabulary that's being used in neuroscience can be quite confusing on two different levels. Depending on if the study's coming out of Europe or out of Asia or out of North America, there's different coding schemes that are preferred. So some people are still using broadman's areas. Other people are using the desk in atlas and other people are using physical placement or areas of the brain. This leads to the second problem is that oftentimes when an individual, when we list these areas of the brain that are being stimulated, it looks like quite a long list, but it actually could be reduced probably about 30% in each case when we eliminate the repetition. The fourth, we're mixing and matching observation, observational studies with neuroimaging studies. And so there's a big doubt here about the ecological validity of somebody lying in an fMRI machine, responding to math questions lying on their back versus sitting in a class or pencil and paper answering of those same questions. So I would have a dream that someday we would do something similar to what John Haddy did in education and be able to compare these rather distinct looking studies by methodological form in a united vision. But up to the current date, there is no such thing. And fifth and finally, while priority was always given to studies that looked at small children zero to six years old, they're very far and few between. And that, and we also know that a lot of the information has to do with damaged brains, things that, you know, this piece is missing. Therefore, we think this piece is important as opposed to actually confirming that in small children, this area of the brain is actually used. That is still something that we don't have. These types of criticisms were echoed. For example, by answers work, we don't have very many studies of kids under six years old. So that makes us question the validity of much of the literature that we're looking at. And the second point has to do with over generalization of the findings. There are a lot of promises related to the cognitive neuroscientific contributions towards mathematics learning. So we have to actually be able to take all of this with a grain of salt and not try to extend the findings beyond their true limitations. And there are several right now we have to look at. I have to remain totally optimistic that on the balance of things, despite all of these potential criticisms and the problems that might occur within this rubric, the combination of this information is yielding a better tool. And it seems evident just from the preliminary study that was done in Costa Rica and Ecuador that the typical teaching practices at least in our context in Latin America are not stimulating as needed those neural networks. And finally, I would have to say given our original research questions, to what extent really are teaching practices, actually taking the best of this information and being able to apply it, at the least it is highly probable that some parts of early math failure are due to an incorrect stimulation. These are activity class or an incorrect introduction at age level, appropriate of information related to math and early literacy skills. So this creates a lot of new opportunities. The tool hasn't been tested yet. So we need you to help out using the rubric in your own context. We might find that some of the neural pathways are missing. If we disaggregate, for example, memory, attention, executive functions even further, could it be that it's gonna be a distinct neural pathways found there? So there might be things added here. While most of these pathways are identified in multiple studies, they still could be wrong, right? So we need to confirm and continue to nurture our information with new studies that continue to come out. While the pathways might be correct, maybe they have been placed in an incorrect age group. And finally, we don't even know whether or not teachers are going to use this rubric. Is a rubric that has all of these elements too complex? Is this too much to ask a really childhood teacher? I don't think so, but it's something that might come up. And the final thing, which I think is the most creative element of all of this, is that there's no complete list of activities that we might suggest and test in the classroom to actually see if we are able to stimulate these different neural pathways in the correct way to actually create a better foundation for children to have math success in the future. So what are our next steps? We need to apply the rubrics. We need to reflect on changes in teaching practices that might be new, different adding or subtracting different types of activities that we already do in class. And then we need to, after we've done those first two steps, come up with a recommended suggestion of good practice. So I invite you to join me in doing this. I think that the real legitimacy of any of these tools has to do with its cross-cultural applications. So if we can find friends in different parts of the world who are willing to apply this, I think that would be a great next step. Thank you very much.


So the other day I received an order in my dropshipping store and one of my biggest fears when it comes to dropshipping came true and that was that I accidentally sent the order to the wrong customer. Now it might not seem like a huge deal but I lost both money as well as the customer. So for one, I had to replace the product and two, the customer had to wait an extra couple of weeks just to receive their item. Safe to say that that customer did not have much of a friendly experience, I tried to make it as best as I could for them so that way they wouldn't be upset but at the end of the day they did have to wait double the time than they usually would have to. So in this video I'm going to explain to you how you can avoid that mistake. In this video we're going to be talking about dropshipping fulfillment but first let's run that intro. What's going on everyone, Mark here with Out of DS and if you haven't done so already please make sure to hit that subscribe button as well as ring that little bell notification so you don't miss out on any future videos. On this channel we cover anything and everything dropshipping related starting from some of the best products to dropship all the way to some of the best tips and tricks to help you succeed. So if that's something that interests you just make sure to subscribe. Now if you want to learn a little bit more about order fulfillment whether it be dropshipping or in-house order fulfillment then just go ahead and check out the description down below. There is going to be a link to a relevant article down there. So today we're talking order fulfillment specifically in the dropshipping sector. Now before we get into that the first thing that we need to do is understand the difference between in-house fulfillment and dropshipping fulfillment. What's the difference? So in-house fulfillment is pretty much you packing up all of your different orders printing out the shipping labels packing the items and then taking them either to UPS USPS or whatever service it is that you're using to fulfill and ship out your orders. And when you do these things manually and you actually ship the items out yourself you're carrying inventory. And overall this gives you quite a bit of margin for actual human error. So you do have a higher chance to commit an error and potentially like in my case send the order to the wrong customer. Of course my end it was with dropshipping but it's the same concept. You mix up to shipping labels or you mix up a customer and the product ends up in the wrong hands. It's happened it can happen with both in-house fulfillment as well as with dropshipping fulfillment. Now ultimately in-house fulfillment does end up being a bit more challenging. It takes a lot more time and at the end of the day it's a lot more work. So for one unless you're extremely organized or maybe you're paying for some sort of software to keep track of your inventory. Inventory management itself is very challenging. It's really easy to lose track of one or two different items and throw off your whole inventory and accidentally keep selling items that are actually out of stock. Aside from that when you do carry your own inventory you also sometimes have to pay for overhead costs. If you don't have anywhere to store these products in your house or maybe your office you're gonna have to rent out a space or a warehouse to be able to store all the extra inventory. So that ultimately adds up to your costs and cuts into your profits. Now with dropshipping fulfillment it's pretty much the exact opposite. So for one while you do have a small margin for human error it's not as large as it is when you're doing things in-house. When you're doing things in-house you have too many things going on and again you have a higher margin for error. Also when it comes to the dropshipping fulfillment you don't have any overhead costs since you don't have any physical inventory you don't have to store anything you don't have to pay for storage you don't have to take up space in your garage or in a corner of your office. And at the end of the day when you're dropshipping and fulfilling your orders it's gonna be a lot quicker for you to get these things done and actually process your orders versus in-house fulfillment where you actually have to pack everything yourself.  The supplier's gonna take care of the rest and you can just cake back and reinvest the time that you have extra back into your business. Alright so now that that's covered let's go ahead and cover the dropshipping supply chain. So this is to help you understand who you're sourcing from who's actually shipping out and fulfilling your orders and why sometimes some shipping and processing times are longer than others. So in the dropshipping supply chain there are three main players. There's the manufacturers, the wholesalers and then the retailers. Manufacturers are typically going to be the ones that actually create and produce the items. For the most part you're not going to be sourcing your items from them they usually end up selling their items in bulk to wholesalers. Now that doesn't mean that you can't purchase from them. You are able to buy from them but you're not going to be able to buy one item at a time and dropship it that way. What you have to do in this case if you want to purchase directly from a manufacturer is place an order in bulk. So you're going to have to order a large quantity of a single item and you're going to have to keep them stored. Now remember when we talked about in-house fulfillment this is where in-house fulfillment comes in. So you can make the purchase, keep it in-house and then fulfill the orders as you go but that's not necessarily ideal because it leaves a big margin for human error. Now the other option you have whenever you want to purchase directly from a manufacturer is getting a dropshipping agent. A dropshipping agent can help you by being able to store these products for you and then whenever you get an order you just contact your agent and then they'll fulfill it for you. But when you're doing it this way at the end of the day you're still purchasing in bulk. So you do have to keep inventory and you are going to have some overhead costs. Then after the manufacturers you have the wholesalers. Typically the wholesalers are going to be the ones that sell the products either to retail stores or to dropshipping stores or dropshippers such as yourself. And finally we have retail suppliers which at the end of the day are going to be the most expensive but you're going to be able to purchase practically anything without any minimum quantity or without any problem. The only thing is is that you are going to have to hike up your prices quite a bit between the three different suppliers. The best one that you can ultimately go with is going to be the wholesale supplier for the most part they're not going to have a minimum order quantity and they are going to be dropshipping friendly. Now let's talk actually fulfilling your orders. Let's talk about order fulfillment and how you can get started doing that. So for one you have the manual way of doing it and this is one example of how you can fulfill orders manually. So let's say I received an order for these big belly wooden cups. Now the first thing I need to do is locate my item with my supplier. So as you can see I have it here and I have to check to see which variation did they order. Did they order color A, B, C, D or some of the others. I got to check that really quick hold on. All right they ordered style B. So now that we have that there all we have to do is click on add to cart and then view our shopping cart and then we have to proceed to the checkout. All right so once we're here we select the item that we want to purchase and then check out. So the next thing we have to do is instead of shipping it directly to our address we have to ship it to our customer's address. So this person is Jane Doe. Their phone number is 456-789-29 whatever. Address is going to be 1234 Main Street. That one right there. Let's go with that one and then we have to click confirm. So we're going to add that as new address and we're going to ship there. All right. So once we have this here we all we have to do is click on the address itself and then that's going to be the address that we ship to now from here all we have to do is actually check out. So put in our credit card details, pay through PayPal whatever form of payment that we're using. That's what's next. Now you can see how there is room for a human error. If you're typing everything in manually then you have the potential to either write a two instead of a one or a Northwest instead of a Southwest or even input the wrong customer's details like I did. And then that's when your problems start and you have to start explaining things to your customers and offering refunds or exchanges and it can potentially become a huge mess. So the other option that we have to do this is the automated way of doing it, which is my personal favorite because it pretty much eliminates room for human error. Another thing is that while this did only take a few minutes, it really didn't take that long. If you're only doing one or two different orders, it's perfectly fine. You know, it's not going to take that long, but once you really start scaling and you start getting maybe 10, 15, 20 different orders, it's going to take a long time for you to just place orders. You're going to spend maybe one or two hours checking how and that can get really boring really fast. Now with automatic orders, the risk for human error is practically eliminated. So what ends up happening is let's say you're using a software like out of DS. What ends up happening at this point after somebody places an order is out of DS. The system itself is going to take that order and it's going to log into your supplier's website using your customer details. So it's going to use your login details to log into let's say Ali express afterwards. It's going to use your credit card details or your banking details, whatever you have programmed to make the actual purchase. Once the order is fulfilled from your supplier, then out of DS is going to update your order and market as shipped. Then once your supplier provides a tracking number, then out of DS will take that tracking number and supply your customer with it as well as that, you know. So as you can see, it's very easy. It's completely automated and you really don't have to do anything whenever an order comes in. It's automatically fulfilled for you. Hence the term automatic orders. Now, how do you activate this? So for one, you need to be signed up to out of DS. And if you want to try that out, you can sign up right now for just one dollar for the trial period. Now, aside from that, once you're actually logged into the system itself and you're at your dashboard, all you have to do is go ahead and click on settings and then move on over to plans and add-ons. And then over here, you're going to see an add-on called order processor. Just activate that and you'll be able to start automating your orders. Now, aside from that, you also have the option to do fulfilled by out of DS, which actually has a lot more benefits. Check this out. Now, it's fulfilled by out of DS. It's pretty much doing the same thing that automatic orders is doing, except for the fact that instead of logging into your supplier's website using your account details, out of DS is actually going to log in using its own credentials. So out of DS has their own buyer accounts for all of the different suppliers.  So on a platform like let's say Amazon, that's not too drop ship or friendly. It can help you avoid things like having your account banned or suspended or restricted. Now, another benefit to fulfill by out of DS is instead of using your credit card details, it's actually going to use a balance that you top up. So you're not going to have to keep track of how much you have in the bank in order to be able to make these purchases. Everything will be displayed for you right here in your balance. And of course, if you want to add more to your balance, it's super easy.  All right. So now that we understand everything about order fulfillment and the differences between in house fulfillment as well as drop shipping fulfillment, I'm going to give you a quick list of some of the best drop shipping suppliers that you can use for your drop shipping business that has some of the best quality products with some of the quickest order fulfillment. And the first one on this list is CJ drop shipping. Now with CJ drop shipping, you have the option to do prints on the man products, white label products, and you have the ability to source new products. So if there's a particular product that you found on a different supplier, but you don't want to purchase it from there for whatever reason and you actually want to purchase it from CJ drop shipping, you can reach out to one of the suppliers on here and have the item sourced. Another option you have is Ali express, which is pretty much everyone's go to, especially when starting Ali express is great to test out different items because they have a little bit of pretty much everything. Some suppliers have quicker shipping than others, but that's something you're going to have to keep a lookout on the product page itself. So for example, on here, all you have to do is check out over here where it says shipping and you have an estimated delivery date. Now, if you click on that, you also get a few more options to see if you have some quicker options. I'll express also ships globally, which is a huge plus so you can drop ship pretty much anywhere you want around the world. Another great option on this list with a massive variety of products is Ali Baba. Now with Ali Baba, there are some suppliers that are going to be manufacturers. So you're going to have to look through the different sellers on here and differentiate the ones that you're able to drop ship with versus the ones that you have to purchase wholesale from. So pretty much differentiate the wholesalers from the manufacturers. Banggood is the next option on this list and one awesome thing about Banggood is that no matter when there's always some sort of sale going on. So every single time I sign on as you can see here, you always have either some new promotion or some new user benefit something that helps save you money. Now that actually makes sense because Banggood itself is a drop shipping website. So this website was made with drop shippers in mind. So pretty much anything that you source from this website, you're going to source at one, a low price and two, it's going to be drop shipper friendly. So all of the sellers on here allow you to purchase just one item so that way you can drop ship it. Now if you're a drop shipper in the home and garden niche, then Costway is one that you do not want to miss. Costway has an awesome 90 day warranty. So if there's any issues with an order, you have up to 90 days or three months to be able to claim it and actually get a refund or an exchange. It's fantastic. Aside from that, you also get free standard shipping and you get cash back on most orders. So with cashback, you're ultimately able to save some money and remember that a dollar saved is a dollar earned. Now sticking to the home niche, you also have Wayfair as an awesome supplier. Wayfair ships globally and they have some pretty good perks with their MyWay program. Now the MyWay program is pretty cool because as a member, you get exclusive perks. For one, you get free expedited shipping and two, you get access to exclusive products, which I think is pretty cool. You're going to have access to a line of products that people that aren't in the MyWay club don't have access to. So that itself is a big advantage because you're going to be able to offer unique products. And that is pretty much everything that you need to know about fulfilling your orders through drop shipping or through in-house order fulfillment. So I want to know which one you prefer. Let me know down in the comments below. Let me know if you like in-house fulfillment or if you prefer drop shipping fulfillment. Are there any tips and tricks that you want to give me regarding order fulfillment? If so, let me know down in the comments below. Huge thank you to everyone for watching, especially if you made it this far all the way to the end. It truly does mean a lot. If you found this video useful, if you found it helpful, if you found it enjoyable, please make sure you leave a like and while you're down there, just make sure you hit that subscribe button as well and ring that little bell notification so you don't miss out on any future videos. Once again, my name is Mario with Out of the S wishing you all nothing but success in your drop shipping venture and catch you all next time.


Mae ydych chi'n ochon gwaith. Mae Llyfrgell yn wneud, nawr nôl o'r pwg ac mae'n dwylo'n dwylo. Y pwg yn dwylo. Cymru, yw Clare Baker? Diolch yn rhoi. Felly fan hynny i'r app Arglwn Iddo, sy'n dwylo yn oeddenol. Mae'n rhan. Mae y pwg yn dwpl i gyd. Yn gyfodol o'r pwylo, On amendment 3617.2, in the name of Murdo Fraser, is yes, 47, no, 65, and there were no abstentions. The amendment is therefore not agreed to. The next question is that amendment 3617.1, in the name of Jackie Baillie, which seeks to amend motion 3617, in the name of John Swinney, on Covid-19 Scotland's strategic framework update, be agreed. Are we all agreed? The amendment is not agreed, and therefore there will be a division. We will now move to a vote, and members should cast their votes now. The vote is now closed. The result of the vote on amendment 3617.1, in the name of Jackie Baillie, is yes, 21, no, 94, there were no abstentions. The amendment is therefore not agreed. The fourth and final question is that motion 3617, in the name of John Swinney, on Covid-19 Scotland's strategic framework update, be agreed. Are we all agreed? We are not agreed, and therefore there will be a division, and members should cast their votes now. The vote is now closed. The result of the vote on motion 3617, in the name of John Swinney, is yes, 68, no, 46. The motion is therefore agreed. That concludes decision time, and we will now move on to members' business. I would ask those members who are leaving the chamber to do so quickly and quietly.


My name is David Cooper. I'm an analyst at the Economic Policy Institute. But before I studied economics, I actually worked for TIPS. I waited tables, I attended bar, I even parked cars as a parking valet. And in my experience, many people don't really understand how tipped workers get paid. Many people think that when they go out to their favorite restaurant or bar and they leave a tip, that they're just giving something extra on top of that worker's regular wage. In most cases, that's actually not true. Under federal law, if a worker receives at least $30 per month in TIPS, then their employers only require to pay them $2.13 per hour. This means that whatever that worker's getting in TIPS is really the bulk of their wages. Now there are seven states where tipped workers get the full minimum wage, and a number of other states have said tip minimum wages above the federal $2.13. But nationwide, in any other occupation, employers would be required to pay these workers at least $7.25 per hour no matter what. But for waiters and waitresses, bartenders, nail salon workers, barbers and hairstylists, it's really the customer that's on the hook for paying the bulk of these employees' wages.