Good morning, everybody. This is Donna Prosser with the Patient Safety Movement Foundation. We're here to bring you another COVID-19 update today. And today, again, are going to be joined by Dr. Ed Kelly from the World Health Organization. Dr. Kelly is the Director of Integrated Health Services in Geneva, Switzerland with the World Health Organization. He joins us every few weeks to give us an update on what's happening across the world. So thank you, Ed, for joining. My pleasure. So glad to have you again today. Well, it's good to be here, and it's always good to be speaking with the Patient Safety Foundation. We're basically in the midst, as I've said before, of the biggest patient safety event of at least our lifetime. So who better than the Foundation to be discussing these things with we head into the next phase of this experience? Great, great. Well, let's go ahead and get started. OK, good. Well, what I'd like to do is just as usual, just to provide a few sort of framing messages. You don't always have the same folks, at least I hope not, for these webinars. And the main point that our Director General and WHO has stated in this whole experience on COVID from very early on is that the strong health systems have been the way forward to lay the foundation for both global health security, but also for longer term efforts that on primary health care and universal health coverage. And it's really quite clear that that no other time was that more true, and that as we start thinking of what you would say in my current country of residence France, the world after COVID, that we really cannot be going back to business as usual. We have to rethink how we're doing things and try to do things better on the next slide. And this applies not just to socioeconomics and the environment, but definitely to health care. So I'd like to just outline a few things in terms of the happy updates and some updates from our side on key pieces that we're working on. And I know we have some questions already that have been coming in in advance of the discussion. So next slide, please. Just a quick update here today, 131,296 new cases in the last 24 hours. One thing that we have to be quite clear about is that we are not out of the epidemic. We've had some transitions in Europe, definitely not in other parts of the world. And for WHO, as of 8th of June, there are almost 7 million cases reported worldwide, almost 400,000 deaths. And in the last nine out of the last 10 days, we've had over 100,000 cases reported to WHO. And on June 7th, we had 136,000 cases reported in 24 hours, the single biggest day so far. So in contrast to how at least the media is covering things because for very good reasons, there's a lot of other things on people's minds right now. The virus is not only not going away, it's not even going down. Next slide. So this gives you the picture. You can see how things have moved. There is a definite change in terms of the epidemiology for in terms of deaths and what we see in terms of case fatality rate. You see case fatality. Next slide, please. If you look at some of the regional comparisons when we were looking at Euro in the European region, we had case fatality rates above 8% on average, where in the Paho region, including America and North America, but also younger populations in South America, you have something like 5%. But if you look at, for instance, just in Africa and Emero, you have rates of 2.4 and 2.3% in terms of case fatality rate. So lots of reasons for that. Partially could be reporting issues and how deaths are being captured and recorded, but also clearly much younger populations. So we'll have to see how this evolves. Next slide, please. So just in terms of providing a recap of our feeling on health systems, there's been an increased demand and really a disruption of services. And all health systems now still, and for an extended period, even if the burden has gone down slightly, will be balancing the demands of responding and treating COVID patients and never being sure if the next patient that comes through with a fever, cough, maybe diarrhea, and other symptoms is a COVID patient and a real risk to you and the rest of the patient populations in your hospital. Or is having any one of a number of other diseases, and that's the whole point of some of the guidance that we came out with. That people don't show up based on the WHO's organogram or based on our conceived notions that we're in the middle of an epidemic. So these people must be COVID, these people must not be. So this means that having guidance on how to handle that is really important. So next slide. We had, when we first came out, just a few updates since we last talked. We've come out with an updated operational guidance, detailed guidance that all of our regional directors asked us to produce. And just to remind people, WHO has six regional offices with elected regional directors who are very senior and important people and respond to member states. And when we came out with our original guidance saying, here are some programmatic things that countries need to do to keep health services open during the crisis. They said, this is great, but I need you to go back and give me detailed guidance for different populations and for different diseases. And so we've done that and I'll show you quickly some of the results of that. But we also have advice on the use of masks that was a lot of work, a lot of work looking at mask and mask use. And happy to answer questions on that. Also looking at, we're working right now on mitigating the impact of COVID in long-term care facilities. That's work based on some work done by our European office and also ongoing work on assessing the continuity of essential health services. But some of the initial data that we have showing that up to 50% of health services on average across the countries who are reporting have been disrupted. Next slide. So these are the three guidances that have come out. The first one was the programmatic high level. The third one that just came out on the 1st of June supersedes it. So it's a more detailed version. So we've taken that first one off the website. So if you have it, you can keep it as a limited collector's edition. Be happy to sign it next time I'm in the foundations gathering. But then we also have a community-based treatment, sorry, and outreach services guidance that we've talked about here before for when health workers, social workers have to leave the health facility and head out into the community to deliver services. How do you plan for that? How do you do it safely? Next slide. Just to give you an idea, I really won't go through it because it was such a massive piece of work across almost 20 or over 20, well over 20 departments and something on the order of 250 collaborators that the team worked with. But it goes through, first, more detail on programmatic considerations and systems level considerations for ensuring health services keep operating. And then what are the specific adaptations that each area needs to consider in order to deliver services? So next slide. So you can see this is the first part that goes through the operational strategies for maintaining services. And this gets into detail around actually selecting those priority services and governing those. Those are not easy questions to be made. Which services are you going to put on hold? Which will you keep going with? And there's a lot of good examples out there already. How do you manage patient flow at all levels? How do you surge and move around workforce? This is an important consideration, particularly in US and other places that are facing a struggle with fall off and demand for other services, keeping the workforce busy and keeping them optimally utilized. Then you have issues on central medicines and how you manage communication and information. But we also have a discussion around removing barriers, financial barriers to access. WHO has a position that during, in general, that we have to minimize. That's the whole goal of the UHC Sustainable Development Goal, is to minimize financial barriers, to access, minimize the number of families driven into poverty because of health care. And so during the pandemic, removing those point of care user fees and other financial barriers is really priority. And then we have some issues on digital platforms. And we can talk about that later. I know there's been some questions that have come up about how to maximize digital platforms and other tools for delivering essential health services. That was in our very first webinar that we did together. We talked about some of the examples from that. Next slide, please. So in the second part, we go through very detailed considerations across the life course, which is how WHO sees these different population groups across the life course needs and how you need to adjust for care for those types of populations. And then looks at nutrition, NCDs and mental health and what are the considerations there and adjustments that are made. And then communicable diseases across the main communicable diseases areas that we have in each section has a table that says what are the, what's the activity that's being undertaken? What are the specific adjustments? And then the third column is what are the sort of medium term kind of recovery or getting back to normal considerations you should start to make as you deliver services. So this is your roadmap that you should, everywhere we're promoting this across all of our countries right now. There's definitely parts of Europe and North America that could use this. Next slide. So we do have, it's, if you click ahead, yeah, thanks. That there's a detailed list of indicators and so when you share these, you can actually read them because they're very small but this gives you some idea of the types of things that we're asking countries to measure as they're looking at whether they are effectively keeping essential services running on schedule. And WHS collecting some of this information we can provide comparisons for those who would like to, to talk with us about more of that data. So next slide. I'll just finish with a couple of key points on, on data. This week we're really focused on data at WHO because some of the, as I mentioned about 60 countries have sent us back information looking at the, you know, how they are ensuring essential services. We've collected information on, on their plans.  Does it talk about essential services? Is there funding behind it? Then what are the, what are the services that are disrupted and the main causes and how are they approaching efforts? You know, what are their efforts to overcome some of these disruptions? What tools and techniques are they using? And then what priorities do they have for technical assistance from WHO and from other partners on this? And anyway, maybe next time we're just pulling it together right now. I had vague thoughts that I would slip a couple of slides in, but you know, as a current and former data person, you really don't like to show data until it's like completely cooked and everything's been firmly set. So also we're hoping to get a few more countries in because it's a little bit biased right now towards Afro, EMRO, we haven't gotten the Euro data. And so next time I come, we'll be able to summarize that. Next slide. So just to mention, without getting into all the detail, I have managed the IPC team for now a big chunk of time since 2007, but I do not count myself as an IPC expert and the amount of time that the IPC team has spent on this mass guidance, you cannot imagine. So honestly, when I took over the World Alliance for Patient Safety here, never in a million years would I think there would be so much attention on infection prevention, just in the regular, sort of your regular community, regular media that probably within about three minutes of this going up, I was getting, we were getting questions from all over the world about this that are other aspects of it. So people have been really waiting for this. There's been obviously advice from CDC and others that have very good information. There is, there are some key differences, but I'm happy to maybe I won't go into it now and we can talk about it afterwards, but basically some originally WHO still stands by the fact that all the evidence suggests in our experience with previous coronaviruses and with SARS shows that mandating masks for healthy populations, i.e. just generally in the community, really, I mean, there's not so much harm except for the fact that people tend to not know how to put on and take off masks and they tend not to wear them correctly, wear them too long, not have them of the correct materials, that's not great, but on average, they really don't anyway help with community infection rates. But in this environment, this mask guidance does make the distinction that at-risk, particularly at-risk patients, elderly patients, others, people with other conditions, if you have to be in crowded settings where you're not able to really physically distance a mask may be a good idea. So there's other key points in there, particularly around materials for masks and we have a bunch of great videos explaining this on our website. So for those of you like me who would rather watch a video than read 10,000 words, you can check that out. Next slide. So I think that's basically it, that that was really the key pieces that I wanted to summarize. And I know this time to leave a little bit, a little bit more space for some discussion. And I know we always have great folks online, so probably you'll get as much information in the chat as you will from listening to me. But anyway, thank you very much, Donna, I'll hand it back to you. No problem. And we have a couple of questions coming in if you can stay for a few more minutes with us. Great. Excellent. The first question is related to the data that you showed suggesting that potentially age is a factor in death from coronavirus. Do you have any data about overall wellness? In other words, in the countries where they have a universal healthcare system where access is not as much of an issue as in other countries, are you noticing any trends? Yeah, that is a very interesting question. I mean, the bottom line is no, we haven't really looked at that. We've been doing deep dives on, we've had several different methods for doing this. We have the epi team here, who's the epidemiology team for the emergencies program, has been doing deep dives, looking at a number of factors, looking at in particular sort of testing strategy and also age structures. We have started to look at, and maybe we'll be able to come back the next time. If people looked at, I think it's a very interesting report and not just because I helped write it, but in 2019 for the biannual UHC monitoring report, which is a report we have to produce the WHO leads on, but does with the World Bank for the SDG 3.8. And this was produced for the high level meeting, the UN high level meeting on universal health coverage. If people remember that September 2019, back when we used to be able to get on planes and trains and get together in big groups, that it put out numbers on the UHC index, which is a set of 16 indicators across different service areas, and then also looks at financial protection and it doesn't rate countries, but actually you could look at that as a measure of universal health coverage. So we're just starting to look at that and how that compares with infection rates and death rates at the moment. There's no really honestly clear pattern at the moment, but because particularly for death rates, the folks that are quite weak, you have four types of countries on UHC, ones that spend a lot of money, but have decent coverage, ones that have very good coverage, but get much more value out of it, don't spend so much money, and then those that aren't able to spend very much and are still able to get decent coverage, and then ones that are really having trouble. And obviously the ones in that last one, the quadrant four are your weak countries, your Yemen, where the health system really collapsed. The people have been following Yemen. There was a pledging conference for Yemen just recently this week. It was very, and we didn't quite get as much as we were hoping. They're really, really struggling very high rates of infections. It's a little bit different than some of the other countries we've seen. So, but death rates have tended to be low in those countries because one, there's a lot of background, other background mortality that may be a classification issue. And then there's also that's a young population. So there's a lot of other factors going in there. But anyway, hope to come back next time and have a little bit more information on that. That's great, thanks. I wonder if you can tell us a little bit about MISC. This is the new inflammatory presentation that we're seeing in children. Do you know much about that in terms of presentation and treatment? Yeah, we have recently put out, and I'm happy to share, in, it's at some point after the webinar, the recent brief on this where we had, and we've got new case report form that we're looking at in terms of this particular multi-system inflammatory syndrome. We've got some information that we've put together that could be useful on this, but in terms of the work that W. Joe has done, it's been recent, it's one of these things that the overall message, I guess, is that it is really quite rare. And the vast, vast majority of how should I put it, of cases don't occur in these patients. There's definitely more information that's needed to be studied on this. And I think that there's some, clearly a mandate for examining this and collecting more information, but right now W. Joe just really doesn't have so much information that's gathered on the topic. And we can send some of this information, once we come to the end of the session here, I can give you a send the link for the recent brief that we've had and what country's doing on this. UNICEF has also published some information on this as well. And I think it's clearly something we should watch, but the bottom line is it's a very, very rare condition and the exact sort of progression from a mild infection to this is really not as of yet understood so well. So I think we really have to gather more data on it. Yeah. Well, it looks like Zandiel is on the ball. She's got links already in the chat box for that. So thank you to Zandiel. In terms of the information that's there, the number that we have now that we are looking at, number of them also have some underlying conditions. And I think that that's one of the things that we're looking at, the features are similar to what people probably heard of Kawasaki disease and also toxic shock syndrome. But the fact that it could be related to COVID is at this point even not sort of definitely sure that it's related to COVID. So and usually these kids are treated with anti-inflammatory treatments, steroids, other treatments like that. But I think understanding exactly what this is will be really important. We do have on the website a case definition that is, anyway it's basically in line with CDC's case definition as well and CDC has some good information on its website also. So once we have more data from the clinical platform that we're collecting information on, we definitely would like to come back to you. And for those of you who are providers or hospital managers who would like to participate in that clinical platform, not just for this reason but for a whole host of reasons in terms of staying on top of some of the stuff WHO is tracking, we can definitely provide you with that information. Great. Another question is related to some of the guidance strategies that you shared before that in some cases are regional. Do you have any recommendations for folks about how they can prioritize these on a local level? How they can prioritize? Some of the guidance strategies that you had discussed. Yeah, I think that on basically some of the strategies, the WHO has come out with in general have the sort of their applicability is only is sort of malleable based on the structure of the health system and how decisions are made. So in some countries decisions on public health considerations and some of the public health measures are made at a national level. Some of them are made at a state or province or region level and some are even made at local levels. And so similarly, the guidance that we've come out with on essential health services, we tried to provide that that's why we have sort of part one which is the kind of systems level or programmatic management considerations that are there. But all of those sort of detailed adjustments are really about what you do at a health facility at a hospital, what you do when you're a campaign team heading out to do immunizations or to go out to visiting on social service work or other outreach work. So the majority of the essential health services work that we have produced has really been about local decision-making. Now, I think that this is one of those questions that country context really makes a big difference. Your own context makes a big difference. So how you operationalize those are some of the lessons that we're trying to collect. We have a new learning platform that we're gathering information on and would be great to connect with the foundation and see how we can gather some of the lessons learned from how people are making their own adjustments and sort of best practices, whether it be in long-term care, hospital care or primary health care in particular that would be really useful. Great. We do have a question about long-term care but I knew that you mentioned that you have some information coming out about that. So we actually have several questions and only about five minutes left. So if it's okay, I'm just gonna pick just a few of these and then perhaps we can send these questions to you over email and then we can share them with our network later. Great. Okay, excellent, excellent. Question for you about the increase in cases that we're seeing, especially here in the United States, you've seen a lack of social distancing most recently, both with economies opening up and with some of the protests. So do you think that there is a, what do you see is gonna happen in the next few weeks? What are you guys thinking may happen with the peaks? Yeah, I've been asked this by many people from like my church council where, anyway, I'm on my church council, I hope that makes you think better of me and that up to sort of some of our national counterparts and the bottom line is like, honestly, WHO has good ideas about it but really no one knows exactly what's gonna happen but at one level, like I've said before, most countries, even now in Switzerland, it's probably only 7% of the population has been exposed. Most countries are no more than 10%. So that means that really from most communities, they're not that much different in terms of being magically immune to this virus than they were before the sort of rise in cases started. So most people are still vulnerable. We seem to have, how shall I put it, kind of come to the point where corresponding to the very understandable move away from the closing of businesses and schools and other things like that, the quote unquote lockdown that countries for lots of very good reasons have to restart. So what needs to happen though, is for a period of time until the vaccine is available to us and I think that's something that's really important. We will have to be maintaining good testing and robust testing approaches and good contact tracing. Those will be our two key tools in managing this. And then the third tool will be the public health measures and really trying to maintain and make hand hygiene stations available as much as possible, managing the numbers for any given interior space, whether it be work or school or other places. And then really depending on the communities to understand that things haven't magically ended that they still need to be responsible even if they're able to have a bit more freedom. So I think the recent big, big gatherings and it's not just the US actually, there was huge protests in France over some, there have been some recent threatened closures of because the difficulties with the economy on different factories, et cetera, big community gatherings and not respecting physical distancing. And so those are basically the definition of super spreader events. So I think we'll have to keep an eye on this but a number of countries have big spikes, bigger spikes even than the first. So for instance, like Iran and then there are countries that have just continued upwards which includes Philippines is another country with a bigger second wave into Bangladesh but France and Switzerland have also seen some increases. So we will likely see a wave like this as we go along and there'll have to be some adjustments as we go. So I think this is something that we're obviously in this for the medium term and there needs to be an approach that manages the opening up but also does it responsibly. And then one more question that's a little bit related to that. As you probably know, there was some confusion yesterday when the World Health Organization was indicating that there isn't that much of a spread from asymptomatic carriers as we had originally thought. I think, I believe that she clarified her comments. I wonder if you could help to clarify that a little bit more about the transmission from asymptomatic carriers. Yeah, we've talked about that with the mask guidance and people should check that out. It's a little bit more detailed in there but it's definitely something we're tracking all the evidence still suggests that it's quite, that it's still relatively difficult to get that people do shed the virus but it's relatively difficult to get it to be infected by an asymptomatic carrier just because the ways, the coughing and other ways that they would leave traces of the virus or expel traces of the virus are much more limited. It's definitely not unheard of particularly and a lot of the asymptomatic has been within families so they're sort of, you know, they're obviously in shared space much more frequently. So, but it's relatively difficult still from what the evidence suggests for an asymptomatic person to casually infect the casual contact rather than a close contact. I appreciate the clarification. I know that it's been all over Twitter, hasn't it?  Yeah, well, thank you very much and I always appreciate you joining us. We're looking forward to having you back in a couple of weeks and in the meantime, we'll send you the questions that we weren't able to get to today and we can send those out to our network separately. That sounds good, Donna. I know there were some questions that people had about elderly parents and our relatives and we'd be happy to talk about that. I think it's a big challenge and I'm hoping that our long-term care guidance is gonna help for some of those congregate facilities but also managing, you know, long-term care and home care obviously doesn't happen just in facilities. So you have to, bottom line is figuring out ways of managing the exposure and potential, you know, minimizing infection. Basically, everything is doable and especially things have to be doable in order to keep people's sort of mental state and spirits and their livelihoods going but it has to be thought through and done deliberately and done every single time. That's the bottom line with infection prevention. But anyway, we're happy to take those questions offline and we can write up some work and put anyone in contact with some of our experts. That's fabulous. Well, thank you again so much and we'll see you in a few weeks, I hope. Sounds good. All right, take care. Have a great day. Bye-bye. Bye, everybody.


of his podcast, Steven Wonder Boy Thompson, who is more than generous with his tips on his diet. Okay, so let's get down to some statistics after we study the weight classes. Let's just go into the definitions. Five, six, seven, eight, nine, 10, 11, 12, 13, 14, 14 weight divisions. It's a lot to remember. It's 115 pounds. Fly weight is 125. Phantom weight is 135. Feather weight is 145. Light weight is 155. Is 165. Welter weight is 170. Super welter weight is 175. Middle weight is 185. Super middle weight is 195. Light heavy weight is 205. Cruiser weight is 225. It's 265 and they don't have a super heavy weight yet. This is Wikipedia. I want to, I got the UFC rankings pound for pound, but because this is Wikipedia, it is subject to change. And also, the last time this was edited was sometime in October. Titles in strong weight, fly weight, bad organizations, recognized women's championships, also sanctioned a separate atom weight, 116 pounds, 30s at 105 pounds going at it, but you never know. Okay, so that's, those are the definitions of rankings. So rankings tend to change when fights happen, et cetera. All right, so Stevie Nicole and Chich. Six, we've got Daniel got Israel Adesanya. At nine, we've got Tim Ferguson. At 10, we've got Kamala Eastman. At 11, we've got Valentina Chivenko. At 12, Dustin Poirier. And 15, Tyrone Woodley. Interestingly enough, on this page right here, Dustin Poirier and Conor McGregor are tied for 12. And there's now 13th, but I read Conor is 13th anyway. Adesanya is a middleweight, but paying attention to weight class or paying attention to pound for pound fighting. I'll start with what I know. Ferguson at number one, Dustin Poirier is number two. Conor McGregor at number three. Justin Gagey at number four. Dolph Serrani at number five. Paul Felder, number six. Dan Hooker, number seven. Kevin LaBose at number 10. Anthony Pettis, number 11. Gregor Galepsi, number 12. Charles O'Vehra, number 13. Alexander Vene is 14. And Islam, Mahakchev, that's like heavyweight. At welterweight, the division has always intrigued me. For instance, Kabeed Antone, Dustin should fight Conor. He's somehow hesitating. He is going to fight Dolph Serrani in an upcoming fight, which is going to be very exciting. Dolph Serrani is an incredible fighter. Quinta is a good fighter. Anthony Pettis, very tough fighter. This division is awesome. The welterweight division is also excellent. We've got Camero Ousmane as champ. Tyrone Wigley, number one. Colby Covington at two. Horny Nesbo at three. Leon Edwards at four. Ralph Hilda's Andrews at five. Damian Meyer at six. San Diego, Paul Zinedebel at seven. Stephen Thompson at eight. Nick Diaz, nine. Anthony Pettis, 10. Robbie Royer, 12. It's said to look at 13. Gilbert Burns at 14. And Jocke Miller, 15. Incredible, exciting division. Tries to go under for his skin. Damian Meyer is a great fighter. Stephen Thompson is an excellent fighter. Robbie Royer years ago. And Ben Asken, of course, is the motormouth of UFC. So, number two losses, unfortunately. So, it's going to take me a minute to figure this out. She holds. I'm so bad. No. Zeng Li is the champion of her division. Number one is Jessica Andrade. Rose, seven units. Off-rug, Rose. Three is Tatiana Suarez. Four is Joanna Zizik. She is Zizik. And Surov. Six is Claudia Gadella. Seven is Carla Esparaza. Eight is Michelle Waterson. Nine is Marina Rodriguez. Ten is Cynthia Caldero. Eleven is Alexa Grasso. Twelve is Salis Herring. Thirteen is Tisha Torres. Fourteenth is Carolina. And fifteen is Amanda Riva. Four is Juliana Pena. Number five is Aspen Ladd. Number six is Raquel Pennington. Number seventh is Yana Cunezcaya. Eight is Irene Aldana. Nine is Marion Reno. Ten is Sarah McMahon. Eleven is Lena Landsberg. Twelve is Mesa Chieson. Thirteen is Bache Correa. Fourteen is Nicole Monteno. And fifteen is Sujero Ubex. And again, this division, exciting, exciting fighters. Aspen Ladd, Raquel Pennington. We best athletes in the entire world. In Valtinas Fleet Division, we have Valtinas Shevchenko as the champ. Number one is Caitlyn Chukye Chien. Number two is Jessica Ai. Number three is Joanne Calderwood. Number four is Liz Comerche. Number five is Jennifer Maya. Six is Evian Arujo. Seven is Andrea Lee. Eight is Lauren Murphy. Nine is Roxanne. Macy Barber. Eleven is Lexis Davis. Rosa. Thirteen is Antonio Chevenko. Valtinas, the champ's sister. Fourteen is Romero Borrello. And fifteen is Paige Vange Sands. Okay, so recently, Macy Barber kept saying that she wants to fight Paige Vange Sands. And the reason that she gives for fighting Paige is that she's a pretty girl who can speak well. I just do this. Paige is such a bad fighter. She has her sights set on Paige. I've never seen Paige fight. I've seen clips of who has submitted and who has, of who they're taking on. Sometimes at trash talk, it's in the trouble. It's gotten several of the male fighters in the trouble. And what if Paige went in there and clocked Macy? So sometimes you got to watch out. That's just me. I'm not a fighter, but I'd watch out. Future episodes. And I will try not to see such a bad printing job. I like to write stuff out longhand. See, 244 results was that it was a good fight. It shouldn't have stopped the fight. He was aghast. Shouldn't have happened to me. I mean, so I thought he was going to fight. I watched some clips, again, in Computer Stam. But, and I like both men equally as fighters and also their respect towards one another. It's awesome. I love how warriors will show each other that they both have cojones. Maybe it should be done again. I've read here that Dana's not interested in doing a rematch, but maybe he should, you know? Why not? It might not be a bad idea.  I guess both those guys were all in. Now, Kevin Lee has had a two, he was on a two-fight losing streak, which to me isn't much of a losing streak, but in MMA world, if you lose two or three fights, it hurts your record. So he was fighting Greta Galepsi and gave him a knockout by head kick. And I guess it was a very, it was a very bad kick. Lee's nickname is the Motown Phenom. Galepsi has been previously undefeated, 13-0. It's a very good record. So it's a 155-pound division that that fight was in. The fight between Darren Till and Kelvin Gaslam was decided by, it was a split decision, with Till winning. And Gaslam has lost two straight also, but he just recently lost to Israel Adesanya, who is the champ of that division. Israel's performance was, I guess I don't remember how, when he did that dance in the ring, but it was great. I thought Kelvin was going to win that fight. It all comes to show that in MMA, you never know. The guy could be bigger, or he could be shorter, or he could be taller, or he could be rounder, but sometimes you just can't, sometimes these fights are just too close to call, especially if you're not a fighter yourself. And what I've been noticing is that when fighters weigh in on upcoming fight, they too will say it's too close to call. So that is a review of weight classes, our pound for pound division rankings, and a little bit about UFC 244 from last week. Okay, that's it for me. I'm your hostess, Betty St. Leveau. I hope you've been enjoying yourself at Octagon St. Leveau today. Several of my family members are either brown belts or black belts. It would only go to show that one day, I too would get bit by fighting warrior fever. Until next time, love one another, everybody, really, love and peace, okay? Ciao.


Hey everyone, my name is Jeffrey Miu. I'm a product manager here at Microsoft and I'm working on the Python data science and AI team in VS code And by the end of the talk I hope that everyone can see how you can accomplish your own data science tasks faster and more efficiently in VS code This is the last session before lunch so I'm extra thankful for everyone to come and I know this is I Hopefully everyone's like actually a true VS code supporter because I know you're coming and taking your time of your lunch to Come watch this talk So today I'll be going through a typical data science project in VS code I'll be walking you through step-by-step throughout the project from the getting started to the data preprocessing and cleaning To the model selection and training as well and finally to the employment of the models And alongside I'll be showcasing all of the data science features VS code has to offer to speed up your development things like VS code notebooks data wrangler which is completely brand-new projects and GitHub code spaces and more and hopefully by the end of 30 minutes. You'll see how VS code revolutionizes the way you do data science If you use Python or VS code before much of what you'll see today Well, I should be features or processes. You haven't seen before it'll be brand new really cool cutting-edge stuff So a quick little bit about myself before we get started I've been on the Python team at VS code for around three years now I've also loved dogs as you see in the screen cats and Python The language I'm still unsure about the snake so we'll see but again You can find my contact info on the screen and if you have any questions during the presentation Feel free to come reach out to me at the Microsoft booth that we have downstairs So let's start with a quick demo of what we're actually building today Everyone's probably heard of like the big great Resignation that's happening right now in the world and especially prominent in the tech industry that most of us are in right now So because of this I want to analyze some salary data Along with the skill sets and background from the Python industry to see if I can develop some sort of model a crude model To predict what your salary should be given your technical skill set and background with the hopes that it helps determine your personal net worth and value in industry So let's just go over to my Let's go over to my favorite editor VS code and I just have like a simple notebook I'm a VS code notebooks right now and I have some simple iPi widgets that just created a form to make it a little more interactive Just to showcase this power, but let's just do for the first prediction Let's just pick a full stack developer.  They know Azure and AWS Some of the works with react and Docker and get and I'll just put three years of coding experience And then let me just run the prediction and they can see what we get and we can see that at least this model Is predicting that their annual salary should be around hundred twenty three thousand US dollars So I'll go over the data set of what we use in a bit But I just want to caveat that this data set is from over 80,000 different tech salaries around the world So it's a statistically statistically significant data set size But now that we saw a sneak peek of what we're actually going to be building today Let's get started with the actual building of this from scratch So let me just jump back Okay, let me just move this so it's easier So today we're going to be walking through the typical data science workflow to build our salary prediction model and each step I'm going to be showing you how this code improves the way that you typically do it So at a high level I like to break it down into three different phases three different steps The first phase is the data exploration phase and this includes things like Providing the appropriate data set getting the data into data stores doing data cleaning and pre-processing such that can be fed into models and Doing any data exploratory analysis of looking for trends or patterns within your data The second phase consists of model training So this includes things like building the training script figuring out which models you need to use Figure the compute because I'm most likely you don't want to be running it on your local machine And then just doing some tuning on the parameters as well And then finally the last phase I like to call it as productionization where you actually just me saving your packages Save packaging for your model having it deployed to the cloud and creating any API so that you can call your inferencing service From your application and by inferencing here I'm just talking about the model prediction and the way this diagram is actually laid out doesn't necessarily show how much time This spent on each step so in the latest survey from Anaconda They found that the majority of data scientists times actually spent during the first phase of this data exploration part Specifically around data cleaning and data pre-processing and preparation You can see on the screen takes up around over 50% of the working hours on average So it's definitely not proportional to What you actually have to do like this one phase in the beginning takes up over 50% of your time and This kind of correlates to the majority of data scientists saying that it's the least enjoyable part of the job Because of how mundane and tedious this task is so I'm just going to be showing Some of the features that we have in VS code that could should productively speed up your time on this such slow It less enjoyable areas of data science So let's start off with the data exploration phase So to start off we'll be using the latest stack overflow data to survey that asked a bunch of questions like what languages you work with what frameworks Etc as well as things about your job information So what role you work for your salary and that's how we can be that's the data We can be using to create this model. So a lot of relevant data That we can extract there's a lot of relevant data that we can extract features from and like I mentioned this This data has been shared publicly of the audience the community so you can see the link up here And there's over 80,000 responses as well Cool, so that's enough slides for now. Let's move on to the live demo, which will be the majority of this talk so Before you start with any coding, of course, you'll need to have VS code downloaded So if you don't have it you can just go to code visual studio calm and download it for your computer It's again completely free open source and cross-platform if you haven't tried it But once you actually have this code downloaded you'll need to go to the extensions tab So this is what these could looks like in a Mac But if you go to the extensions tab, which is this kind of like four cubes right here and be just searched for the keyword Python And that should be the first one from Microsoft and installing it should get we'll get you all the Python Jupyter notebook related functionality for VS code and that's where all the data science features They'll be showcasing today reside as well Cool, so let's just go back to the top of our notebook So We'll be using three super common Python data science library packages for your project that you can install via anaconda or pip We're gonna be using pandas and numpy for data ingestion and data cleaning and data preprocessing as well as sklearn for model training and inferencing and Once we actually have everything installing it up. We can actually get started So to get started I have my Jupyter notebook here and I'll walk through step-by-step of what I actually done and Although I might be stepping through the code a little bit quickly because it is a 30 minute talk The focus here is mostly around the tooling and the productivity provides So if you want to actually look at the code itself just feel free to pause and re-watch this recorded talk anytime to look at the code in More detail But again one of the first things we need to do with any data science project will be to import your data set Right, so you can see here in the second cell and hopefully it's pretty good size, but let me know if I need to zoom in But you can see the first thing you'll want to do is do a pandas So it'll do pd.read and you can see as I'm typing so read.tsv Intel VS code gives you intelligent suggestions and auto completions in the cell So it gives me things like oh, it suggests It's what I'm trying to do so you can it can kind of tell I'm trying to do read CSV So I can just tab that as well It gives me some doc strings about how I can actually use read CSV as well as some examples And then I have the I'm going to use the stack overflow survey data set and again It understands that this is this files in my explorer So I can it gives me that odd suggestion and then to run the cell I can either click this run button here or I can just use a Jupyter hotkey shift enter and Again, now that we've created this new variable DF There's oftentimes you're actually gonna be having hundreds of different variables in your notebook And it's kind of hard to keep track of them, right? So although notebooks are great for flexibility where you can run cells out of order multiple times One of the biggest annoyances with data scientists is being able to keep track of the state of all these variables, right? So we made that easier by building in a built-in variable explorer within data within the notebook editor So if you just click on this icon at the top right, which brings up the bottom tab and you click on Jupyter variables. You'll now see Essentially all your active variables within your notebook and your running kernel and you can also see the most up-to-date values You can see here. I have this DF that I just created gives me information like what the type is the size So you saw it's over 80,000. I have a bunch of other Variables on here because I've ran this notebook just for the sake of this demo But you can see how it's really easy to keep track of alphabetized and just super easy to keep track of all everything that's happening with your notebook So now that we've actually imported our data into our notebook We'll want to look at the data itself and again the probably the most most common API that every data science use is a DF.head So once I run the DF.head we can kind of see like a snippet of our data. It says there's actually five 50 columns, but if I scroll across If I scroll across you can see there's definitely not 50 that's being shown here And this is because by default Jupyter always truncates large data sets So if you want to see the entire data set we can go to Google try to search on Stack Overflow like what code we need to do to write To show the entire data set But you can see this view is always going to be pretty limited because it has to fit within a notebook cell output And this is where data Wrangler VS Code data Wrangler can come in and help So today, I'm excited to show you like a really sneak peek of a project that we've been working on I'm trying to scroll for some reason. Yeah. Yes, sorry It's I'm trying to show today I'm really excited to show a sneak peek of a new project we've been working on on the VS Code team called data Wrangler And hasn't been released to the public yet So I think this might be actually the one of the first public audiences to see it But in a nutshell data Wrangler is a free code-centric data cleaning data preprocessing tool That can interact that you can interact with in an Excel like interface But it's bread and butter is automatically generating the underlining Python code automatically in the background as you perform your data operations But again, rather than giving like a whole marketing spiel of what it is Let's just go ahead and actually use it. So again when you're working with VS Code notebooks And you do any outputs to data frame You'll now see a launch data Wrangler button that you see here above the table When you click on it, it'll take you to a new tab where you can perform all of your data cleaning and preparation tasks And the cool part is this is a sandbox environment So here you can explore manipulate the data with your heart's content without ever having to worry about messing up your original data set So this is just everything sandbox here When you first launch it, you'll see a grid view of your entire data set rather than a truncated view that you just saw earlier So it gives you way more information. You can see every single column here You can also see the summary statistics, which is super nice So if you see on the left-hand side here, it gives you information about which columns are missing values So it makes it really easy to tell if the stack overflow or whatever data set you're working with needs to be cleaned And if so, which areas of the data that I actually need to look at and clean So again going back to the problem at hand We see the data set has a ton of information like 50 plus columns and we have to sift through to figure out the relevant data For them to feed to our model, but we'll set up with something simple such as filtering for Python developers only So again, I've looked at this data set obviously, so I do know some of the key Coms we're gonna be looking at so you can see here language. I've worked with we're gonna want to filter for the keyword Python So I can just right-click click filter and then I'm gonna want to just pull this down But I'm gonna want to search for anything that contains the keyword Python Because you can see a lot of the data is aggregated right here And you can see as I do an operation not only do I get a preview of what's actually happening to the data grid So you can see hopefully the contrast kind of shows but the rows that are being removed are highlighted in red But also at the bottom you can see data wrangler automatically generates the code that is required to do that operation So instills really trust in the tool rather than having a black box and just doing it in the background It tells you exactly what's actually happening So again, if I if I like what I see here, I can just click accept The other thing that typical data scientists have to do is you have to filter between your data, right? So again, like I mentioned, there's around 50 plus columns But we can easily choose which columns we want to keep you see there's a lot of data A lot of it's really interesting and relevant that might help us but things that might not be useful Like if they have a stack overflow account or if they have like how often they check out stack overflow stuff like that So we can just do interactive like we would interact with Excel just doing like a control click And if I wanted to remove them, I can just do a simple drop column click accept and you can see it get a preview What's actually happening and they're all now removed One really other cool feature I want to show is if we look at the comp and there's the last column There's a column called converted comp early and we can see this is the actual data that we want to get because it's the annual salary But we can see there's a lot of missing values in here So one thing we can do is to actually remove these missing values, right as many data scientists do So to do so we can easily again right click on the column click drop missing values in column Again gives me a preview of what's actually being removed. I can click accept and again You can see all the code that's automatically generated for me within data Wrangler The other thing I'll need to do is I need to convert this year this comp column from this currency string format To some sort of integer so I can actually do some numeric operations on it And actually such that our model can understand it because it's not going to really understand string So we want to convert it to something that her model can understand So to do so normally I would need to write some custom code Maybe I need to go write some regex to extract it out Many of you are probably cringing at the word regex right now because every data scientist read it But we can actually leverage a built-in transformation within data Wrangler called flash fill to do so So if I just right click and click flash fill Flash fill is an operation that's built into data Wrangler that uses an AI model in the back end to understand What information you're trying to derive from the original source column just based on a single example? So you can see here. Let's just say I want to extract out the actual 50 like the actual currency value So I'll just type in fifty one thousand five hundred fifty two and then you can see that just based on a single example This AI model is able to infer the rest of the values for the new column And the other great thing is you can see it perfectly did it and it also it gives me the exact code And we try to make the code as human readable as possible So we don't we try to use as many built-in Python libraries We don't really you can see there's no other additional imports We need but the code is super clear in the super clear definition with a really nice doctrine as well So it tells you it's extremely clear and reproducible for the users So again, this looks good to me, so I can just click click click accept And now I have this brand new column and again I just did this in a few seconds But if I had to do this with regex and googling this might take like maybe five ten plus minutes to do The last part I just wanted to quickly show is let's say This is still you can see it's object type So basically it's the string type right now, but like I mentioned earlier We want to convert it to something that is Understandable, so let's move it Let's just convert it. Let's say to a float 64 and then I can click accept So now we can see it's in a float. It's a float value The other really interesting thing is after converting it now We can see there's still your data wrangler also has built in visualizations So this is automatically generated again I didn't write any code to do this, but you'll notice there's a lot of outliers in this histogram A lot of the data is concentrated at the very left of it So we can actually zoom in on this histogram just by dragging and dropping and we can see a lot of the a lot of the data is Below a 500k salary. So the south of the salary points here They range from let's say like zero or something to like over 10 million So we're gonna want to remove a lot of these outliers as we call them in the industry and again We can just do so by if we look at the summary statistics again We can see let's see the the minimum value is 48 So I don't I think that's also a noise as well because most likely someone's not making $48 annually as a As a software engineer so again, we can just do a filter operation again and then filter for things in that range so let's just Hide this real quick write this down and let's say we want to filter for just arbitrarily things that are Data points that are greater than $10,000 a year and then let's also add something else. Let's say a max of Let me just do this. Let's say a max of 500,000. So we want to do less than 500,000 And you can see as I'm typing all this out the code is automatically being generated for me the previous up being updated live So you can see this rose highlighted red because it's 500,000 or over and again once everything looks good to me I can click accept and now my data is filtered and if I look back at the visualization now data looks a lot more clean Right, there's a lot less outliers if I want to remove these outliers I can set the filter be even lower, but yeah So the last part just wanted to quickly show is a lot of the data is aggregated into One specific column. So if I want to look at what packages people work with you can see a lot of the data is right Especially here. It's all agree into one column. So normally I can I would need to split out this column I would need to encode it for my model to understand But I can actually just leverage data wranglers built in multi-label text binarizer Just doing a right-click specifying a delimiter of a semi colon and you can see data wrangler automatically figures out Hey, what are all the categories in here based on this delimiter separation and you can see it creates new columns for each of them So there's one for dotnet core dotnet framework apache spark Etc and sets a value of one to encode if this respondent actually uses it So again, I can quickly click accept. So again, I'll skip the rest of the data cleaning operation It's just for the sake of time but I wanted to point out that when you are done you can just quickly click this button export code back to notebook and exit and all the code you literally just wrote is automatically generated or thrown back into your notebook and You can just rerun it as normal and again We didn't actually do any modifications to the original data unless you actually run this code yourself So you can see hopefully you can see how much time this saves during the most time-consuming process of the data science workflow So let's just jump back to the slides and just quickly summarize So data wrangler again code centric data cleaning preparation tool that was built in directly to notebooks So I didn't have to use any separate tools had smart suggestions quick insights of Some summary statistics of like what I need to do with my data set a bunch of built-in data transformations I just only touched the tip of the iceberg and a bunch of built-in data visualization. So you saw with like the histogram as well So because we're working with Now that we've been actually cleaned and encoder our data We can go ahead to the second phase of training which is around Which is training so because we're working with the continuous data set of salary data We want to we can easily leverage sk learns existing library of linear models rather than writing your models from scratch So here's just a simple graphic of how you can actually choose which models right for your given problem space But just specifically for this problem I chose ridge regression because for the salary For the salary prediction case we're working with the regression problem But with under a hundred thousand data points But there's potentially a lot of different features that we can determine some salary So you can see if I just follow that flow chart. I end up on ridge regression But let's just jump back to the notebook again to quickly see the actual code for this So let me just jump back To the model training section so you can see here again I'll need to first split my data into test and training sets I'm just using the train test split function from sk learn makes it super simple Saves me from running a lot of code and you can see I'm allocating 20% of my test data for training and the rest will be Used for testing sorry 20% for training testing the rest will be used for training The input for my model will be a bunch of features that I've cleaned and pre-processed that I've already done with data wrangler ahead of time Just for the sake of this demo things like you saw in the beginning of the presentation like your batch ground education level What ml packages or what web packages are using so you can see that's my x value and my y is just converted Compurely because that's what we're actually going to be predicting upon and Then here I'm just defining the model and then the fit functions during the actual training So because I'm just using a simple tabular data set. There's no images or anything It's not too complex of a data set the training is pretty quick to run So I'm able to run on my local machine But if I was doing something with like a convolutional neural net or with like millions of images my laptop would definitely be too slow So this is where the remote ssh feature can come in and save the day And if you want to leverage more like compute power for your training job Or just have your train job run in the cloud You can make it super easy to connect to remote servers using a remote ssh And all you need to do is just download this remote ssh extension again from the marketplace So just search remote you'll see remote ssh there and once you actually have that downloaded you can simply just go into the You'll see this green button the bottom left and here you can click connect to hosts And then you can just type in your host IP of that server and then basically BS codes back in will be run on that server But the front end will remain the same So hopefully the main thing you'll notice as the end user is that all your training everything runs Just snap here because it's running on that back end as well, but the front end is exactly the same So once I'm actually done with the training you can see I've got an accuracy of around 87% which is pretty solid again Just for this simple trivial example But once I'm done with the training and I'm happy with the code I want to share the code to others or even use get to deploy the code What's great is VS code is native get integration with full support for notebooks So if I just go into the source control tab if I want to just do a quick like see what changes I made to like this This notebook I made what's really great is VS code puts it into a really nice UI So rather than viewing as like a crude json diff if you had to do this within a terminal VS code understands like hey, what did I change in the actual input cell via code? What did I change in the metadata? What did I change the outputs and organize that all that for you? So it makes it super easy to just do a get diff if you need to as well The other amazing thing about get integration I want to show is that the entire data experience data science experience They just saw can be done with github code spaces So github code spaces essentially VS code built into github, but how you access it is if you have a GitHub repository like you said here you see this code icon if I do click this drop-down You'll see this now new code spaces button and then I can click that and it launches something like this where you can see It's the exact same interface exact same features my same notebooks same data set everything But now I'm running in the browser. So I don't actually need to have VS code downloaded to do everything You just saw today So that was kind of like the training phase in a nutshell The last step is kind of to just to production as the model So once we're actually happy with the model we can actually let me just get back to the code But once we're actually happy with the model I will need to do again I just made it super simple for the sake of time But we'll just need to serialize it with the pickle package such that I can save it in my local directory And then I can just use this to Upload and deploy it to the cloud and once my model is actually saved then we can use Like online services or cloud services like Azure to deploy the cloud and what's great is VS code is built in direct integrations with these So these Azure services so things like Azure machine learning if you want to run experiments or Azure functions If you want to host an endpoint, but it's all directly integrated within VS code again Just through the extensions tab and again I don't want to get I won't get into the details for this talk But I'll just quickly showcase some things you can do with the Azure machine learning extension So you can see here you can deploy your models out here and what's great is It will automatically keep track of the versioning of your models So all that's an automatically for you You keep you can create endpoints to actually hit that model if you're trying to create API endpoint as well And then there's computes and data stores if you want to store your data set here as well So again, just for the sake of time, I'm just gonna skip through this a little bit But just want to close out quickly. I know we talked a lot about different features in VS code today But I just want to summarize everything we've done and the easy into easy takeaways So I'll take you back to the data science workflow diagram that you saw earlier We started off with the data exploration right and we did that with data Wrangler, right? All of that was done data Wrangler the all the code is automatically generated You can see how much faster it was the next step We did was the training steps and that's where we use VS code notebooks with Jupiter notebooks And that's where we developed the training script We did the compute and we can connect to remote SSH as well for remote compute power and the last part was productionization where we used just different Azure extensions to this for VS code So things like Azure machine learning and that's where we were able to do some inferencing We're able to store models of the cloud, etc But the main most important thing which many of you might not realize is I did this all inside of VS code I didn't need to context which back and forth between multiple tools or do much setup at all And this is how VS code is actually revolutionizing the way you do data science So what's next? So if you want if you're interested in building your if you're interested in trying out your data science workflow With VS code you can go to this aka MS link slash VS code stupider and I'll tell you how you can get started So like I mentioned as well data wrangler hasn't actually been officially released to the public yet So we're still trying to target some sort of beta build potentially the summer so stay tuned for that but if you want to Maybe get like a sneak peek or join our mailing list for early insiders access You feel free to click this link and just submit Just submit your information and you'll be notified of when it's like the official beta is released And you'll be added to our mailing list of beta builds So we have a lot more exciting new features in Python data science coming in the pipeline So hopefully I'll be back again soon to share this with you, but thank you so much for your time and thank you


Most times we think they are always happy and carefree. According to WHO, nearly 3 in 4 children or 300 million children aged 2-4 years regularly suffer physical or psychological abuse from parents and caregivers. Wow, that's a lot. Most children who suffer abuse end up as broken adults who go on to inflict pain on others. So how can you tell when a child is being abused? What kind of abuse is going on? How do you stop it? Have you ever asked yourself if you've suffered any form of abuse in your childhood or what effect that may be having on your current relationships? The key to quality relationships and happiness in life is the wholeness of individuals. Watch this video and understand the 6 types of childhood abuse. 1. Were you abused as a child? Some of you may not think so. So many parents, teachers and coaches think they are yelling at children in an effective way or putting them on the right path but on the contrary, it amounts to verbal abuse and can leave a child with low self-esteem than ever. According to Health Day, verbal abuse can undermine your child's self-esteem, damage his ability to trust and form relationships, and cheap away an academic and social scale. Did you know that verbal abuse produces the same result as sexual abuse and other forms of abuse? Health Day gives us a clue according to them. Current research shows that verbal abuse of children can be just as destructive emotionally as fiscal and sexual abuse and puts them in a small risk for depression and anxiety. What then will you do as a parent who loves their kid but does not want to yell at them because it will hurt them? This question will come up frequently but there is one right answer. You should talk to your kids more. Recommend good books to them. The more they feel loved and cared for, the more they will try not to do things that hurt you. You need to be able to get through to them. If you try any forceful means, they might grow up resenting you. No parents want that from their children. 2. Physical Abuse Do not under any circumstance hit the child. It is true that if you were abused as a child, you are more likely to repeat the cycle as an adult. On the other hand, many adult survivors of child abuse have a strong motivation to protect their children against what they went through and become excellent parents. Children are rather delicate and may not withstand physical abuse the way an adult might. Adults are older and can think deeply about issues. Many times, adults also choose to remain in abusive relationships. Children don't have that choice. Physical abuse is quite different than other forms of abuse because it contains both emotional and physical factors. A person who is hit by a loved one can feel psychologically unsafe as well as have the bruises and blood to show for it. No one deserves to be abused by anyone at all. This is why social service works very hard to pry children away from abusive and irresponsible parents. Simply because you got your girlfriend pregnant does not in any way qualify you as a parent. Parenting is a commitment to help your child grow to become the best version of themselves. If you are not ready to make that commitment, don't waste anyone's time by getting married to them. Emotional Abuse This is a very pervasive form of abuse that children face daily. According to a very well-mined, emotional abuse is a way to control another person by using emotions to criticize, embarrass, shame, blame or otherwise manipulate another person. Sexual abuse can also be a form of physical and emotional abuse. Emotional abuse is actually a pretty difficult form of abuse to identify because it is subtle. You can't really tell it's there.  Some of you may be puzzled and wonder whether these things actually happen. If you didn't experience similar things, you probably had a blessed childhood. Some people live all their lives hiding under the shadows because of these types of childhood. Their abusive parents continue to control their lives even after they have died. No one deserves to live this way. But how can you help? One of the ways you can help is by focusing on being a great parent when you eventually have a kid to reduce the world's population of abused children. Another way you can help is to volunteer at NGOs targeted at rehabilitating abused children. This will go a long way in the process of healing. If you were abused yourself, allow yourself to heal. It is okay to be afraid of making mistakes. It is okay to feel small. It is even okay to stutter and run away from responsibility.    4. Child Labor and Exploitation Child labor may seem like a term forgotten in history. Some of us may even think it is a myth. However, according to UNICEF, in the world's poorest countries, slightly more than one in four children are engaged in child labor. This means that over 200 million children globally are child laborers. Now, what do you make of that? According to the world counts, 73 million out of the 200 million children who are engaged in paid and unpaid employment are less than 10 years. Now, how old were you when you got your first job? Did you think it was hard? Now put yourself in the shoes of children who have to fend for themselves. In these developing countries where child labor is so prevalent, the challenges are numerous. Even the ones old enough to work face huge unemployment. While many of them are underemployed, some of them have employment with mega-renumerations that can barely feed their families. In the end, government institutions in these countries have a large role to play in stemming global child labor statistics. Children are at a critical stage of development where they need to focus on learning and feeding well. Children require time and patience to get there because a lot of them are different. The trouble is that unscrupulous entrepreneurs prefer hiring child laborers because they are more desperate than other workers. They work mainly for food or enumeration that can buy food. Children deserve to be taken better care of. A very effective way of addressing this problem is by not getting married unless one is fully ready for the responsibilities that come with raising children. Irresponsible parents are one of the major causes of child labor. Other causes are death and absent mothers or fathers. 5.  You are wrong. Children actually need attention more than you think. Many children grew up resenting their parents even when their parents worked so hard and left them a huge fortune. They still hate them for not being there, for thinking that money could take their place. Sadly, a lot of parents make this mistake. Money can't make someone love you is a principle as old as time itself. Even money has its limits.  Maybe you may win custody and court after a divorce because the judge believes you have better finances to take care of the child. The child, however, will resent you nonetheless. It just doesn't work. However, attention neglect is just one of the types of neglect abuse. Many parents don't take good care of their children. Their reason for this may vary. Sometimes, it is the inability to play properly. Some parents are drug addicts and prioritize their drugs over groceries which can negatively affect their children. Therefore, the children may also look dirty, be left alone or in the care of other young children. Eat more than usual at a meal or save food for later. Such children seldom have medical, dental or mental health care. They may also miss a lot at school. It will be almost as though the parents are bad for the child. It is often the case. One way to prevent this type of abuse is not to get pregnant when you are not ready for a child. You can always give the child social service who will take better care of them than raise them yourself. 6. Bullying People generally think bullying and its trauma goes away after one has left high school. They are wrong. Bullying can actually affect all the parties involved. Bullied and the One Bullying According to StopBully.gov, bullying is considered an adverse childhood experience, ACE. ACEs are potentially traumatic events that can have negative, lasting effect on a person's development, the way they interact with others and how they perform in school. There are very few bullies who are actually confident in themselves. No confident person thinks putting others down will make them feel powerful. It is the incompleteness they feel by themselves that makes them act the way they do. People bullied are scared out of their weeds for years. Bullying doesn't just happen in school. It happens everywhere. On the bus and the park, even on the streets when no parents are watching, it can be a nightmare for those bullied. People bullied when they were younger can try to repress their memories in order to carry on with life. This may make them generally indifferent. StopBully.gov On bullying and post-traumatic stress, some children may repress their thoughts or feelings about what took place. This can lead to numbness or loss of interest in activities. People who leave this way have no actual goals or interests even. Help end bullying today. No one deserves to either bully or be bullied.


So today I'm going to talk to you about how to keep driving after a DUI arrest. In the state of Florida it is impossible to get around and to continue your life normally without a driver's license. Now there's two different suspensions that can happen if you've been arrested for a DUI. If you provided a breath sample and it was over a 0.08 breath alcohol level, you have been suspended for six months by the Department of Motor Vehicles. If you refuse to take a breath sample, in your case you've already been suspended for one year. Now the chances are the officer physically took your driver's license and the next day when you got out of jail you have no license and you are terrified that you're not going to be able to continue working and living and providing for your family. Now there's a great option to keep you driving through the entirety of your case. Now that means from the next day through the next 10 days you're able to drive on your citation. There's going to be tiny little font on the bottom of your citation that says this is a temporary driver's license for 10 days. Now they're doing that so that you can hire an attorney, find someone who is an expert in DUIs that can help you through this process.  Now it's a little complicated but the simple breakdown is this. You need to sign up for what's called DUI school. Different counties in Florida have different DUI school programs. You have to sign up for the one specific for your county. Once you get the receipt showing I've signed up for DUI school then you need to file an application for a hardship license. As long as you have never been convicted of DUI previously you will be accepted for a hardship license from the DMV. Now once you have that hardship license in your hand you are able to drive for work, church, education purposes and for life necessities. Now similarly if you refuse that breath sample and you've already been suspended for one year that hardship license will keep you driving for that entire year. This will also make it so you can go see your attorney so that you can get to court and so that you can complete the other things that you need while your DUI is pending. Now in the alternative if you know that you were wrongfully arrested and you either didn't blow over a .08 and your license shouldn't have been suspended or if you refuse to provide a breath sample but you don't think they had a reason to arrest you you can fight the suspension. Now you need to hire an attorney who specializes in DUIs and fighting DUI suspensions in order to go forward with what is called a formal review hearing. Now at that hearing an attorney can fight for you and get the entire suspension thrown out. Now the best option for most people keep driving get that hardship license in your pocket keep moving forward with your life. Now if you find yourself in this predicament where your license has been suspended again the very first thing you should do is call an attorney who specializes in DUIs and have them walk you through this process. They can save you a lot of steps and help you get your license in your hand fast.