Paradoxifi

Ann Clark McFarland
  • 5.0 (2)
  • SCIENCE

Unexpected STEM and Faith Stories

Episodes

  1. 12/05/2020

    Healthcare, Healing, & The Gift of Anesthesia

    Some aspects of medicine are a paradox. For instance, there are treatments that will hurt before they can heal. Or, there is the ordeal of having an operation. A surgeon must cut before he can cure. Thankfully, we live in a time where healthcare and healing are aided by the gift of anesthesia. What is anesthesia? In general terms, anesthesia is medicine given before surgery or medical procedures to put you to sleep. Under the influence of anesthesia medicines, a patient experiences loss of sensation. This affect can be with or without loss of consciousness depending on the desired outcome. When did anesthesia first come on the scene? A Christian Creationist might believe that the first episode of anesthesia appeared on the scene when God created a woman. The bible describes the “surgical” event in the second chapter of Genesis starting with verse eighteen. “Then the Lord God said, “It is not good that the man should be alone. I will make him a helper fit for him.” Now out of the ground the Lord God had formed every beast of the field and every bird of the heavens and brought them to the man to see what he would call them. And whatever the man called every living creature, that was its name. The man gave names to all livestock and to the birds of the heavens and to every beast of the field. But for Adam there was not found a helper fit for him. So the Lord God caused a deep sleep to fall upon the man. While he slept, God took one of his ribs and closed up its place with flesh. And the rib that God took he made into a woman and brought her to the man. Then the man said, “This at last is bone of my bones and flesh of my flesh; she shall be called Woman, because she was taken out of Man.” Who claims the credit for modern anesthesia? In American history, the credit goes to a dentist named William Thomas Green Morton. His mojo juice was ether. On “Ether Day,” Friday October 16, 1846, at Massachusetts General Hospital in Boston, William demonstrated how the inhalation of ether vapor could overcome the pain of surgery. How did anesthesia work before ether was discovered? It didn’t. Only remedies that alleviated pain or caused a stupor were available. One record of a surgery without anesthesia came just thirty five years prior to William’s demonstration. It concerned a woman named Frances (Fanny) Burney d’Arblay who might have been spared a great deal of agony had she lived to see Ether Day. Fanny lived from 1752 to 1840. She wrote four novels, eight plays, one biography, and twenty volumes of journals and letters. Her work influenced writers who came after her, namely Jane Austen and William Makepeace Thackeray. In one detailed and gory letter to her sister Esther, Fanny describes the event of having a mastectomy for breast cancer without anesthesia. Baron Dominique-Jean Larrey, Napoleon’s own surgeon, performed Fanny’s surgery. Fanny received little detail beforehand to keep her from being nervous. It took a team of nine to hold Fanny still and accomplish the procedure. The ordeal lasted over three hours. Amazingly, Fanny lived an additional twenty-nine years after her nightmare. Her story begs for the gift of anesthesia. In this podcast episode, Healthcare, Healing, & The Gift of Anesthesia, we discuss a variety of aspects regarding the use of anesthesia. Show notes include: 1:30        Episode riddle (We present a riddle or puzzle with every episode and give the answer by the end of the show.) 2:10        Educational path, job market and typical work day of a CRNA 8:30        History of anesthesia 14:00       Mechanisms of anesthesia compared to natural sleep 16:45       Different groups and types of anesthesia 20:00       The process of waking up from anesthesia 22:45       Opioid crises connection to anesthesia 25:00       Physiological complications affect anesthesia use 28:30       Safety of anesthesia use 31:30       Unusual events on the job of giving anesthesia 35:00       Answer to the riddle 37:00       Faith testimony Other 2020 podcast topics include: MD Talk: Virus News and Clinical Treatment MD Talk: Healthcare? How To Be Your Own Best Medical Expert Tech Talk: Language, ASL & Machine Learning MD Talk: Mental Health & Disease Crises MD Talk: Virus Combat Tips To Stay Well MD Talk: Virus Habits & History Unexpected STEM And Faith stories (our intro to Paradoxifi podcast)

    41 min

  2. 09/24/2020

    MD Talk: Virus News and Clinical Treatment

    Thanks to China, Italy, and NYC, doctors have learned a lot about Covid 19. The new virus is the most recent and novel science event to affect millions of people across the world. In this podcast, MD Talk: Virus News and Clinical Treatment, Dr. Tim McFarland elaborates on some of the newest Covid 19 treatment discoveries. Many of us are tired of hearing about Covid 19. However, this new disease is here to stay. On social media and the daily news there is a constant barrage of information about Covid 19. Some of the details are misleading or at best, confusing. It is our hope that this podcast discussion will relieve some fears and also strengthen listeners resolve to stay the course to minimize Covid 19 cases in their community. In our highly politicized climate, each new release of “Covid 19 information” seems to add fuel to the divisions between people. An ancient story about some bulls and a lion illustrates a caution. The tale is credited to Aesop, a person, believed by many to be a former Greek slave who lived in the late mid-6th century BCE. The narrative goes something like this…. Once upon a time, a lion made a daily trek to a field to watch three bulls graze. The view of a possible meal was irresistible. Early on, he had tried to attack them, but the bulls kept together and drove him off with their sharp horns and hoofs. He had little hope of eating them, but he came to drool over them anyway. Then one day the lion came to the field and found the bulls in separate corners as far away from each other as possible. The bulls had had a fight. The lion seized his opportunity. The bulls became easy cat food when they stood apart. The moral of the story seems to be this. United we stand. Divided we fall. With Covid 19 or politics, if we don’t eventually and consistently pull together, then we are likely to become easy pickings for more “lion-like” disasters. It is our hope and prayer that this does not happen to our country, or even our personal lives as we face our differences and foes, virus or otherwise. Be encouraged as you listen to this MD Talk podcast about virus news and clinical treatment. Then, let’s work together to protect one another. “Do nothing out of selfish ambition or vain conceit. Rather, in humility value others above yourselves.” Philippians 2:3 Show Notes: 3:20 Review of RNA and DNA and how a virus acts in the body 4:15 Discovery of Dmitri Ivanovsky related to virus presence 5:00 Virus math, contagious rate, cycles and R0 7:40 Using math to beat Covid 19 spread (Tracker board ) 8:20 Corona virus family news 9:10 Guns and Covid 19 transmission spread 10:00 Social Distance test 10:38 Masks, mosquitos and fences 14:45 NBA bubble 16:00 New things learned regarding Covid 19 16:50 Remdesivir treatment 17:50 Off label treatment explained 18:00 Ivermectin treatment 19:00 Covid 19 and Inflammation response 19:30 Pulmacort, steroids, and Cholchicine treatment 20:15 Covid 19 and clotting 20:55 Prophylactic treatments , proning and intubation 26:00 Cure for Covid 19? 27:00 High Risk groups and factors 29:30 Covid 19 as cause of death 31:16 Hydroxychloroquin treatment 32:40 Politics and Covid 19 34:40 Cytokine storm 36:15 Covid 19 vaccines and trial phases 37:15 Herd immunity 40:50 Our economy, social distancing and Covid In several of our other podcasts we discuss Covid 19 related information. Topics include: How to Be Your Own Best Medical Expert. Mental Health during Disease Crises Virus Combat Tips to Stay Well Virus Habits and History

    45 min

  3. 07/08/2020

    MD Talk: Healthcare? How To Be Your Own best Medical Expert

    In this podcast episode, consumers learn how to be their own best medical expert for important decisions regarding their healthcare. With so much contradictory health information readily available via the internet, understanding how to be your own best medical expert can be challenging. Facts based in good science can be hard to identify. In addition, facts take time and effort to establish. A new airborne contagious virus pandemic will not have adequate studies about specifics until time has passed and the event unfolds. But, researching information of airborne viral disease events of the past for comparison is possible. Also finding good science data on the use of protections, such as mask wearing to avoid disease spread, can be done. The key to web research is to remember that anyone can put up a website and promote their version of science “facts.”The idea that “good information based on accurate science” will rise to the top of your web search is not a given anymore. Knowledge is in an age of paradox. While vast amounts of information are more readily available than ever before, it takes close scrutiny to sort out fact from fiction. How can healthcare consumers better discover accurate answers? First, understand the difference between anecdotal information and scientific statistical analysis of group data. What is anecdotal evidence? An anecdote is a story. In medicine, it is what we share about a medical experience we have had or someone else has had. We share it and often believe it is “evidence” that proves a course of action is correct. As humans, we are predisposed to like anecdotes, because we can relate better to stories. Scientific evidence is hard to grasp. What is scientific evidence? Scientific evidence today lies most often within the scope of statistics. Statistics is also a science, and it deals with collecting, organizing, analyzing, and drawing conclusions from sampled data to the whole population. A proper medical test study design will have an appropriate selection of study samples. Also there will be a double blind component which means information which may influence the participants of the experiment is withheld (masked or blinded) until after the experiment is complete. Good blinding can reduce or eliminate experimental biases that might come from a participants’ expectations. Other bias possibilities that need to be eliminated in the study would be any effect on the participants caused by the observer’s study of them, observer bias, confirmation bias, and other sources of bias. Peer review is also part of a good scientific study. Decades ago, scientists didn’t conduct randomized controlled trials. They based their data and treatment on case series and anecdotal evidence. (Anyone ever hear of heroin being used as a cough suppressant for children in the early 1900s?) Medical recommendations for treatments from physicians today are most often based in studies with statistical analysis of large groups of data. Do we really want to go backwards and use anecdotal evidence as the mainstream basis for treatment again? Ask yourself this question when looking at medical information. Is this source of healthcare information rooted in anecdotal evidence or based in evidence from a structured study that used the scientific method to determine results and eliminate bias? A second major concern when examining healthcare and medical expertise is “conformation bias.“ Bias can occur not just in the study itself but also in ourselves. The fact is we all like to see our conclusions confirmed. We seek the answers we want to hear. We are all subject to the weakness of confirmation bias when we research answers. Being aware of this at the start will help steer us away from less authentic answers. Ask yourself this question when looking at medical information. What possible confirmation bias do I hold onto when I research this healthcare subject? A third major concern is becoming overconfident in your learning. Psychologists call it the Dunning Kruger effect. The Dunning Kruger effect is the name given by psychologists David Dunning and Justin Kruger in 1999, to describe an aspect of confirmation bias. It’s the part where we become over confident and over estimate what we have learned about a subject. In our overconfidence, we become blind to what we really don’t know. When asked to rank their knowledge on a subject, experts tend to rank themselves less knowledgeable due to the fact that the more they have studied their field the more they realize that there is a lot more to know on the topic than what appears at first glance. Whereas the ones who actually are not experts will believe and measure themselves as more expert because they tend to overestimate what they know. Its why an expert golfer might actually rank themselves lower than a good, but non-expert, and overly confident golfer. It’s too hard to accurately estimate what we don’t know. The Dunning Kruger affect applies to all of us. Self-knowledge of this confidence bias tendency is an important factor in an honest research process. Ask yourself this question when looking at medical information. How can I avoid the Dunning Kruger effect in my efforts as I research healthcare and become my own best medical expert? For more examples and discussion on good research strategies for healthcare consumers, listen to our podcast, MD Talk: Healthcare? How To Be Your Own best Medical Expert. Show notes: 2:00 Puzzle of the roast and the dinner party 3:30 How does personal experience factor into seeking the truth of a medical subject? 5:00 Anecdotal versus statistical analysis in regards to recommended treatment decisions 7:30 Observations drive our actions to survive 10:00 What is confirmation bias and why does it matter? 13:30 What is Dunning Kruger effect and why does it matter? 20:00 How do I search for relevant healthcare information 25:00 How is data manipulated to confirm a desired outcome? How can I weed out “bad” study data? (See “Spotting Bad Science” poster below) Is the headline sensationalized? Are the results misinterpreted for the sake of a good story? (Read the original research) Is something being sold for financial gain? Is the correlation and causation clear or confused? What conclusions are supported by the evidence or is there speculative statements? Do the sample sizes appear adequate or quite small? How are the sample sizes representative of the larger population? (They should be.) Are sample groups randomly assigned, and is there a control group? Is there blind testing when applicable? This is where researcher and participants are not in “the know” as to whether they are in the actual test group or in the placebo group. Does the researcher report the data selectively, i.e. “cherry pick,” or are they forthright about all results? Can the results be replicated? If so, that is a good thing. Extraordinary claims require extraordinary evidence. Have other scientists had opportunity to appraise and critique the study? (peer review) 25:00-36:00 How do I find trusted resources? Why trust an expert over a highly vocal dissenter? How can I remove my emotion from my pursuit of truth as I research? Isn’t truth relative? In science, what is the difference between absolute truth and perspective based truth? Why do medical professionals usually not post information on social media regarding “controversial” issues, medical or otherwise? 40:00 Testimony of faith Answer of the puzzle of the roast and the dinner party Healthcare topics? Here are some resources. Uptodate: they have an incredible wealth of patient knowledge and information it just isn’t always easy to find exactly what you are looking for but it is probably the most vetted medical information available https://www.uptodate.com/home/uptodate-subscription-options-patients Google Scholar: A general scientific search forum that combines an extensive group of scholarly search engines into one platform. Best for more scientific references. Remember to look for the abstracts if available.  https://scholar.google.com/ WebMD: patient oriented medical information but limited in scope.  https://www.webmd.com/ Mayo Clinic Patient Topics: good general patient information.  https://www.mayoclinic.org/patient-care-and-health-information AAFP Patient Handouts: educational handouts.  https://www.aafp.org/afp/handouts/viewAll.htm Dunning Kruger effect A psychological impact. We all have a tendency for it. https://www.psychologytoday.com/us/basics/dunning-kruger-effect At work https://www.welcometothejungle.com/en/articles/dunning-kruger-effect?fbclid=IwAR0ltLxYreecBi0OecQSRXEcJx9C6qRByfICQjzV_Txn6AysU1qn9UDOLnE In life. “The fool doth think he is wise, but the wise man knows himself to be a fool,” wrote Shakespeare in As You Like It. Little did he know, but this line perfectly encapsulates the spirit of the Dunning-Kruger effect.” https://www.zmescience.com/science/the-dunning-kruger-effect-feature/ Rough Guide to Spotting Bad Science Poster Facebook post about this “spotting bad science “poster https://www.facebook.com/FlinnScientific/photos/a.331994060213678/1679041635508907/?type=3&theater Facebook page that featured this poster https://www.facebook.com/FlinnScientific/ Poster purchase https://www.flinnsci.com/compound-interest-a-rough-guide-to-spotting-bad-science-poster/ap9764/?fbclid=IwAR0OoaeZCn3OAInpxH1AbOfHPxCZmNcm7Tz4vts2vZRBMyalWJAuzwbR8r8

    45 min

  4. 05/22/2020

    Tech Talk: Language, ASL & Machine Learning

    In this podcast episode, Tech Talk: Language, ASL, & Machine Learning, we interview software engineer, KJ Price, whose many work projects include the creation of a website to help people find datasets and a Machine Learning model project in conjunction with ASL  Machine Learning is a sub-field of Data Science, which is a popular study field that has expanded tremendously in the last decade. Machine Learning is a form of AI or artificial intelligence Language, as it is used by humans to communicate, involves complex and complicated sets of rules for sounds, sentences, and meaning. The use of language allows humans to collect information into broad bodies of knowledge about life and the world. Future generations draw on that collected knowledge expressed in language and do not have to start from scratch every time. Language seems to be a uniquely human ability. But as of yet, there are no unique finding that scientists agree upon that explain the exclusive connection of language to humans. Although chimps and apes have some capacity for language, it is primarily expressed in the use of signs. Their ability to respond to gestures and pointing varies with their environment and exposure to human interaction. One especially bright gorilla named Koko was able to understand a thousand or more signs and two thousand or so words of spoken English. (insert link) There are many debates about how language skills are acquired by humans. One of the more prominent language theories came from linguist Noam Chomsky in 1957. He suggested that human beings may be born with an innate understanding of how language works. What language we learn, whether it is French, Arabic, Chinese or sign language is determined by the circumstances in our lives. Chomsky believed, as did/do many linguists, that humans have some kind of genetic wiring that predisposes them to understanding communication structure. Machines can be taught language type skills using machine learning methods. Machine Learning works by using datasets and teaching a computer application to recognize patterns in the data so that the machine can perform tasks automatically, where these tasks used to be done manually. An simple example of this type of task would be to “teach” a machine to be able to distinguish between a cat or a dog in an image. After a few hundred examples of images that contain cats and other images that contain dogs, a Machine Learning model could be “taught” to recognize the difference. Then, none would need to manually look through each image and label them as “cat” or “dog” as a computer could label hundreds of these images in just a couple seconds. Siri uses Machine Learning to handle requests from Apple users. Siri’s process is related to a data science field called “Natural Language Processing” Natural Language Processing, as used by Siri, is an exciting field and a bit of a holy grail to be able to have computers and humans communicate using human language. Another example of this type of Machine Learning is to teach a machine to tell if a body of text represents a positive attitude or a negative attitude. For example, you could analyze published articles and see if they are in favor or against a political party. This is called “Sentiment Analysis,” where the machine is taught to decide the general sentiment of a body of text. There are many other ways that Machine Learning can be used in natural language. For example, IBM’s Watson can understand the English language with very high proficiency. Another example is Google Translate. These all use Machine Learning for natural language understanding and natural language generation. Interestingly, the same Machine Learning algorithms which do really well in handling image recognition, like the cats and dogs example also work really well with natural language understanding too. “Natural Language” is just another way of saying a “human language.” More specifically, “Natural Language” is a human language that was created “naturally”. It would be important to distinguish what is not a natural language. There are some languages that are “created in a lab” so to speak. A few of these “synthetic” languages would be Klingon (from Star Trek), Elvish (from JRR Tolkien), and also computer programming languages. Natural languages are languages that evolved over time naturally, for the purpose of humans to be able to communicate with each other. These natural languages include English, German, Spanish, and most spoken languages.  American Sign Language or “ASL” is a natural language. Sign language was created naturally. Some people believe sign language started in France in the 1700’s where people with hearing disabilities practiced an intricate collection of signs to communicate with each other. Sign language is a bona fide language. “Natural Language Processing” is when a computer is able to do “something” using real, natural, human language skills. A recent project, described by our guest and software engineer, KJ Price, involved teaching a computer to understand sign language. “I worked on the project for my thesis during my graduate degree. My team decided to try to have a computer understand sign language. This had been tried many times before, most notably with the use of haptic gloves to recognize the exact positions of the hands and fingers and the machine was to decide the correct sign related to the orientation of the hands. We found out that with machine learning we do the same thing with image recognition. At first we wanted the Mercedes Benz of sign language. We wanted it to have all the bells and whistles. We wanted a model to understand and translate full “sentences” from American Sign Language (ASL) into English. When we first got started, I knew nothing about ASL and thought this could just be a direct translation. It was actually incredibly difficult to do. There are many challenges in the teaching a machine to recognize ASL In the first couple of months of working with ASL, we quickly started to understand that we were way over our heads. We came to understand that ASL is absolutely nothing like English. In particular, the “grammar” between the languages are completely different. Our understanding changed as we came to recognize there are two distinct perspectives when it comes to grappling with language differences. People in general, the “average Joe,” focus more on on how words are different between different languages, or the etymology of words. Linguists, on the other hand, are actually much more interested in grammatical differences, or the syntax of a language. Linguists can tell how similar a language is to another language based on the grammar, or how the language structures its words or symbols to make a complete thought. Its not just about word to word exchanges. Because of this aspect, we did not realize the size of the task of teaching a machine to translate ASL language, and we were quickly overwhelmed as we realized how different the English language is from ASL. There are dozens of examples of the “syntax” differences with ASL language I could give, but just one scenario is this. In English you might say “I am going to the store”, but in ASL you would probably sign this as “Me store go” or “Me go store”. You can tell right away that there is no distinction between the pronoun “I” and “me” in ASL. Also, articles like “the” are not used in ASL. Also, the subject, in this case “store,” can be placed all around the sentence that the signer is presenting. Being able to switch the grammar is just one tiny part of fully translating the language. After syntax (or “grammar”), we really care about the semantics of a language, or the real underlying meaning of the phrase. This is a monumental task for any language, and I think this is particularly true for ASL. It turned out that just being able to translate a single sign into its English equivalent was really difficult and that is as far as we got. We ended up training a model using 30,000 images of ASL signs into their English word equivalents. We weren’t even close to getting into syntax or semantics. But what we were able to get is 100% accuracy and precision on matching an individual sign to the English word. A bit technical but for people that care, we got these metrics off of the unseen testing data to know that our model generalizes well to data that it hasn’t seen yet. Even doing the most simple task of recognizing single signs turned out to be difficult. For example, the sign for the letter “I” is made by just putting your pinky up in the air. The letter “J” is the exact same as the letter “I” except it has a little flick of the wrist. This makes our machine learning task much more difficult. We couldn’t just look at individual images to create a sign. The sequence of the images was important. This would require using an algorithm that is able to “remember” what the last image was that it was given. Other considerations to sign language are that not all hands are the same. For hands in men are differently shaped than hands in females. The dataset we used was of a man, so the machine learning algorithm could recognize the signs I performed pretty easily. My partner in the project was not able to get the same results for the simple fact that the machine learning model could not recognize her hand as well. This issue became even more pronounced for different skin tones. Overall, we were really proud of what we achieved. It took a lot of work. We published the paper which will hopefully help people get to where we got pretty easily. We also documented some of the complexities of the ASL language and Machine Learning.” To discover more of KJ’s Data Science work and knowledge of language studies, listen to the full podcast, Tech Talk: Language, ASL, & Machine Learni

    42 min

  5. 04/29/2020

    MD Talk: Mental Health & Disease Crises

    Mental health & disease crises is the theme of this podcast episode. Building mental stability, either as an individual or family, is always a benefit. But in times of disease crisis, such as a pandemic, the effort becomes critical. Hosts, author, Ann Clark McFarland, and her husband, Dr. Tim McFarland, talk with podcast guest, Rebecca Jones, a Christian counseling graduate student, about the unexpected event of disease crises such as Covid19 and how it impacts mental health. Disease crises affect our mental health in different ways. In times of disease crises, families and individuals face unforeseen challenges. However, everyone does not experiences the crises in the same way. The idea that “we are all in this together” is good, but we are actually “in it” and experiencing “it” (the crisis) in many different ways. The “new normal” seems abnormal and reminds us of loss. In addition, the “new normal” often reminds individuals or families of the losses they have experienced. For example, the smaller losses might include the inability to use prior methods of coping, which may not be available or permitted in the “new normal.” Larger losses, such as the loss of a loved one, loss of health, work, ability, or routines, can supercharge the emotions of sadness, anger, and fear. Disease crises holds both a physical and mental challenge. Recognize that coping with disease is both a physical and mental challenge. Take the first step of acknowledging the challenge. Don’t ignore or avoid building better mental health strategies in your life. Learn to identify mental health problems, find resources, and adopt best behavior practices during times of disease crises. The effort you make can make you more resilient in future crises times, and if you are part of a family, the work of building better mental health strategies can lead to better functioning homes and relationships. Helpful Resources Additional resources: Dr. Bessel Van see Kolk’s website (has details on schedule and why as well as great article for single adults) Virtual Hope Box is an app that helps teach calming/relaxation techniques (like deep breathing and meditation) as well as a few other things and resources within.  Podcast Timeline & Tips 02:00     Mental Health & Disease Crises 03:15     Mental Crisis: Global vs Personal 07:30     Negative or Harmful Responses 09:50     Signs of Starting Negative Responses 10:50     What Can We Do If We Go Too Negative 12:30     Tips to Successfully Handing Crisis 14:50     Sympathetic Nervous System – Fight or Flight 16:45     Tips for Families with Teenagers 19:00     Tips for Families with Younger Children 22:00     Tips for Families with Special Needs or Prior Mental Health Issues 23:10     Couple’s Advice 25:10     Singles Advice 28:15     Stages of Grief 31:00     Rebecca shares her faith story and Elijah example. MD Talk: Mental Health & Disease Crises is the third and final episode concerning the unexpected virus season of Covid 19. The first episode, MD Talk: Virus Habits and History, explains the behavior and structure of a virus and explores contradictions and assumptions about viruses. The second episode, MD Talk: Virus Combat Tips, identifies ways to fight virus impact and spread and boost the immune system. Future podcast episodes will focus on non Covid 19 topics. Theme subjects will be unexpected events in the STEM fields of science, technology, engineering, and math. Join us again soon as we bring to light unexpected STEM and faith stories.

    36 min

  6. 04/09/2020

    MD Talk: Virus Combat Tips To Stay Well

    In this episode, we offer virus combat tips to stay well and avoid getting sick. In our focus on the unexpected STEM subject of Covid19, Dr. Tim McFarland offers examples of the ways ordinary people can combat a virus infection. Board certified in Family Medicine with thirty plus years of medical practice experience, he currently works as an ER physician and hospitalist. Discussion topics include: Does viral dose and viral load affect your outcome? Who or what is a super spreader? Is there a better way to use my mask and gloves for grocery shopping? How do I minimize surface contamination? What is herd immunity and why is it beneficial? What old wives tales do people use to help heal? Do these things work? When do I need to get help from the hospital for my illness? If a Covid positive person is identified within a household in America, then what happens? Why are we still out of toilet paper? What impact does faith have on a crises? MD Talk: Virus Combat Tips is the second of three podcast episodes. All episodes center on the unexpected virus season of Covid 19. Our first episode, MD Talk: Virus Habits & History explains the behavior and structure of virus entities versus bacteria. The show also pointed out the contradictions and assumptions about viruses. Our third podcast, MD Talk: Mental Health During Crises, will identify strategies to sustain good mental health during times of emergency. After this series, we plan to divert to non Covid 19 topics and highlight unexpected STEM and faith stories.

    33 min

  7. 04/01/2020

    MD Talk: Virus Habits & History

    Dr. Tim McFarland, board certified in Family Medicine offers his views on the 2020 Covid 19 virus pandemic. Today’s discussion, MD Talk: Virus Habits and History explains several ideas influenced by his knowledge from thirty plus years of medical practice experience in his own clinic and current work as an ER physician and hospitalist. Discussion topics include: 1.            The differences between bacteria and viruses in structure and treatment. 2.            Existing viruses and virus impacts from recent history. 3.            Viruses as a cause of flus and colds 4.            Vaccines 5.            Super outbreaks, virus jumps, mutations, antigenic shift, reassortment of virus 6.            How contagious rate and the death rate impact public risk. 7.            Virus testing theories 8.            Assumptions and contradictions related to pandemic treatment 9.            Why is there a toilet paper shortage? 10.         What does my faith have to do with this? MD Talk: Virus Habits and History is one of three sessions regarding this unexpected virus season with Covid 19. The next podcast session, MD Talk: Virus Combat, will elaborate on strategies used to fight virus impact on our bodies. After that, in our final session on Covid 19, we will highlight mental health. Our plan is to incorporate several guests in these discussions. From then on, we hope to divert to non Covid 19 topics. Oh yah. There’s one more thing. Toilet paper hoarding. Why is it happening? Here’s ONE of the answers we found that explains why people are hoarding toilet paper. It seems this is not a new activity. Our sources say that in the Spanish Flu pandemic of 1918 panicked citizens swamped stores and pharmacies to hoard goods. One answer brought by psychologists is this. In times of imminent danger, whether natural such as a hurricane or snowstorm or something like Covid 19, people panic buy. Why? BECAUSE it gives them a sense that they are doing something to take precautions and address the risk when they have little else they can do. Stay tuned! Very soon you will be able to subscribe to our podcast in your favorite format using our Subscribe Page. Check it out. We are waiting for our show to update to all the formats. Until then, or if you would rather, join us on this website as we continue to highlight unexpected STEM and faith. stories.

    23 min

  8. 03/28/2020

    Unexpected STEM And Faith stories

    Developments in STEM fields, science, technology, engineering, and math affect our world on a daily basis, but the path to success is sometimes unexpected. These journeys of progress take “faith” (religious or not), because the developer has to believe in a result that is not yet seen. These are the journeys we hope to explore in our podcast. They are the unexpected STEM and faith stories. Co-hosts, speculative fiction author and screenwriter, Ann Clark McFarland, and her husband, Dr. Tim McFarland will interview guests about discoveries and innovations in their STEM fields. The podcast name, Paradoxifi, comes from the idea that sometimes what is proposed- “IF I do this ….then this will happen” – turns out to be successful only after an alternate course is followed. Then, it is that new journey that brings out the desired truth. Every episode starts with a riddle or question, and listeners try to solve it. The answer will be given during the show. Although guests primarily talk about their STEM subjects during the bulk of the show, at the end they will answer four predetermined questions about faith. The occasion gives opportunity for guests to express in their own words their view of faith. The goal is simple expression of perspective or experience and not an argument or an attempt to convert. A wide variety of guests and faith/or atheist views will be expressed. (Faith itself is a paradox. Although unseen, to people of faith, it is very real.) We hope that by sharing these unexpected STEM and faith stories we will provide examples of how people, inventions, and discoveries survived, thrived, and even changed through unexpected times.  Join us as we dive into these unexpected STEM and faith stories. Very soon you will be able to subscribe to our podcast in your favorite format using our Subscribe Page. Check it out. Until then, keep coming back to our website where our shows will be featured. Let’s get started.

    2 min
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Unexpected STEM and Faith Stories

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  • Creator
    Ann Clark McFarland
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