 And Heather says Seattle based midwife educator in clinician with a passion for midwifery and women's health. She strives to expand and diversify the midwifery and women's health nurse practitioner workforce address inequities within health care and improve perinatal outcomes. She currently serves as the assistant program director for the nurse midwifery women's health nurse practitioner and women's health midwifery programs at Georgetown University School of Nursing in Washington DC. And clinically she's been a midwife since 2002 and provided full scope midwifery care at Evergreen Health in Kirkland, Washington, where she's now per diem. She has attended almost 800 births and I'm going to skip a little bit here. She's currently pursuing a PhD in nursing science from the Vanderbilt University School of Nursing studying why birthing persons with higher body weights are more likely to birth via C section, cesarean sectioning, focusing on weight bias among nurse midwives. And her doctor on her doctoral committee is Dr. Jeremy Neal who is joining us on this call. So welcome to there both. And now I am going to turn it over to Heather. I can get my, I can get my, here we go. Make presenter. Great. All right. I'm going to mute myself. Take it away. Great. Thank you so much, Catherine. It's a real honor. What an exciting day for midwives. And I'm just tickled to be here. A lot of people in the room that are near and dear to me. So thank you so much for being with me this morning. It's kind of a gloomy morning here in Seattle. It's pretty gray, but this room is very bright. So thank you for that. I want to first just acknowledge funding for my PhD research by the ACNM Foundation, March of Dimes, Sigma Theta Tau, Sciat Large Chapter and Vanderbilt University. And also a big thanks to the American Midwifery Certification Board who agreed to send out my survey to all CMs and CNMs in the United States. And then of course, to my dissertation committee, Dr. Neal, who's with us this morning, Dr. Philippe Dietrich and Dr. Poole. And I just also want to take a pause. I've talked a lot, given a lot of podium presentations about weight bias. And I think it's important to just recognize sometimes this can be hard to hear when you're thinking about perhaps experiences yourself of experiencing harm in maybe the health care setting or in other settings, maybe in school or in your workplace. But also as practitioners, you know, we all have bias in some way. And I think it's important to just recognize sometimes we have caused harm. And I want to just start with everyone has bias. And our goal is really just to mitigate that and think about how we can do better. So that was really the impetus for my PhD studies and what I'll be talking about today. I want to first just start with some definitions. Weight bias can be thought about in two different ways, both in implicit and explicit ways. I'm going to be really focusing on weight bias as thinking about devaluation of someone who lives in a larger body, so someone with a higher body weight. You can have weight bias towards someone who lives in a smaller body, but the focus of my research has really been on what's more prevalent in U.S. culture of bias towards someone with a larger body. So as I mentioned, it can occur in the workplace, educational settings, media, interpersonal relationships. And I also want to just say that the sort of the assumption, the unconscious experience of weight bias is that you're assuming that someone who lives in a larger body, it's really a matter of responsibility and lack of self-care, that it's really willpower that has led to them looking at the way that they do and personal decisions. But what we really know a lot of has been studied and many by experts in this room that it's much more complex and multifactorial, that there's a lot of different factors that lead to what someone looks like in terms of their body size, metabolic, behavioral, cultural, familial, environmental, and socioeconomic factors that all play in there, including racism. So I want to just call that out. And that's a whole separate talk that we won't get into, but I just want to lay the groundwork there. OK, so I want to just start with some definitions. The World Health Organization and National Heart, Lung, and Blood Institute have categorized these particular categories on the screen here using the Body Mass Index, or BMI. And so the focus of my research has been on focusing on people with a BMI of 30 or greater, which is considered the obese category according to these organizations. And so I want to just first share that that was the focus of my studies, but you won't hear me use the word obesity at all or see that word in my content at all. The word obesity actually means to eat oneself fat. And it's considered in terms of people that live in larger bodies, the most offensive of terms. And so I'm really trying to move away from what is considered harmful in itself of using that word and using language that is preferred by the patients that we care for. So the words that I tend to use are higher body weight or someone who lives in a larger body. But I am focused on folks with a BMI greater than 30. I also want to just call attention to the use of BMI. BMI is not a good measure of health. It was really designed for measuring large populations of cis white men, and it's really not designed for measuring one person's individual health status. Unfortunately, we use it as really kind of the fifth vital sign when we walk in and see a patient, but it doesn't take into effect adiposity, bone structure. It doesn't really measure any of that. And so I just always suggest we give pause when we use BMI as a measure of health. But essentially, in the world of health care, it's what we have. Obviously, we need to find something better. But for now, that's sort of how it, you know, one of the measures that's used. But I just want to call attention to that. OK, so just giving some history, you can notice the red line over time. Essentially, in the US culture, our rates of BMI are going up. The black line is someone who is considered, quote, unquote, normal weight. And again, I'm very cautious of the use of the word normal. But you can see that number, that black line has come down. So not surprising statistics there. So just a quick overview of the harm of and what we know about weight bias in health care. It has been studied a fair amount in the health care setting, but not very much in perinatal care, which is really what my focus is. But in terms of broadly within the health care setting, we know it's been measured and found both implicit explicit levels in many health disciplines. It's been studied among nurses, physicians, dietitians, nutritionists, physical therapists. There's been studies of prevalence of weight bias among physicians who specialize in care of patients who live in larger bodies. So it's essentially everywhere in terms of health care, but has not been studied in the perinatal space. We know that weight bias is associated with negative communication patterns, less active listening, less rapport. Providers spend less time with patients that live in larger bodies without, you know, very unconsciously. Clinical decision making can be affected. So all sorts of what is offered, not offering a treatment because of based on someone's size, even though there's evidence that says that it would be of benefit. So overall quality of care has been studied that weight bias in the health care setting strongly impacts quality of care. Consequently, patients who live in larger bodies have developed a distrust of the health care system in many settings. We know we have higher rates of unplanned pregnancy in folks who live in larger bodies. Patient satisfaction rates way lower. I was reading a study about higher rates of patients who transfer care during their pregnancy because they feel harmed by their provider. So all sorts of problems of what we know about what's occurring in the health care setting related to discrimination towards people that live in higher body weights. And then there has been studies that look at lower rates of cervical and breast cancer screening again because someone does not want to pursue care. So they're forgoing a pelvic exam because of harm, like feeling mistreated, not wanting to have a mammogram. In terms of the adverse health outcomes, we know that when weight bias occurs in the health care setting, it contributes to patients who have increased food intake, less activity, poor body image, body dissatisfaction. All of those you can imagine are all associated. Low self-esteem, waking, depression, anxiety, stress, even suicidality and substance use. So we have a lot of work to do in improving the care that we're providing. Again, this is all in general health care setting, nothing in the perinatal space. What has been studied is we know that postpartum and pregnant persons are reporting that they are experiencing harm. This was a relatively small study, but 70% saying they experienced weight bias during their pregnancy or postpartum care. And we've talked about strain communication that has been studied in the perinatal space. Patients reporting, basically not feeling like they're getting the information that they need from their midwives and obstetricians about weight, weight gain, or nutrition. One really, really exciting report did come out. You're probably familiar with this birth settings in America. It was a huge, huge consensus study report by the National Academy of Medicine. And it had one little paragraph that talked about weight bias and how it can be associated with poor reproductive health outcomes. No studies have been done that have looked at that association. But for me, it was exciting that there was a call out to basically say, let's start to think about this. But as I said, it has not been measured. So here were the aims of my dissertation. I'm presenting preliminary findings today, not all of my findings. I plan to defend or hope to defend in June. So still working through some data analysis. I'm going to discuss today our goals were to develop an explicit weight bias among midwives in the United States. And to see if there was an association between those two types of bias, comparing them to other health professionals and the US public. Here is my conceptual model. This is really where I see the future of my research going. I'm really interested in decreasing C-section rates among patients that live in larger bodies. That's my ultimate goal. And really thinking about that statistic of, we know that women who live in larger bodies are three times more likely to have a C-section compared to someone who lives in a, quote unquote, normal with a normal BMI. So I'm interested in what is the phenomenon of going, what's going on during labor in terms of the care that we're providing, both unconsciously and consciously? What interventions are occurring that probably are not evidence-based? Are we allowing, excuse me, I shouldn't say the word allow, supporting physiologic birth, what's happening in terms of the care that's being provided? And my hypothesis is that we're contributing to unnecessary cesareans because we're not suggesting perhaps use of the tub as a form of pain management because we're not thinking, oh, I'm not sure this patient wants to get in the tub or I'm not sure I can help the patient get in and out of the tub or not supporting movement, ambulation perhaps, because in our minds we're subconsciously thinking, I'm not sure that this person has the stamina to walk around the labor unit. So I'm just gonna encourage an epidural. Again, these are things I'm really interested in studying but that's sort of what my conceptual model is getting at here. So in my PhD study, that's interesting, my table on the left did not show up, sorry about that. It was a retrospective cross-sectional study. I sent a survey through the American Midwifery Certification Board to all midwives in the United States, which is just under 13,000 midwives. And I solicited directly to midwives to fill out my survey last year at our ACNM annual meeting in Chicago. It was sent out via the listserv several times. And then I also used social media, specifically ACNM, our professional organization has a listserv called Connect. What was on the left here are basically the measures that I used. So I'll just describe those. I used for measuring implicit bias, I used the implicit association test or the IAT. If you're not familiar with the IAT, I would check it out. It's a free survey or free assessment that you can take. It's hosted by Harvard, it's called Project Implicit. So if you just put in IAT or Project Implicit, there's a bunch of different assessments that you can take, not just weight bias, but perhaps other forms of discrimination that you're interested in exploring about yourself. But I really encourage, it takes about eight minutes and it's free, so just encourage you to look at that. And then I used several explicit bias measures. One of them was called the fat phobia scale, one was called the anti-fat attitude questionnaire. I chose my explicit measures very specifically. I was interested in measures that used language that really got at willpower, endurance, self-esteem. So asking the question, when you think about someone who lives in a larger body, what do you think about their endurance? What do you think about their self-esteem? Things that I chose these measures because I'm thinking about treatment, how we're feeling about patients as we're caring for them in labor. So perhaps we're making some unconscious thoughts around, I'm not sure this person has the endurance to do this. So getting at that, those are how I chose my explicit measures. So here is my response rate, let me just make sure I didn't miss this slide there. Okay, great. So slide 26, here we are. Here is my response rate, 17%, just over 2,200 midwives filled out my survey. The median age was 46, median BMI 27, which according to World Health Organization CDC is considered in the overweight category, very geographically diverse and very representative of CNNs and CMs in the United States, mostly white, mostly female, almost 98% female. We do have just under 2% CMs in the country and that was representative here. Median number of years since certification 11 and then most of the respondents had an active license more than half, almost 70% were working full-time and then around 65% were attending births and of those who attend births, about 88% were attending births in the hospital setting. So I'll go through this slide relatively slowly, but I want you to start your, move your eyes to the top left under current study in total. So essentially this is one of my measures looking at implicit bias using that tool that I was discussing on Project Implicit and it is measured through what they call a D-score, which is from minus two to plus two and plus two indicates that you have implicit weight bias, minus two means you prefer, you have preference towards people who live in larger bodies. So I had again just over 2,200 respondents for this particular survey measure and the D-score among the midwives was 0.36. And I was interested in, how does that compare to other health professionals? So if you move to the right, there was a study of 2,200 physicians in 2012 done by Dr. Saban. And in that study, the D-score was 0.4. There's been ongoing collection of data through the IAT site of the US public and that very large study was just published in 2022 and reported a D-score of 0.48. So you can see that the midwives D-score, 0.36, was lower than health professionals and the US public. And in the statistics world, those were all statistically different, even though they seem quite close together, they were statistically different. What's interesting though, is if you just pulled out the physicians who were women, who identified as women, so now I'm in the lower middle box where it says 1,258. So of the 1,258 female physicians in Dr. Saban's study, the D-score went down a little bit of 0.37. And that is consistent with what we know that men tend to have more weight bias than women. So I thought that was interesting and I compared just the women in my survey. Again, it was mostly females who filled out my survey, who identified as female. And that was 0.36 still and then 0.37. So those were not statistically different. They're similar. And so that I think is interesting. But essentially what we now know is that female midwives have a similar level of implicit weight bias as female physicians, which I think is interesting. One might suspect, well, our midwives, maybe we have less weight bias because we're in the caring profession. It's our model of care thinking, perhaps that was the case, but what we learned is essentially, we're like other health professionals in that respect that we also have same levels of implicit bias. And looking at it in a different way, this graph shows midwives only of how much weight bias we had. And the D-score actually is put into three different, what they call cut scores, putting them into categories. So slight, moderate, or strong. And you can see the numbers at the bottom there. If you have a D-score of over 0.65, you're considered to have strong weight bias. And so if you group those slight, moderate, and strong together, it's around 70%. So what we can say is 70% of midwives in the United States have some level of implicit weight bias. And around 20% have strong levels of weight bias, which again, I think is interesting. Okay, now I'm gonna move on to explicit weight bias scores and what we learned. Again, I mentioned the fat phobia scale and the anti-fat attitude scale. So again, I'll start if you wanna draw your attention to that top left where it says current study. The fat phobia scale is a Likert scale, measures from one to five. And again, looks at things like endurance and willpower and self-esteem. And I wanted to compare that to other health professionals. And so if you compare that 3.24 to the 3.48, again, this was not Dr. Saban study. This was a different study among all groups of health professionals. So nutritionists and dietitians and physical therapists and physicians and nurses were lumped into those 878 people. And again, those numbers are very close, 3.24 to 3.48. You can also see US public 3.38. I suspect that number is maybe a little bit lower than the health professionals because it was different population, but essentially all very consistent, but statistically different. And then if you look at the anti-fat attitude scale, that scale went from one, I'm sorry, from zero to nine. And midwives measured on average 2.46 on that scale compared to health professionals 2.85. So again, what this tells me is that midwives have explicit weight bias, but essentially at weaker levels compared to other health professionals and the US public. And then a third explicit weight bias measure that I looked at was something called the preference for thin people. And essentially asked the question on a Likert scale of one to seven, do you strongly prefer fat people or do you strongly prefer thin people? And so you can see the current study. This is where midwives tallied in that column compared to the Dr. Saban study of US physicians from 2012. And essentially you can see that our rates are lower as compared to the US physicians. Specifically, I think it's interesting to look at the column where it says slightly prefer thin people, a little more consistent there, 26% compared to 28%. And then obviously bigger differences when you get into moderately and strongly prefer thin people. So interesting, I think. And then I looked at just females. So again, going with my earlier, if you've just looked at female physicians pulling out the male physicians that have higher weight bias than tend to have higher weight bias than women. Again, still statistically different, but a little more consistent there if you look at the slightly preferred thin people, 26%, 32%. So all similar, I didn't really glean out anything new there, just we are statistically different. But again, showing that midwives do have explicit weight bias at some level. So what we can say is a couple of broad statements. Midwives have moderate levels of implicit weight bias and it is similar to female physicians specifically, but lower than physicians or as a whole and lower than the US public. And we do have explicit weight bias, but again, our scores are lower than other health professionals in the US public. So moving forward, the next part of my data analysis that I'm interested in that I will be doing in the next bit of time here is really looking at how our weight bias among the midwives, how it varies by different demographic factors. So I collected race, ethnicity, age, BMI, years in practice and region should be on there. And I'm interested in looking at how does it vary? So this will, I think that's interesting because it will help us tailor perhaps our interventions. My goal is to help think about talking about weight bias with midwives across the country and how will that intervention be tailored? What particular types of midwives is it people who've been practicing for longer or is it our newer graduates who have higher levels of weight bias? Is it higher among white midwives versus black? Is it, it's interesting to think about someone's BMI. One might assume that someone who lives in a larger body would have less weight bias. So I'm really interested in diving into that data. And then moving forward after I defend my dissertation, my goal is to really move into clinical decision making and looking at what's going on in the labor process, what interventions are happening, specifically looking at some data set, a data set to see if there's an association and really hoping that if we can identify whether weight bias is a contributing factor to unnecessary C-sections in people who live in larger bodies. So that's where I'm headed in the future. So at this point, I am happy to take questions. We are a relatively small group of 41 people. I can take questions in the chat or we can end early, whatever folks would like to do. No, I think people should have some questions and want to hear a little bit more about, I don't know how you untangled. I'm curious about how you untangled all this data. It just seems like an overwhelming amount of categorized and organized and all that. So especially the implicit bias part. Well, I have a great committee. I think that it's interesting to me, where I thought you were gonna go, Catherine, is the untangling around. There's so many things that contribute to, if you have a laboring patient with no risk factors who walks in to a birthing setting in spontaneous labor, what are the things that happen between when they walk in that space and when they end up on the OR table that could have contributed to that C-section and what happened that maybe they could have had a vaginal birth. And to me, the untangling that I think is gonna be challenging is how much can we say it was related to bias that occurred, specifically weight bias? I'm really interested in that because there's a lot of different factors, right? And the clinical management is the art, really, of the care that we provide, the clinical management decisions that are occurring. So that's really interesting to me, but I'm really hoping that I can have a small impact on perhaps elucidating information about ways that that might be impacted by weight bias. Absolutely. Well, I think you definitely identified a key topic, both for providers and researchers. Cecilia Jevitt says that she is not surprised that healthcare providers have biases that are close to the US population because we live in a culture long before we study health and this study begs for more education about weight and health for providers. Cecilia agrees and Megan Arbor says, well, can we brainstorm? What are your wild brainstorms about how to reduce implicit bias? What kind of interventions work for addressing them? Yeah. So, Megan, thanks for that question. I'm happy to share we wrote a team of folks and I wrote an article about mitigating weight bias in the clinical setting for general midwifery and women's health. It's not, hasn't been accepted. It's not off-depress or anything yet, but really what we wrote about was number one is starting with self-reflection. And I think that for a lot of people, when I talk about, hey, I'm studying weight bias, they're like, I never thought about that. Like, this came from when we were children, our parents inculturated us with, this started so early. If you look at types of bullying that occur in elementary school, weight bias is number one over racism or gender bias, this starts really early. And so I think the first step is self-reflection. And then really the next steps are just all the things that we do well as midwives, thinking about language, thinking about shared decision-making and partnering. The number of times in this work that I've done, people tell me stories. I went in for a sinus infection to be treated and I left being told that I need to lose weight. So it's just so prevalent of like people think they're doing someone a favor by saying you need to lose weight, but we know that recommending weight loss does not really work. It's about a lot of different things. And we talk about health at every size is our framework. But I just think that self-reflection and then just the things that we do well as midwives, applying it to these principles of shared decision-making language and just reframing how we approach our care, I think is really important. Oh, thank you. Well, Seal points out that there are actually international guidelines for quote management of labor for people with obesity, but from the US it does seem to push people down the path towards intervention. Grace, I was just to comment, but Grace Daniel, it's following up on interventions in the future, which I think you addressed. I was just gonna answer that, Katherine, if I could. My first step is really just to look at the evidence. Interventions is maybe five to 10 years from now. I feel like we have a lot to study because as I said, it's been, this was the first step to really measure weight bias. We didn't even have, it really has not, it has not been measured among obstetricians specifically. There was one small study that looked at 27 obstetricians at one hospital in the Midwest, but essentially it has not been studied. So we have a lot of work to do, but my goal is, yes, to move towards interventions, but that's later in my research. Sorry to interrupt, Katherine. No problem, thank you for addressing. Jeremy is interested in what is known about the presence of implicit bias, how it influences clinical decision-making in other, i.e., non-paranatal settings. So primary care basically, is it similar, different in primary care settings? Yes, thank you, Dr. Neal, for that question. There have been studies that have basically shown, you're given a scenario, here's the patients that are presented and then what treatment options are being recommended and clinical decision-making was impacted by the patient's size, even though the treatment was completely unrelated to what someone looks like. So, yes, there is studies in that space. There's actually a lot that's coming out, of course, around racial bias and variations in clinical decision-making and treatments, but nothing in the weight bias. Just very few small studies on weight bias, I would say just a few. And with Margaret, who is in the UK, mentions that they also do automatic, she says, referral testing for gestational diabetes. So I guess, would you consider that one intervention that might be related to bias or is it clinically appropriate, regardless of that? Yeah, so I think it's, yeah, that's kind of a complicated answer. My focus has really been on labor, but my understanding is, there are different guidelines that you can use. I think Varney's Woodwifery does say, if you have a BMI over X amount, then it is recommended for early GDM testing. That's the evidence that, what our students are being taught based on the latest edition of Varney's. But I think that does vary based on what guidelines you're using. All right, well, Ciel also addresses the labor issues, up to 50% of bias towards giving up on labor early. And then Susan, we're gonna have to wrap up here pretty quickly, but Susan Egglebert says when she was a family nurse practitioner, our Ann, the provider just told the woman to lose weight and her back wouldn't hurt. But you know, everybody has back pain. People have all sides of back pain. Right, the stories are the story, it's been really, you know, oftentimes, so we do a weight bias talk in healthcare with our students at Georgetown. And it's quite common that a student needs to get up and leave in tears because they've been harmed or they, you know, tell stories. It's just, there are a lot of stories. And I think that as Megan asked earlier, like what can we do, you know, again, just starts with reflection. There's, I see a couple other stories here that are also just as appalling. So yeah, lots of work to be done. Right, and let's see. Oh, well, who, two people, I guess Megan and Seal both introduced their experiences of midwifery educators and how we can and should address this with our students and also our physician partners who, yeah, I see that story from Seal that's about a physician said you ate yourself into this C-section, sorry. Joy compliments you on your fantastic work. Just be tuned for when Heather publishes, right? Thank you, yes, working hard. Yes, really appreciate you all being here today. It's really been an honor. And I would love to collaborate. I appreciate insight collectively in this room. A lot of great experts in the field. So thank you very much. Great, and Andrea, I hope I'm pronouncing her name correctly, is a member of the Obesity Medicine Association, which, you know, does, well, she recognizes that biases are ingrained in stigmatizing, I guess within that association and without. We talk about textbooks. Let's see, Helen talks about the education again, what we've been taught about health issues associated with weight and what studies untangle bias from real issues. And that's gonna be our last question because we do have to finish the wrap up the session. So last question, what studies untangle bias from real health issues? Well, that's a very complicated question I probably can't answer in this short time. But I think that we have a lot of work to do and I welcome collaboration. I think that, you know, textbooks are really, really important. It's, you know, and textbooks take a long time from the moment you write it to the moment it's published. So I just encourage you all to think about language that you're using, you know, ways that we're talking around, you know, care of patients that live in larger bodies. Text, I'm happy that in the next edition of Varney, there's gonna be some therapeutic communication content in there and we've tried to incorporate some content around weight bias in this arena. So, but yeah, lots of work to do.