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Ethical Considerations in Digital Mental Health
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Hello and welcome. I'm Dr. Amy Cohen, Program Director for SMI Advisor and a clinical psychologist. I am pleased that you're joining us for today's SMI Advisor webinar, Ethical Considerations in Digital Mental Health. SMI Advisor, also known as the Clinical Support System for Serious Mental Illness, is an APA and SAMHSA initiative devoted to helping clinicians implement evidence-based care for those living with serious mental illness. Working with experts from across the SMI clinician community, our interdisciplinary effort has been designed to help you get the answers you need to care for your patients. And now I'd like to introduce you to the faculty for today's webinar, Dr. Camille Nebaker. Camille Nebaker is a research ethicist at the University of California, San Diego, and the Director of Recode Health. Her research has applied training in public health, lifespan development, and the science of teaching and learning to promote community capacity to advance public health research and ethical standards to guide technology-enabled research. The goal of Dr. Nebaker's research is to increase understanding of the ethical and social implications of behavioral and biomedical research with a focus on digital mental health. Dr. Nebaker, thank you for leading today's webinar. Thank you so much. It's a pleasure to be here, and I'm really looking forward to sharing the work that we've been doing at Recode Health, and that stands for the Research Center for Optimal Digital Ethics in Health. And so for today, what I'm going to be talking about are the different kinds of digital technologies that are used in health research with a focus on mental health studies. I'll also describe several ethical challenges that are associated with digital health, and I'm going to introduce a digital health decision support tool and framework that we've developed to help researchers, clinicians, tech developers, participants in research, patients who are making decisions, and basically consumers who are looking to adopt a health technology to help them manage their own health. And so also, you know, thinking about what are some of the barriers to making sure people are informed about the choices they're making. We'll all talk about informed consent and some of the challenges to actually getting meaningful informed consent. So before I get too far along, I want to talk about what I mean when I talk about ethical implications. And this acronym ELSI was developed back about 30 to 35 years ago when the first monies were made available to study the human genome. And the NIH required and basically earmarked 10% of that budget to go into studies of the ethical, legal, and social implications of genomic and genetic research. They knew it was really important to study these implications, and so they coined this ELSI term, which I've been adopting for use in the work that we're doing with digital health research. And so when I talk about ethics, I'm very much a practical and applied thinker in this realm. And when I first started doing this work, it was really because behavioral scientists were having such a hard time getting approval from institutional review boards, which are the bodies that approve research with human subjects. And being that I had been involved with IRBs for 20 years and also part of a behavioral science department, I really wanted to look to see what are the new or nuanced ethical issues that people were struggling with. And in research ethics, which is slightly different than clinical ethics, we have three principles that guide practices. And those principles are respect for persons, beneficence, and justice. And how they are practiced is with respect for persons, it happens through the consent process. So the principle is respect, the practice is informed consent. With some of the technologies that we have seen used in digital health research as well as clinical practice, bystanders are often picked up. These are people that are nearby a person that is using the technology. And so we've had to start unpacking things like bystander rights. These are people that if you were doing research, they would not be considered participants. And I'll get into that as we talk. I just want to really kind of give everyone some grounding on how I put these different areas of this ELSI acronym into these buckets. And so the other thing about the principles, the ethical principles is beneficence. And that is how we think about risk, how we think about risk management, and then weigh those risks and the strategies to mitigate risk against the benefits, the potential good that can come not only to an individual, but to society and people like that person. And justice, the principle of justice is really trying to make sure that when people are engaged in a research study that will then inform clinical practice, that they resemble the people that are most likely to benefit from the knowledge gained. And so with digital technologies that are used in health research and clinical care, we really want to make sure that those technologies are accessible, that they have been developed with the person in mind who is going to use it, and that it can be used both short-term and long-term. And so that is kind of what I call the ethical lane. So risk and benefit, consent, and enrollment of people that are most likely to benefit. The legal and regulatory lane or bucket is taking into account the federal regulations that guide some of the practices. In research, it would be the protection of human subjects. We also have to think about conflicts of interest. And more recently, there's been new regulations that are focusing specifically on an individual's right to have their data shared by an entity. And so Europe came up with the GDPR and implemented that in 2018. The CCPR is California's version of the general data protection regulation that Europe has, or the EU. And so the CCPR went into effect just this year in California. And from what I understand, there's going to be several states that start to pick up these privacy regulations and state by state will start seeing an increase in regulations around privacy of health data and other types of data that are being captured by digital platforms. The social lane or bucket is the things that we really have to think about in terms of societal benefit. And I think about this pandemic that we're experiencing right now and the pressure to put digital tools in place that can be used for surveillance at the expense of a person's right to privacy or their expectations for privacy. By pushing some of these technical solutions out quickly without a lot of input from stakeholders, we could in fact be introducing some pretty significant risks and unknown unknowns. And so this is an area that really requires a lot of thought. And so as a person in this ethics LC domain, this is the work that I've been doing for the past 25 years, really thinking carefully about how do we do this research responsibly and ethically and engage the right stakeholders in the process of deciding what that is. And so I just wanted to use this first few minutes to give everyone a grounding of what I'm thinking about when I use the term ethics. So to give you some background of what we're talking about when we talk about digital health, this is a figure that a colleague of mine at UC San Diego, Dr. Kevin Patrick created probably about three or four years ago. And I think what's so fascinating about this, and it's just such a nice way of showing all the different sources of data that are actually health data. And we may have been thinking, oh gosh, the electronic health record is our go-to, that's where there's so much important health data. And those of you on this webinar are probably well familiar that the electronic health record was designed as a billing system, not as a source of health diagnostics, but it's being used incredibly useful for many in terms of applying artificial intelligence algorithms to make predictive analytics. And in some cases, it's really helpful in terms of reducing burden on physicians, but it also is a tool that can be potentially dangerous if the data in that database are not representative of the people that we are trying to help. The other source of health data that is really taking off is the microbiome. We've had a long history of going through and accessing genomic data. Environmental data is something that is really telling with respect to how people are moving through their environments, where they eat, where they shop, how they move. And so for public health, for healthcare, we really have to take into account all of these different sources of health behaviors, including social networks that can be used to do predictive analytics on outbreaks. I don't know how many of you were following how Twitter was being used right after the outbreak in Wuhan, China, to identify how people moved out of that province around the globe to be able to see how this disease was going to spread. So when we think about all of those different sources of health data, there are different types of tools that are being used to capture health data and or to capture information using survey techniques. Instead of asking somebody to recall what they've done two weeks ago, you can actually deploy a short survey through their mobile phone using a tool called ecological momentary assessment. And so rather than have somebody come into a lab or to recall behaviors that have happened in the past, you can deploy these surveys in real time and capture accurate information in real time from a participant. So the mobile phone is one method of capturing information about an individual. This is the tool that actually got me involved in digital health research ethics. And my colleagues in behavioral medicine were really interested in how healthy people behave in the wild. And so again, rather than asking them to come to a laboratory and disclose what they were doing and trying to remember, they would ask them to wear a camera that faced outward and the camera would record what they were doing or basically first person point of view every seven seconds. And so by the end of one week of participating in a study, they would have about 32,000 images of their everyday life stored on one of these types of cameras. So this camera up here on the left upper is called a sense cam. It was made by Microsoft and it was initially made to help people experiencing early onset Alzheimer's or dementia remember what they had done during the day. So it was used for assisting in recall. This one here is called the autographer and this camera is one of the research tools that was used in looking at everyday behaviors. It's been used with children, with older adults up to 102. And again, these are just new tools to better understand how people behave in their everyday life. This is a sensor that was created by a colleague of mine that's in bioengineering. And he has used these kinds of sensor tools. This is about an inch and a half by an inch and it's placed on the skin. It's like about the size of a postage stamp. And you can see that it has temperature gauges, ECG sensors, EEG sensors. It can power itself. It is able to transmit data wirelessly in real time. So one of the applications that he used this for was to detect fetal heartbeat to identify whether or not, and also maternal contractions to identify when mom needed to be able, needed to transport to the hospital. So these are new applications that are getting incredibly more sophisticated. On top of that, we have all of these different kinds of apps that are not intended for clinical use. They're intended for consumer access and, you know, whether it's Runkeeper or Facebook, there are a lot of different kinds of social platforms that are available now. We have all kinds of apps that can be downloaded and how we use those apps in research or in clinical care is really important because you may have heard this from other presenters. And my colleague John Torres has done a lot of work on privacy policies that are associated with apps that people may use for meditation or to track their steps and other kinds of behaviors. So with these new methods, we have new sources of data, new types of data. The data are very, very granular. There is a volume, a volume of data that has not heretofore had to be thought through by, in the case of researchers, by IRBs. And so you'll see here, this is a GPS tracking device that was, you know, this is from five years ago. This was a commercial grade GPS tracking device that would show where an individual had traveled within. This is a map of San Diego County. You can see down here is the border of Mexico. You can see this person was going back and forth across the border. These traces can show to the minute where a person was at a given time. So it shows activity and this is an image of what this autographer camera, this is actually a sense cam, what it's taking. So instead of asking a person to disclose what their food intake was or how active they were, by combining these different kinds of wearable sensors, you can actually observe whether a person's active or passive. You can confirm a dietary intake to what they're actually eating. You can just capture so much more data than you ever could before to better understand a person or an individual and their patterns. When we started the research that we're doing, these commercial products did not exist. So Fitbit had not yet come out. And one of the things that I found really interesting is that once they did come out, their terms of service and privacy agreements, if they in fact had a privacy agreement, were not in the best interest of the consumer. And in research ethics, especially with research that is governed by the federal regulations, there are statements in the federal regulations that require that the research not jeopardize the rights of an individual to file a claim if they're harmed. What Fitbit has done is they've waived that, they've basically said that they have no right to file a claim, which in fact contradicts the federal regulations. And so for a researcher using one of these devices, they would not be able to use it without violating the federal regulations for human research protections. And so there's been some interesting tensions between these consumer grade wearable sensors and researchers who want to use them to capture information about research participants. Again, with the explosion of health technology companies, I think it's really important to pay attention to whether or not these companies are vetted, whether or not they have proven any type of evidence to show that they're effective. You'll see that on these little clips of information, like this is an online mobile meditation content in the health and wellness space. We are not a healthcare or medical device provider, nor should our products be considered medical advice. So these disclaimers are in many of these different kinds of products. So in the health technology space, there are some companies that are really doing excellent work. And there are companies that are not even aware that they have standards of excellent work. So this is just an example of things that we need to be paying attention to as stakeholders in this space, so that we can not only make good decisions about how we think about use of these apps for ourselves, but for research participants, for patients, and generally how are we making decisions about what choices we make. So with that, we have new challenges and opportunities. And basically we can monitor or be monitored. We can deliver interventions 24-7 on the fly and in real time. And so never before have we been able to do this kind of research or clinical management. Now that we have these technologies, we can actually do things that we've never done before. Another thing that's really shifted over the past decade is that it used to be that academics, research institutions were those that were doing the majority of the research. And now what we're seeing because of these new tools is that technology companies are now in the health care space. They're doing health care and health research. We have seen an explosion of citizen science and people that are doing what's called quantified self, doing a lot of measurement of their own bodies and conducting experiments on themselves. So some of these tools are regulated, some of them are not. So the other thing is because of the granular nature of the data that are being collected, we can't promise anonymity anymore. And again, as I mentioned, the regulations are variable across this ecosystem, which introduces a lot of confusion. And so I think this is an area that we really have to be paying attention to. And something that I've tried to do, and I'm still tinkering around with this figure, is trying to map who is in this ecosystem, the extent to which they're regulated or not, whether they're trained or not. And when I say trained, I'm talking about trained in the scientific method. They actually have the formal education to carry out research experiments or provide clinical care. Now, we have people that we've worked with in participant-led research, which is also sometimes called patient-led research, where they may be brilliant engineers or CPAs, but they're not researchers or clinicians. And so they have very little acculturation with respect to ethics in their training. They may not know how to think about risk and benefit. They may not have a good sense of how even to think through what decisions they're making. And so I've worked quite a bit, if we look over here on the left side, with people who are learning how to use these tools to solve their own problems. And these are people that, in many cases, feel that the healthcare system has failed them, and they want to start using different kinds of self-tracking tools to help them better understand what's happening in their own life, in their own health. And then in some cases, they want to communicate this to their healthcare provider. In other cases, they've gotten fed up. And so I've helped a couple of these groups think through how would they implement ethical practices given that they're not regulated, they are not well-trained, and they have no ethical acculturation with respect to doing this kind of work. The other group that I've worked quite a bit with are community health workers and promotoras. And so I'm in Southern California on the border in San Diego, and we have a very large population of Latino in this area. We also have a large refugee community and Native Hawaiian Pacific Islanders. And so community health workers, which, I mean, they have probably 200 different classifications across the globe. Promotora is a term used locally for Latina, mostly Latina women, although not exclusive. But these are people that are from the community. They know the customs, the traditions, the language of the community. And they are the bridges between the academic researcher and the community where research may take place or any kind of access to improve the health of a community. So I've worked with the community health workers for 20-something years and have focused a lot of my work on developing educational materials to help them understand what the scientific method is, how do you design and implement research, how do you work with academic researchers, what is their role on research studies, how do they manage data, how do they implement informed consent. So that is a group that I think is increasingly becoming more important because especially right now with the need to do contact tracing around COVID, community health workers are playing a really key role in our area. And with the Native Hawaiian Pacific Islander community, they're called community health navigators. Like I said, Latino population, it's promotor, promotoras. So that is another area where they're unregulated except when they're working with academic researchers who are, they have very little formal research training, but they have very much a role in research. So it's really important that they do have the training and they want to learn how to do research ethically. So this group here falls in the middle. And then we have here bio and the pharmaceutical and the tech industry. I would say pharmaceutical is going to be more like academic research where they have a lot of rules. They have extensive training for research ethics and documentation, informed consent. This group here that I was thinking about is big tech. It could be health tech startups where they may have regulations and it may not be specific to research. So unless you accept federal dollars from Department of Health and Human Services to conduct research, you don't have to sign an agreement with the government that you're going to follow the rules or the federal regulations. So a lot of the health tech companies that are doing research don't have the same regulations that those of us in an academic environment have. So because of that, I've put that group, you know, many of the people that are working in this industry have been, they're formally trained clinicians, psychologists, psychiatrists. They have gone from an academic setting into industry. So they bring their knowledge with them. They also often have extensive clinical and research ethics training. But what's variable with that group is the degree to which they're regulated. And so if you have questions about this figure, I would love to talk more about it to figure out how do we make it a little bit more comprehensive. So with this comes a lot of research questions that we've been asking over the years. We're trying to understand whether or not machine learning and artificial intelligence can make clinicians more efficient and effective. What kind of things we need to look out for. I'm working now on a study called artificial intelligence and healthy aging. So we have a hundred people enrolled in a study. They're all over 65 and we're learning how do people age over time and how might we use artificial intelligence to improve the likelihood that they can continue to live independently. So is it possible to put sensors in somebody's home, on their remote, on the floor, under their mattress, on the teapot, on the canned goods that they have in their refrigerator to understand how they move through their environment every single day. So that when things start to change, we'll be able to pick up those micro behaviors and maybe make inferences about what's happening with that individual. Those kinds of studies are happening and they could end up really helping people in the long run. But there are a lot of really interesting privacy issues that we have to think about and work through. And we have been doing a lot of that work with the older adults that are participating in our studies to find out what they want back in terms of information, what would be valuable for them, why would they end up staying in a study for five years without getting anything back of value. So we're working on that. We also, as I've mentioned, the governance of this digital health environment is not consistent. So we have variability with respect to our conventions, our norms, our regulations. We have a lot of data that need to be managed and yet we don't have any consistent structures or guidelines for how that data should be managed. It's also really important to recognize that we can't promise anonymity any longer. It's too easy to re-identify people based on just a few bits of data. Now I've heard people say so often that, oh, it's just my steps. I don't really care if somebody knows how many steps I've taken. But if that data from steps that Fitbit has is then merged with Google, which it now can be because Google has acquired Fitbit, you can tell, and this is some work that was done from Evidation Health, that out of 100,000 people with six steps, you can figure out who that person is. And then consent is something that I think is a cornerstone of so much of what we do and how can it be truly informed if our patients or participants are not technology literate, are not data literate. And this is really important because most people don't understand how these technologies work, where their data go, who can access it. And as we mentioned, just norms for privacy are changing, but they may not be changing with the person's preferences reflected. And we've done a lot of research on looking at how IRBs are thinking about digital health studies, what they identify as risky, what they think about with respect to bystander rights, things that they're missing that we think are particularly important to capture. We've looked at participants to see what their experience has been using these wearable sensors, what their concerns have been. We have looked at different cultures. We've done studies with Latino, refugee, Native Hawaiian, Pacific Islander to find out what kind of issues or concerns they have with respect to digital technologies, specifically those that pick up health behaviors. And then we've looked at participant terms and conditions to figure out whether or not they are written in any close way to something that would be accessible to an individual. We were interested in apps that were targeting adolescents and to see whether or not any of these privacy policies were even thinking about targeting a reading level that was maybe around the sixth to eighth grade. We've looked at the NIH reporter database to see what kinds of institutes are funding this research, how that's changing over time. And it is, and it's growing rapidly. So this is not something that's going away. If anything, it's really picking up. And so part of what we're doing is we're wanting to make our research results easily accessible to people. So we've been publishing all of our papers and webinars and other educational content through our eScholarship site. And we do webinars that are all available on the Recode Health website. One thing that we've really spent quite a bit of time on that I'm going to spend the next 15 minutes on is this digital decision support framework and checklist. This is something that we started to develop because we really wanted researchers, clinicians, app developers, and consumers to be able to know how to think through what they needed to think about so that they could make informed decisions about the use of a digital tool, whether it's a wearable or a mobile app. And the human-centered design process is something that I have incorporated into pretty much all of the research that we do, where what we want to do is think about what is the problem? How do we start thinking about what we want to develop? And throughout the process, make sure that we're engaging the audience that we're building something for. How do we develop it? Then test it with them. Then do a beta release. Then get feedback. And we just continue to do this iterative cycle of building what we're hoping to develop as a tool. And so we did a process like that when we started to develop what we have now called the digital health checklist. And we used as a starting point the APA checklist that Dr. Torres developed. You're probably familiar with this. And as John and I were looking at this pyramid, one of the things that I was thinking about is could this be useful for a researcher? Is it really something that has the ability to be generalizable to different groups? And so we had a focus group, and this was in 2017, that involved regulatory compliance experts, legal scholars, clinicians. It was healthcare workers, researchers. We had a nurse. We had an anthropologist involved. And we kind of broke it down, and our outcome was these are the things that we really think are important. We don't think it's quite the shape of this pyramid. We think they're equally important. They may be equally, some may be more important for some people than others. It might depend on the device and how it's going to be used. And so we took their feedback and then created a survey that we deployed with a group of behavioral scientists. And we asked them to reflect on the last study that they had done and to go through our checklist and to identify whether or not it was either confirming that they had thought about all these things or revealing that they needed to think about these things and maybe hadn't. And so through that process, we created this framework, which is grounded in ethical principles. And so respect for persons, beneficence, and justice that we spoke about earlier. And then we added respect for law and public interest, which was a fourth principle that came about as a result of the MENLA report, which was created by cybersecurity experts who were affiliated with the Department of Homeland Security in 2012, recognizing that information and communication technology research was going to rapidly increase. And they wanted to make sure that they had guiding principles. And this involves a lot of work with artificial intelligence. And so knowing that artificial intelligence machine learning is a big component of digital health, we wanted to make sure we were thinking about how respect for law and public interest was critical to this kind of decision making. And so the focus groups and the surveys with the behavioral scientists led us to this framework, which, again, grounded in ethical principles. But what they were telling us is that access and usability, privacy, data management, and risks and benefits were the key domains. And from this, we started developing the checklist questions. And this is in no way comprehensive in that it is going to be constantly evolving. And so we have this up on our website in a beta test form that I'll share with you all in case you ever want to use this. It's open to the public. We have space under each one of these domains for people to add or ask questions or add items to the checklist that might be useful for others in the future. So with this domain, we're talking about whether or not the product design, whether it's been really informed by the end users. Can they use the tool? We want to know how it works, how information is communicated to the user, whether or not it's been used with the target population, whether accessory tools are needed like internet access or a smartphone, and can it be used both short and long term. And so on the right over here, you can see that we have all the ethical principles labeled. And then under each one of these ethical principles, we've identified checklist items that we think are aligned with those principles. So privacy is about the personal information collected and expectations of the patient or participant to keep information secured. And if shared, what is collected, what is shared, why is it shared, and what control does that end user have? And again, similar to usability that we just showed you, again, the checklist is organized based on the ethical principles. Risks and benefits. This is the domain that we use to evaluate the types of possible risks and the extent of possible harm. Once we can identify what those risks are and how they can be mitigated, then we can assess whether or not the benefit or potential benefit is reasonable in relation to potential risks. So we're thinking about the type of harm, whether that's psychological, economic, reputational, physical, how severe is that harm? How long is that harm going to last and how intense is it? So these are really important steps to think about when we're thinking about how do we assess it. But then also, I'm a big advocate for making sure that the person at the end is able to make a decision about how this works for them and really respect autonomy, give people the information they need so that they can then make that decision. So what we're coaching people to do is think about under beneficence, you'll see here on the right, we want to know whether or not there's any evidence to support that this technology is reliable and valid. Was that evidence peer-reviewed? Are there risks that are unknown? And how do we convey information about that to a person that may be adopting this? So all of these things really need to be thought through before making a recommendation if you're a clinician, before choosing this tool as a component of a research study, or even as a consumer, what do you need to think about before downloading that app? Data management is the fourth domain, and this is really about how data are collected, stored, shared, and whether and to what degree they interact with other systems, so the interoperability. We want to know what is collected, what is shared, why is it shared, and again, what control does the end user have? Can they opt out? Can they take their data with them? All of these things are really important and are not consistent because we don't have standards and norms in place yet to really direct how this is all happening. So the path forward is to move purposely and fix things, which I've adopted from Omidyar, a blog that they wrote back in 2019, instead of move fast and break things, which is not something that's going to help us moving forward. And so with our Recode Health Center, which you see San Diego is supporting at this point, but we're moving into external funding to keep us moving along, is that we're shaping responsible and ethical practices in the digital health sector. We do research, we provide consultation, we have education, and I mentioned that we have this digital health checklist, which is part of our tools that we make available, but we also have created the Building Research Integrity and Capacity Educational Modules, which is really for community health workers, people that are instrumental in helping with sharing information with communities. We really want to help make sure that they have the capacity to do their jobs well, and so we've invested in creating that education for people, community members, and then the core platform is something that we started creating back in 2015 as a learning ethics system for the digital health research community. And it's a platform, we have a forum, we have a resource library where people will share their IRB protocols, consent language, we have a forum where people can just ask and answer questions, and we have a network now that is close to 900 people globally that includes nurses, psychiatrists, legal scholars, ethicists, regulators, behavioral scientists, computer scientists. It's just a really rich community that we've been able to create, and with that, I think what I'll do is stop here, and this is information if you'd like to contact me, and I'll put it over here to turn it back over to you, Amy.
Video Summary
The video features Dr. Camille Nebeker discussing ethical considerations in digital mental health. Dr. Nebeker is a research ethicist and the Director of Recode Health at the University of California, San Diego. The video is part of the SMI Advisor webinar series, which aims to help clinicians implement evidence-based care for individuals with serious mental illness.<br /><br />Dr. Nebeker begins by explaining the purpose of the SMI Advisor initiative and introduces the faculty for the webinar. She then delves into the use of digital technologies in health research, particularly in the domain of mental health. She highlights the ethical challenges associated with digital health and introduces a digital health decision support tool and framework developed by Recode Health.<br /><br />The framework includes four domains: usability, privacy, risks and benefits, and data management. The checklist within each domain encompasses specific questions related to ethical principles such as respect for persons, beneficence, and justice. Dr. Nebeker emphasizes the importance of human-centered design and the need to consider the end users' perspectives when developing digital health technologies.<br /><br />She concludes by discussing the need for responsible and ethical practices in the digital health sector. Recode Health offers research, consultation, and education, including the digital health checklist and the Building Research Integrity and Capacity Educational Modules. Dr. Nebeker also mentions the Core Platform, a community and resource hub for digital health researchers.<br /><br />Overall, the video provides an overview of ethical considerations in digital mental health and introduces a decision support framework to guide researchers, clinicians, and consumers in making informed choices.
Keywords
Dr. Camille Nebeker
ethical considerations
digital mental health
SMI Advisor webinar series
evidence-based care
digital technologies
health research
ethical challenges
Funding for SMI Adviser was made possible by Grant No. SM080818 from SAMHSA of the U.S. Department of Health and Human Services (HHS). The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by, SAMHSA/HHS or the U.S. Government.
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