Tag: dark

  • The Dark Legacy of Elite University Medical Centers

    The Dark Legacy of Elite University Medical Centers

     

    (Image: Mass General is Harvard University Medical School’s teaching hospital.)  

     

    For decades, America’s elite university medical centers have been the epitome of healthcare research and innovation, providing world-class treatment, education, and cutting-edge medical advancements. Yet, beneath this polished surface lies a troubling legacy of medical exploitation, systemic inequality, and profound injustice—one that disproportionately impacts marginalized communities. While the focus has often been on racial disparities, this issue is not solely about race; it is also deeply entangled with class. In recent years, books like Medical Apartheid by Harriet Washington have illuminated the history of medical abuse, but they also serve as a reminder that inequality in healthcare goes far beyond race and touches upon the economic and social circumstances of individuals.

    The term Medical Apartheid, as coined by Harriet Washington, refers to the systemic and institutionalized exploitation of Black Americans in medical research and healthcare. Washington’s work examines the history of Black Americans as both victims of medical experimentation and subjects of discriminatory practices that have left deep scars within the healthcare system. Yet, the complex interplay between race and class means that many poor or economically disadvantaged individuals, regardless of race, have also faced neglect and exploitation within these prestigious medical institutions. The legacy of inequality within elite university medical centers, therefore, is not limited to race but is also an issue of class disparity, where wealthier individuals are more likely to receive proper care and access to cutting-edge treatments while the poor are relegated to substandard care.

    Historical examples of exploitation and abuse in medical centers are well-documented in Washington’s work, and contemporary lawsuits and investigations reveal that these systemic problems still persist. Poor patients, especially those from marginalized racial backgrounds, are often viewed as expendable research subjects. The lawsuit underscores the intersectionality of race and class, arguing that these patients’ socio-economic status exacerbates their vulnerability to medical exploitation, making it easier for institutions to treat them as less than human, especially when they lack the resources or power to contest medical practices.

    One of the most critical components of this issue is the stark contrast in healthcare access between the wealthy and the poor. While elite university medical centers boast state-of-the-art facilities, cutting-edge treatments, and renowned researchers, these resources are often not equally accessible to all. Wealthier patients are more likely to have the financial means to receive the best care, not just because of their ability to pay but because they are more likely to be referred to these prestigious centers. Conversely, low-income patients, especially those without insurance or with inadequate insurance, are often forced into overcrowded public hospitals or community clinics that are underfunded, understaffed, and unable to provide the level of care available at elite institutions.

    The issue of class inequality within medical care is evident in several key areas. For instance, studies have shown that low-income patients, regardless of race, are less likely to receive timely and appropriate medical care. A 2019 report from the National Academy of Medicine found that low-income patients are often dismissed by healthcare professionals who underestimate the severity of their symptoms or assume they are less knowledgeable about their own health. In addition, patients from lower socio-economic backgrounds are more likely to experience medical debt, which can lead to long-term financial struggles and prevent them from seeking care in the future.

    Moreover, class plays a significant role in the underrepresentation of poor individuals in medical research, which is often conducted at elite university medical centers. Historically, clinical trials have excluded low-income participants, leaving them without access to potentially life-saving treatments or advancements. Wealthier individuals, on the other hand, are more likely to be invited to participate in research studies, ensuring they benefit from the very innovations and breakthroughs that these institutions claim to provide.

    Class-based disparities are also reflected in the inequities in medical professions. The road to becoming a physician or researcher in these elite institutions is often paved with significant economic barriers. Medical students from low-income backgrounds face steep financial challenges, which can hinder their ability to gain acceptance into prestigious medical schools or pursue advanced research opportunities. Even when low-income students do manage to enter these programs, they often face biases and discrimination in clinical settings, where their abilities are unfairly questioned, and their economic status may prevent them from fully participating in research or other educational opportunities.

    Yet, the inequities within these institutions don’t stop at the patients. Behind the scenes, workers at elite university medical centers, particularly those from working-class and marginalized backgrounds, face their own form of exploitation. These medical centers are not only spaces of high medical achievement but also sites of labor stratification, where workers in lower-paying roles are largely people of color and often immigrants. Support staff—such as janitors, food service workers, custodians, and administrative assistants—are often invisible but essential to the functioning of these hospitals and research institutions. These workers face long hours, poor working conditions, and low wages, all while contributing to the daily operations of elite medical centers. Many of these workers, employed through third-party contractors, lack benefits, job security, or protections, leaving them vulnerable to exploitation.

    Custodial workers, who are often exposed to hazardous chemicals and physically demanding work, may struggle to make ends meet, despite playing a crucial role in maintaining the hospital environment. Similarly, food service workers—many of whom are Black, Latinx, or immigrant—also work in demanding conditions for low wages. These workers frequently face job insecurity and are not given the same recognition or compensation as the high-ranking physicians, researchers, or administrators in these centers.

    At the same time, the stratification in these institutions extends beyond support staff. Medical researchers, residents, and postdoctoral fellows—often young, early-career individuals, many from working-class backgrounds or communities of color—are similarly subjected to precarious working conditions. These individuals perform much of the vital research that drives innovation at these centers, yet they often face exploitative working hours, low pay, and job insecurity. They are the backbone of the institution’s research output but frequently face barriers to advancement and recognition.

    The higher ranks of these institutions—senior doctors, professors, and researchers—enjoy financial rewards, job security, and prestige, while those at the lower rungs continue to experience instability and exploitation. This division, which mirrors the economic and racial hierarchies of broader society, reinforces the very class-based inequalities these medical centers are meant to address.

    In recent years, some progress has been made in addressing these inequalities. Many elite universities have implemented diversity and inclusion programs aimed at increasing access for underrepresented minority and low-income students in medical schools. Some institutions have also begun to emphasize the importance of cultural competence in training medical professionals, acknowledging the need to recognize and understand both racial and economic disparities in healthcare.

    However, critics argue that these efforts, while important, are often superficial and fail to address the root causes of inequality. The institutional focus on “diversity” and “inclusion” often overlooks the more significant structural issues, such as the affordability of education, the class-based access to healthcare, and the economic barriers that continue to undermine the ability of disadvantaged individuals to receive quality care.

    In addition to acknowledging racial inequality, it is crucial to tackle the broader issue of class within the healthcare system. The disproportionate number of Black and low-income individuals suffering from poor healthcare outcomes is a direct result of a system that privileges wealth and status over human dignity. To begin addressing these issues, we need to move beyond token diversity initiatives and work toward policy reforms that focus on economic access, insurance coverage, and the equitable distribution of medical resources.

    Scholars like Harriet Washington, whose work documents the intersection of race, class, and healthcare inequality, continue to play a pivotal role in bringing attention to these systemic injustices. Washington’s book Medical Apartheid serves as a historical record but also as a call to action for creating a healthcare system that genuinely serves all people, regardless of race or socio-economic status. The fight for healthcare equity must, therefore, be a dual one—against both racial and class-based disparities that have long plagued our medical institutions.

    The story of Henrietta Lacks, as told in The Immortal Life of Henrietta Lacks by Rebecca Skloot, exemplifies the longstanding exploitation of marginalized individuals in elite university medical centers. The case of Lacks, whose cells were taken without consent by researchers at Johns Hopkins University, brings to light both the historical abuse of Black bodies and the profit-driven nature of academic medical research. Johns Hopkins, one of the most prestigious medical centers in the world, has been complicit in the kind of exploitation and neglect that these institutions are often criticized for—issues that disproportionately affect not only Black Americans but also economically disadvantaged individuals.

    The Black Panther Party’s healthcare activism, as chronicled by Alondra Nelson in Body and Soul, also directly challenges elite medical institutions’ failure to provide adequate care for Black and low-income communities. Nelson’s work reflects how, even today, these institutions are often slow to address the systemic issues of health disparities that activists like the Panthers fought against.

    Recent lawsuits against elite medical centers further underscore the importance of holding these institutions accountable for their role in perpetuating medical exploitation and inequality. In An American Sickness by Elisabeth Rosenthal, the commercialization of healthcare is explored, highlighting how university hospitals and medical centers often prioritize profits over patient care, leaving low-income and marginalized groups with limited access to treatment. Rosenthal’s work highlights the role these institutions play in a larger system that disproportionately benefits wealthier patients while neglecting the most vulnerable.

    A Global Comparison: Countries with Better Health Outcomes

    While the United States struggles with systemic healthcare disparities, other nations have shown that equitable healthcare outcomes are possible when class and race are not barriers to care. Nations with universal healthcare systems, such as those in Canada, the United Kingdom, and many Scandinavian countries, consistently rank higher in overall health outcomes compared to the U.S.

    For instance, Canada’s single-payer system ensures that all citizens have access to healthcare, regardless of their income. This system reduces the financial burdens that often lead to delays in care or avoidance of treatment due to costs. According to the World Health Organization, Canada has better health outcomes on a variety of metrics, including life expectancy and infant mortality, compared to the U.S., where medical costs often lead to unequal access to care.

    Similarly, the United Kingdom’s National Health Service (NHS) provides healthcare free at the point of use for all citizens. Despite challenges such as funding constraints and wait times, the NHS has been successful in ensuring that healthcare is a right, not a privilege. The U.K. consistently ranks higher than the U.S. in terms of access to care, health outcomes, and overall public health.

    Nordic countries, such as Norway and Sweden, also exemplify how universal healthcare can lead to better outcomes. These countries invest heavily in public health and preventative care, ensuring that even their most marginalized citizens receive the necessary medical services. The result is a population with some of the highest life expectancies and lowest rates of chronic diseases in the world.

    These nations show that, while access to healthcare is a critical issue in the U.S., the challenge is not a lack of innovation or capability. Instead, it is the systemic barriers—both racial and economic—that persist in elite medical centers, undermining the potential for universal health equity. The U.S. could learn from these nations by adopting policies that reduce economic inequality in healthcare access and focusing on preventative care and public health strategies that serve all people equally.

    Ultimately, the dark legacy of elite university medical centers is not something that can be erased, but it is something that must be acknowledged. Only by confronting this painful history, alongside addressing class-based disparities, can we begin to build a more just and equitable healthcare system—one that serves everyone, regardless of race, background, or socio-economic status. Until this happens, the distrust and skepticism that many marginalized communities feel toward these institutions will continue to shape the landscape of American healthcare. The path forward requires a concerted effort to address both racial and class-based inequities that have defined these institutions for far too long. The U.S. can, and must, strive for healthcare outcomes akin to those seen in nations that have built systems prioritizing equity and fairness—systems that put human dignity over profit.

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  • Ed data goes dark: Why it matters (opinion)

    Ed data goes dark: Why it matters (opinion)

    When President Donald Trump and Elon Musk’s Department of Government Efficiency set out to slash billions from the federal budget, it puzzled me as to why one of their first targets was an obscure data collection and research agency, the Institute of Education Sciences, a relatively modest operation buried deeply in the corridors of the Department of Education, and indeed one few had ever heard of. Since then, the newly installed secretary of education has ordered a review of all the department’s functions as part of what she ominously called the department’s “momentous final mission.”

    A conversation with a trusted colleague helped me understand the cuts to IES, noting that the action should be seen as part of a new breed of autocrats around the world who seek to control information to hide the impacts of their actions from the public. In contemporary authoritarian governments, control of information—or what has come to be known today as informational autocracy—often substitutes for brute force.

    Similar to how the Trump administration is seizing control of the White House press pool, canceling contracts for independent, high-quality education research is another way of controlling information. As Democratic lawmakers wrote in a Feb. 21 letter decrying the cuts, “The consequences of these actions will prevent the public from accessing accurate information about student demographics and academic achievement, abruptly end evaluations of federal programs that ensure taxpayer funds are spent wisely, and set back efforts to implement evidence-based reforms to improve student outcomes.”

    IES houses a vast warehouse of the nation’s education statistics. Data collected by the agency is used by policymakers, researchers, teachers and colleges to understand student achievement, enrollment and much more about the state of American education. With IES being among the largest funders of education research, cutting it limits public access to what’s happening in the nation’s schools and colleges.

    Claiming to eliminate waste and corruption, Musk’s first round of cuts involved canceling what DOGE initially said were nearly $900 million in IES contracts (though, as subsequent reporting has since revealed, DOGE’s math doesn’t add up and the canceled contracts seem to amount to much less). A second round purportedly sliced another $350 million in contracts and grants. It’s unclear how much more is destined to be chopped, since these may only be the first in a series of cuts designed to completely dismantle the Education Department. Though a department spokesperson initially said that the cuts would not affect the National Assessment of Educational Progress, a standardized test known as the nation’s report card, and the College Scorecard, which allows citizens to search for and compare information about colleges, we’ve since seen the cancellation of a national NAEP test for 17-year-olds.

    In the Obama years, public data helped reveal bad actors among for-profit colleges, which were receiving millions in federal aid while delivering inferior education to poor and working-class students who yearned for college degrees. Since so few actually completed, what many got instead was crushing college debt. Luckily, good data helped drive nearly half of all for-profit programs to shut down. Publicly disseminated data exposes where things go wrong. But you can’t track down con men without evidence.

    Ideally, in a well-functioning democracy, with a richly informed public, data helps us reach informed decisions, leading to greater accountability and enabling us to hold officials responsible for their actions. With access to reliable information about what’s happening behind closed doors, data helps us understand what may be going on, even to protest actions we may oppose.

    Lately, however, things aren’t looking good. Since Trump and his top officials have slashed race-conscious programs and moved to prohibit funding for certain areas of research, higher ed leadership has remained mostly silent, with only a handful of college presidents protesting. Most have shrunk into the wings, cowed by Trump’s power to defund institutions. It already has the eerie feeling of watching your step.

    Shutting down potentially revealing data collection is perhaps the least worrisome page in an autocrat’s playbook. As Trump continues to follow the authoritarian path set by leaders in Hungary, Turkey and elsewhere, we should expect other, more damaging and more frightening higher ed moves that have been imposed by other autocrats—selecting college presidents, controlling faculty hiring and advancement, punishing academic dissent, imposing travel restrictions.

    Just a few months ago, there was comfort in knowing everything was there—data on enrollments, graduation rates, participation rates of women and other groups. All very neatly organized and accessible whenever you wanted. Even though some found IES technology old and clunky, it felt like higher ed was running according to a reliable scheme, that you could go online and open data files as in a railroad timetable. Without it, there might be a train wreck ahead and you wouldn’t know it until it was too late. Now these luxurious numbers may soon be lost, with decades of America’s academic history pitched into digital darkness.

    It’s frightening to realize that we’ll no longer be operating on solid intelligence. That we’ll no longer have guideposts, supported by racks of sensibly collected numbers to tell us if we’re on the right path or if we’re far afield. Trump’s wrecking ball has smashed our confidence, a confidence built on years of reliable data. We’ll soon be in the dark.

    Robert Ubell is vice dean emeritus of online learning at New York University’s Tandon School of Engineering and senior editor of CHLOE 9, the ninth national survey of higher ed chief online learning officers. A collection of his essays on virtual education, Staying Online: How to Navigate Digital Higher Education, was published by Routledge.

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  • The data dark ages | Wonkhe

    The data dark ages | Wonkhe

    Is there something going wrong with large surveys?

    We asked a bunch of people but they didn’t answer. That’s been the story of the Labour Force Survey (LFS) and the Annual Population Survey (APS) – two venerable fixtures in the Office for National Statistics (ONS) arsenal of data collections.

    Both have just lost their accreditation as official statistics. A statement from the Office for Statistical Regulation highlights just how much of the data we use to understand the world around us is at risk as a result: statistics about employment are affected by the LFS concerns, whereas APS covers everything from regional labour markets, to household income, to basic stuff about the population of the UK by nationality. These are huge, fundamental, sources of information on the way people work and live.

    The LFS response rate has historically been around 50 per cent, but it had fallen to 40 per cent by 2020 and is now below 20 per cent. The APS is an additional sample using the LFS approach – current advice suggests that response rates have deteriorated to the extent that it is no longer safe to use APS data at local authority level (the resolution it was designed to be used at).

    What’s going on?

    With so much of our understanding of social policy issues coming through survey data, problems like these feel almost existential in scope. Online survey tools have made it easier to design and conduct surveys – and often design in the kind of good survey development practices that used to be the domain of specialists. Theoretically, it should be easier to run good quality surveys than ever before – certainly we see more of them (we even run them ourselves).

    Is it simply a matter of survey fatigue? Or are people less likely to (less willing to?) give information to researchers for reasons of trust?

    In our world of higher education, we have recently seen the Graduate Outcomes response rate drop below 50 per cent for the first time, casting doubt as to its suitability as a regulatory measure. The survey still has accredited official statistics status, and there has been important work done on understanding the impact of non-response bias – but it is a concerning trend. The national student survey (NSS) is an outlier here – it has a 72 per cent response rate last time round (so you can be fairly confident in validity right down to course level), but it does enjoy an unusually good level of survey population awareness even despite the removal of a requirement for providers to promote the survey to students. And of course, many of the more egregious issues with HESA Student have been founded on student characteristics – the kind of thing gathered during enrollment or entry surveys.

    A survey of the literature

    There is a literature on survey response rates in published research. A meta-analysis by Wu et al (Computers in Human Behavior, 2022) found that, at this point, the average online survey result was 44.1 per cent – finding benefits for using (as NSS does) a clearly defined and refined population, pre-contacting participants, and using reminders. A smaller study by Diaker et al (Journal of Survey Statistics and Methodology, 2020) found that, in general, online surveys yield lower response rates (on average, 12 percentage point lower) than other approaches.

    Interestingly, Holton et al (Human Relations, 2022) show an increase in response rates over time in a sample of 1014 journals, and do not find a statistically significant difference linked to survey modes.

    ONS itself works with the ESRC-funded Survey Futures project, which:

    aims to deliver a step change in survey research to ensure that it will remain possible in the UK to carry out high quality social surveys of the kinds required by the public and academic sectors to monitor and understand society, and to provide an evidence base for policy

    It feels like timely stuff. Nine strands of work in the first phase included work on mode effects, and on addressing non-response.

    Fixing surveys

    ONS have been taking steps to repair LFS – implementing some of the recontacting/reminder approaches that have been successfully implemented and documented in the academic literature. There’s a renewed focus on households that include young people, and a return to the larger sample sizes we saw during the pandemic (when the whole survey had to be conducted remotely). Reweighting has led to a bunch of tweaks to the way samples are chosen, and non-responses accounted for.

    Longer term, the Transformed Labour Force Survey (TLFS) is already being trialed, though the initial March 2024 plans for full introduction has been revised to allow for further testing – important given a bias towards older age group responses, and an increased level of partial responses. Yes, there’s a lessons learned review. The old LFS and the new, online first, TLFS will be running together at least until early 2025 – with a knock on impact on APS.

    But it is worth bearing in mind that, even given the changes made to drive up responses, trial TLFS response rates have been hovering around just below 40 per cent. This is a return to 2020 levels, addressing some of the recent damage, but a long way from the historic norm.

    Survey fatigue

    More usually the term “survey fatigue” is used to describe the impact of additional questions on completion rate – respondents tire during long surveys (as Jeong et al observe in the Journal of Development Economics) and deliberately choose not to answer questions to hasten the end of the survey.

    But it is possible to consider the idea of a civilisational survey fatigue. Arguably, large parts of the online economy are propped up on the collection and reuse of personal data, which can then be used to target advertisements and reminders. Increasingly, you now have to pay to opt out of targeted ads on websites – assuming you can view the website at all without paying. After a period of abeyance, concerns around data privacy are beginning to reemerge. Forms of social media that rely on a constant drive to share personal information are unexpectedly beginning to struggle – for younger generations participatory social media is more likely to be a group chat or discord server, while formerly participatory services like YouTube and TikTok have become platforms for media consumption.

    In the world of public opinion research the struggle with response rates has partially been met via a switch from randomised phone or in-person to the use of pre-vetted online panels. This (as with the rise of focus groups) has generated a new cadre of “professional respondents” – with huge implications for the validity of polling even when weighting is applied.

    Governments and industry are moving towards administrative data – the most recognisable example in higher education being the LEO dataset of graduate salaries. But this brings problems in itself – LEO lets us know how much income graduates pay tax on from their main job, but deals poorly with the portfolio careers that are the expectation of many graduates. LEO never cut it as a policymaking tool precisely because of how broadbrush it is.

    In a world where everything is data driven, what happens when the quality of data drops? If we were ever making good, data-driven decisions, a problem with the raw material suggests a problem with the end product. There are methodological and statistical workarounds, but the trend appears to be shifting away from people being happy to give out personal information without compensation. User interaction data – the traces we create as we interact with everything from ecommerce to online learning – are for now unaffected, but are necessarily limited in scope and explanatory value.

    We’ve lived through a generation where data seemed unlimited. What tools do we need to survive a data dark age?

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