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Wednesday, January 8th, 2020

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    1:00a
    “She” goes missing from presidential language

    Throughout most of 2016, a significant percentage of the American public believed that the winner of the November 2016 presidential election would be a woman — Hillary Clinton.

    Strikingly, a new study from cognitive scientists and linguists at MIT, the University of Potsdam, and the University of California at San Diego shows that despite those beliefs, people rarely used the pronoun “she” when referring to the next U.S. president before the election. Furthermore, when reading about the future president, encountering the pronoun “she” caused a significant stumble in their reading.

    “There seemed to be a real bias against referring to the next president as ‘she.’ This was true even for people who most strongly expected and probably wanted the next president to be a female,” says Roger Levy, an MIT professor of brain and cognitive sciences and the senior author of the new study. “There’s a systematic underuse of ‘she’ pronouns for these kinds of contexts. It was quite eye-opening.”

    As part of their study, Levy and his colleagues also conducted similar experiments in the lead-up to the 2017 general election in the United Kingdom, which determined the next prime minister. In that case, people were more likely to use the pronoun “she” than “he” when referring to the next prime minister.

    Levy suggests that sociopolitical context may account for at least some of the differences seen between the U.S. and the U.K.: At the time, Theresa May was prime minister and very strongly expected to win, plus many Britons likely remember the long tenure of former Prime Minister Margaret Thatcher.

    “The situation was very different there because there was an incumbent who was a woman, and there is a history of referring to the prime minister as ‘she’ and thinking about the prime minster as potentially a woman,” he says.

    The lead author of the study is Titus von der Malsburg, a research affiliate at MIT and a researcher in the Department of Linguistics at the University of Potsdam, Germany. Till Poppels, a graduate student at the University of California at San Diego, is also an author of the paper, which appears in the journal Psychological Science.

    Implicit linguistic biases

    Levy and his colleagues began their study in early 2016, planning to investigate how people’s expectations about world events, specifically, the prospect of a woman being elected president, would influence their use of language. They hypothesized that the strong possibility of a female president might override the implicit bias people have toward referring to the president as “he.”

    “We wanted to use the 2016 electoral campaign as a natural experiment, to look at what kind of language people would produce or expect to hear as their expectations about who was likely to win the race changed,” Levy says.

    Before beginning the study, he expected that people’s use of the pronoun “she” would go up or down based on their beliefs about who would win the election. He planned to explore how long would it take for changes in pronoun use to appear, and how much of a boost “she” usage would experience if a majority of people expected the next president to be a woman.

    However, such a boost never materialized, even though Clinton was expected to win the election.

    The researchers performed their experiment 12 times between June 2016 and January 2017, with a total of nearly 25,000 participants from the Amazon Mechanical Turk platform. The study included three tasks, and each participant was asked to perform one of them. The first task was to predict the likelihood of three candidates winning the election — Clinton, Donald Trump, or Bernie Sanders. From those numbers, the researchers could estimate the percentage of people who believed the next president would be a woman. This number was higher than 50 percent during most of the period leading up to the election, and reached just over 60 percent right before the election.

    The next two tasks were based on common linguistics research methods — one to test people’s patterns of language production, and the other to test how the words they encounter affect their reading comprehension.

    To test language production, the researchers asked participants to complete a paragraph such as “The next U.S. president will be sworn into office in January 2017. After moving into the Oval Office, one of the first things that ….”

    In this task, about 40 percent of the participants ended up using a pronoun in their text. Early in the study period, more than 25 percent of those participants used “he,” fewer than 10 percent used “she,” and around 50 percent used “they.” As the election got closer, and Clinton’s victory seemed more likely, the percentage of “she” usage never went up, but usage of “they” climbed to about 60 percent. While these results indicate that the singular “they” has reached widespread acceptance as a de facto standard in contemporary English, they also suggest a strong persistent bias against using “she” in a context where the gender of the individual referred to is not yet known.

    “After Clinton won the primary, by late summer, most people thought that she would win. Certainly Democrats, and especially female Democrats, thought that Clinton would win. But even in these groups, people were very reluctant to use ‘she’ to refer to the next president. It was never the case that ‘she’ was preferred over ‘he,’” Levy says.

    For the third task, participants were asked to read a short passage about the next president. As the participants read the text on a screen, they had to press a button to reveal each word of the sentence. This setup allows the researchers to measure how quickly participants are reading. Surprise or difficulty in comprehension leads to longer reading times.

    In this case, the researchers found that when participants encountered the pronoun “she” in a sentence referring to the next president, it cost them about a third of a second in reading time — a seemingly short amount of time that is nevertheless known from sentence processing research to indicate a substantial disruption relative to ordinary reading — compared to sentences that used “he.” This did not change over the course of the study.

    “For months, we were in a situation where large segments of the population strongly expected that a woman would win, yet those segments of the population actually didn’t use the word ‘she’ to refer to the next president, and were surprised to encounter ‘she’ references to the next president,” Levy says.

    Strong stereotypes

    The findings suggest that gender biases regarding the presidency are so deeply ingrained that they are extremely difficult to overcome even when people strongly believe that the next president will be a woman, Levy says.

    “It was surprising that the stereotype that the U.S. president is always a man would so strongly influence language, even in this case, which offered the best possible circumstances for particularized knowledge about an upcoming event to override the stereotypes,” he says. “Perhaps it’s an association of different pronouns with positions of prestige and power, or it’s simply an overall reluctance to refer to people in a way that indicates they’re female if you’re not sure.”

    The U.K. component of the study was conducted in June 2017 (before the election) and July 2017 (after the election but before Theresa May had successfully formed a government). Before the election, the researchers found that “she” was used about 25 percent of the time, while “he” was used less than 5 percent of the time. However, reading times for sentences referring to the prime minister as “she” were no faster than than those for “he,” suggesting that there was still some bias against “she” in comprehension relative to usage preferences, even in a country that already has a woman prime minister.

    The type of gender bias seen in this study appears to extend beyond previously seen stereotypes that are based on demographic patterns, Levy says. For example, people usually refer to nurses as “she,” even if they don’t know the nurse’s gender, and more than 80 percent of nurses in the U.S. are female. In an ongoing study, von der Malsburg, Poppels, Levy, and recent MIT graduate Veronica Boyce have found that even for professions that have fairly equal representation of men and women, such as baker, “she” pronouns are underused.

    “If you ask people how likely a baker is to be male or female, it’s about 50/50. But if you ask people to complete text passages that are about bakers, people are twice as likely to use he as she,” Levy says. “Embedded within the way that we use pronouns to talk about individuals whose identities we don’t know yet, or whose identities may not be definitive, there seems to be this systematic underconveyance of expectations for female gender.”

    The research was funded by the National Institutes of Health, a Feodor Lynen Research Fellowship from the Alexander von Humboldt Foundation, and an Alfred P. Sloan Fellowship.

    4:59p
    In health care, does “hotspotting” make patients better?

    The new health care practice of “hotspotting” — in which providers identify very high-cost patients and attempt to reduce their medical spending while improving care — has virtually no impact on patient outcomes, according to a new study led by MIT economists. 

    The finding underscores the challenge of reducing spending on “superutilizers” of health care, the roughly 5 percent of patients in the U.S. who account for half the nation’s health care costs. The concept of hotspotting, a little more than a decade old, consists of programs that give at-risk patients sustained contact with doctors, other caregivers, and social service providers, in an attempt to prevent rehospitalizations and other intensive, expensive forms of care. 

    The MIT study was developed in cooperation with the Camden Coalition of Healthcare Providers, which runs one of the nation’s best-known hotspotting programs. The researchers conducted a four-year analysis of the program and found that being enrolled in it makes no significant difference to patients’ health care use.  

    “This intervention had no impact in reducing hospital readmissions,” says Amy Finkelstein, an MIT health care economist who led the study.

    Significantly, the new study was a randomized, controlled trial, in which two otherwise similar groups of patients in Camden were separated by one large factor: Some were randomly selected to be part of the hotspotting program, and an equal number of randomly selected patients were not. The two groups generated virtually the same results over time.

    “The reason it was so important we did a randomized, controlled trial,” Finkelstein says, “is that if you just look at the individuals in the intervention group, it would look like the program caused a huge reduction in readmissions. But when you look at the individuals in the control group — who were eligible for the program but were not randomly selected to get it — you see the exact same pattern.”

    The paper, “Health Care Hotspotting — A Randomized, Controlled Trial” is being published today in the New England Journal of Medicine. The co-authors are Finkelstein, the John and Jennie S. MacDonald Professor Economics at MIT, who is the paper’s corresponding author; Joseph Doyle, an economist who is the Erwin H. Schell Professor of Management at the MIT Sloan School of Management; Sarah Taubman, a research scientist at J-PAL North America, part of MIT’s Abdul Latif Jameel Poverty Action Lab; and Annetta Zhou, a postdoc at the National Bureau of Economic Research.

    Camden Coalition “fabulous partners” in seeking answers

    To conduct the study, the MIT-led research team evaluated 800 patients enrolled in the Camden Coalition of Healthcare Providers program from 2014 to 2017. The patients in the study had been hospitalized at least once in the six months prior to admission and had at least two chronic medical conditions, among other health care issues. The study was constructed after extensive consultation with the coalition.

    “They were fabulous partners,” Finkelstein says about the coalition. “Because they’re so data-driven, they had the data infrastructure in place, which made this possible.”

    Finkelstein particularly cites the founder of the Camden Coalition of Healthcare Providers, Jeffrey Brenner, who served as executive director of the organization from 2006 through 2017, and whose development of “hotspotting” concepts has received substantial public attention. In Camden, where 2 percent of patients represent 33 percent of medical expenses, preventing the need for acute care is a pressing concern. 

    “Dr. Brenner is a really extraordinary person, and he’s trying to solve a very hard problem,” Finkelstein says, crediting Brenner for actively seeking data about his organization’s results without knowing what those outcome would be.

    Half of the study’s 800 patients were placed in a group that used the program’s services, and half were in a control group that did not take part in the program. The Camden hotspotting program includes extensive home care visits, coordinated follow-up care, and medical monitoring — all designed to help stabilize the health of patients after hospitalization. It also helps patients apply for social services and behavioral health programs.

    Overall, the study found that the 180-day hospital readmission rate was 62.3 percent for people in the program and 61.7 percent for people not in the program. 

    Additional measurements in the study — such as the number of hospital readmissions for patients, aggregate number of days spent in the hospital, and multiple financial statistics — also showed very similar outcomes between the two groups.

    The study shows that while the overall number of people in hotspotting programs who need rehospitalization declines over the course of the program, it does not decline by a larger amount than it would if those people were outside the program’s reach.

    In short, people in hotspotting programs require fewer rehospitalizations because any group of patients currently using a lot of health care resources will tend to have lower health care use in the future. Previous reports about hotspotting programs had focused on the roughly 40 percent decline in six-month hospital readmissions — while not comparing that to the rate for comparable patient groups outside such programs.

    “If you think about health care interventions, almost by definition they’re occurring at a time of unusually poor health or unusually high cost,” Finkelstein says. “That’s why you’re intervening. So they’re almost by construction going to be plagued by the issue of regression to[ward] the mean. I think that’s a really important lesson as we continue to try to figure out how to improve health care delivery, especially as so much of the work focuses on these high-cost patients.”

    “We’re not going to give up”

    To be sure, as Finkelstein notes, the new study is a local one, and hotspotting programs exist in many locations. It also examines the four-year results of the program, which underwent some evolution during the study period; if the program had made a breakthrough change in, say, 2016, that would only partially be reflected in the four-year data. As it happens, however, the study found no such large changes over time. 

    Brenner’s perspective about studying the effectiveness of his own initiative, Finkelstein says, was that, by analogy, “if you have a new medication to try to cure cancer, and you run a clinical trial on it and it doesn’t work, you don’t just say, ‘I guess that’s it, we’re stuck with cancer.’ You keep trying other things. … We’re not going to give up on improving the efficiency of health care delivery and the well-being of this incredibly under-served population. We need to continue to develop potential solutions and rigorously evaluate them.”

    Finkelstein also notes that the current study is just one piece of research in the complicated area of improving health care and reducing costs for people in need of extensive treatment, and says she welcomes additional research in this area.

    “I hope it inspires more research and that more organizations will partner with us to study [these issues],” Finkelstein says.

    Finkelstein also serves as the scientific director of J-PAL North America at MIT, which backs randomized controlled trials on a variety of social issues.

    The data for the study came from the Camden Coalition of Healthcare Providers; Camden’s four hospitals; and the state of New Jersey. 

    The research was supported by the National Institute on Aging of the National Institutes of Health; the Health Care Delivery Initiative of J-PAL North America; and the MIT Sloan School of Management.

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