Campus briefs


No Record printed or email editions during winter vacation

The University Record will not publish a printed edition Feb. 26, due to the winter semester vacation. Also, no daily Record emails will be sent Feb. 26-March 1. Major items of interest to faculty and staff may be posted to the Record’s website over the break. The printed Record will resume March 4 with weekly publication through May 6. The Record email will resume March 4, with Monday-Friday distribution through May 17. Both versions of the Record will run on a reduced schedule during the late spring and summer.

Nearly 15% of Americans deny climate change is real, AI study finds

Using social media data and artificial intelligence in a comprehensive national assessment, a new University of Michigan study reveals that nearly 15% of Americans deny that climate change is real. Scientists have long warned that a warming climate will cause communities around the globe to face increasing risks due to unprecedented levels of flooding, wildfires, heat stress, sea-level rise and more. Though the science is sound — even showing that human-induced, climate-related natural disasters are growing in frequency and intensity sooner than originally anticipated — climate change is still not wholly accepted as true in the United States. The researchers used Twitter (now X) data from 2017-19 and artificial intelligence techniques to understand how social media has spread climate change denialism, analyzing the data to estimate climate change belief and denial rates. The study, published online Feb. 14 in the journal Scientific Reports, also identified key influencers, such as former President Donald Trump, and how they spread and cement misinformation about climate change by leveraging world and weather events. Read more about this study.

Survey shows burnout rate is high among Michigan nurses

Ninety-four percent of Michigan nurses report emotional exhaustion, with younger nurses significantly more likely to report burnout than colleagues over 45, according to a School of Nursing survey. “I’ve been studying nurse burnout for 20 years and these are among the highest numbers I’ve seen,” said principal investigator Christopher Friese, Elizabeth Tone Hosmer Professor of Nursing and professor of nursing in the School of Nursing, and professor of health management and policy in the School of Public Health. The study examined three outcomes among nurses: emotional exhaustion, a key component of burnout; thoughts of self-harm and overall wellness; and identified interventions. “Findings from this study are a call to action to generate evidence-based system-level interventions to promote nurses’ health, address emotional exhaustion and promote well-being of the nursing workforce,” said lead author Marita Titler, professor emerita of nursing. Read more about the survey.

Long COVID, other infections linked to chronic pain conditions

Many patients continue to struggle in the wake of the pandemic as they grapple with ongoing symptoms triggered by COVID-19 infection, a condition commonly known as long COVID. However, the onset of symptoms such as brain fog, fatigue, headache and other types of pain is not unique to COVID infection, according to a new U-M study. What’s more, these patients may be helped by capitalizing on the body of research around chronic overlapping pain conditions, such as fibromyalgia, migraine, low back pain and others. The work, led by Rachel Bergmans, research assistant professor of anesthesiology, and a team from Michigan Medicine’s Chronic Pain & Fatigue Research Center, sought to identify whether long COVID was distinct from other pain syndromes and whether chronic pain conditions increased the risk of features of long COVID. They discovered that having a COPC increased the risk for long COVID features in each group and had a similar effect size as sex or being hospitalized for COVID, known risk factors for long COVID. Interestingly, those with influenza were even more likely than those with COVID infection to have features of long COVID. Furthermore, long COVID features were found in a little over 24% of people with COPCs even in the absence of infection. Read more about the research.

AI tool for early sepsis detection may be cribbing doctors’ suspicions

Proprietary artificial intelligence software designed to be an early warning system for sepsis can’t differentiate high and low risk patients before they receive treatments, according to a new U-M study. The tool, named the Epic Sepsis Model, is part of Epic’s electronic medical record software, which serves 54% of patients in the United States and 2.5% of patients internationally, according to a statement from the company’s CEO reported by the Wisconsin State Journal. It automatically generates sepsis risk estimates in the records of hospitalized patients every 20 minutes, which clinicians hope can allow them to detect when a patient might get sepsis before things go bad. Sepsis is responsible for a third of all hospital deaths in the U.S., and early treatment is key to patient survival. The hope is that AI predictions could be instrumental in making that happen, but at present, they don’t seem to be getting more out of patient data than clinicians are. Read more about this study.

Compiled by James Iseler, The University Record


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