Primary Care Physician Gender and Electronic Health Record Workload.

Background: Prior research indicates that female physicians spend more time working in the electronic health record (EHR) than do male physicians.

Objective: To examine gender differences in EHR usage among primary care physicians and identify potential causes for those differences.

Design: Retrospective study of EHR usage by primary care physicians (PCPs) in an academic hospital system.

Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic.

Unlabelled: The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size.

How production networks amplify economic growth.

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth.

Health benefits of decreases in on-road transportation emissions in the United States from 2008 to 2017.

Decades of air pollution regulation have yielded enormous benefits in the United States, but vehicle emissions remain a climate and public health issue. Studies have quantified the vehicle-related fine particulate matter (PM)-attributable mortality but lack the combination of proper counterfactual scenarios, latest epidemiological evidence, and detailed spatial resolution; all needed to assess the benefits of recent emission reductions. We use this combination to assess PM-attributable health benefits and also assess the climate benefits of on-road emission reductions between 2008 and 2017.

Effects of demolishing abandoned buildings on firearm violence: a moderation analysis using aerial imagery and deep learning.

Purpose: Demolishing abandoned buildings has been found to reduce nearby firearm violence. However, these effects might vary within cities and across time scales. We aimed to identify potential moderators of the effects of demolitions on firearm violence using a novel approach that combined machine learning and aerial imagery.

Further Inspection: Integrating Housing Code Enforcement and Social Services to Improve Community Health.

As a result of working inside homes, city housing inspectors witness hidden and serious threats to public health. However, systems to respond to the range of problems they encounter are lacking. In this study, we describe the impact and enabling environment for integrating a novel Social Service Referral Program within the Inspectional Services Department in Chelsea, MA.

Advancing health equity with artificial intelligence.

Population and public health are in the midst of an artificial intelligence revolution capable of radically altering existing models of care delivery and practice. Just as AI seeks to mirror human cognition through its data-driven analytics, it can also reflect the biases present in our collective conscience. In this Viewpoint, we use past and counterfactual examples to illustrate the sequelae of unmitigated bias in healthcare artificial intelligence.

Create a COVID-19 commission.

We need a definitive public reference for the history of events.

Social science research to inform solar geoengineering.

What are the benefits and drawbacks, and for whom?.

Toward constructive disagreement about geoengineering.

A shared taxonomy of concerns may help.

The association of paid medical and caregiving leave with the economic security and wellbeing of service sector workers.

Background: Service-sector workers in the U.S. face extremely limited access to paid family and medical leave, but little research has examined the consequences for worker wellbeing.

Growth In ACA-Compliant Marketplace Enrollment And Spending Risk Changes During The COVID-19 Pandemic.

In 2020 the COVID-19 pandemic caused millions to lose their jobs and, consequently, their employer-sponsored health insurance. Enacted in 2010, the Affordable Care Act (ACA) created safeguards for such events by expanding Medicaid coverage and establishing Marketplaces through which people could purchase health insurance. Using a novel national data set with information on ACA-compliant individual insurance plans, we found large increases in Marketplace enrollment in 2020 compared with 2019 but with varying percentage increases and spending risk implications across states.

Comparing the sensitivity of face matching assessments to detect face perception impairments.

Numerous neurological, developmental, and psychiatric conditions demonstrate impaired face recognition, which can be socially debilitating. These impairments can be caused by either deficient face perception or face memory mechanisms. Though there are well-validated, sensitive measures of face memory impairments, it currently remains unclear which assessments best measure face perception impairments.

Class Inequality in Parental Childcare Time: Evidence from Synthetic Couples in the ATUS.

The time that parents spend teaching and playing with their young children has important consequences for later life achievement and attainment. Previous research suggests that there are significant class inequalities in how much time parents devote to this kind of developmental childcare in the United States. Yet, due in part to data limitations, prior research has not accounted for how class inequalities in family structure, assortative mating, and specialization between partners may exacerbate or ameliorate these gaps.

Improving health and economic security by reducing work schedule uncertainty.

Work schedules in the service sector are routinely unstable and unpredictable, and this unpredictability may have harmful effects on health and economic insecurity. However, because schedule unpredictability often coincides with low wages and other dimensions of poor job quality, the causal effects of unpredictable work schedules are uncertain. Seattle's Secure Scheduling ordinance, enacted in 2017, mandated greater schedule predictability, providing an opportunity to examine the causal relationship between work scheduling and worker health and economic security.

Poison Parasite Counter: Turning Duplicitous Mass Communications Into Self-Negating Memory-Retrieval Cues.

Disinformation in politics, advertising, and mass communications has proliferated in recent years. Few counterargumentation strategies have proven effective at undermining a deceptive message over time. This article introduces the Poison Parasite Counter (PPC), a cognitive-science-based strategy for durably countering deceptive communications.

Diabetes Microvascular Disease Diagnosis and Treatment After High-Deductible Health Plan Enrollment.

Objective: The Affordable Care Act mandates that primary preventive services have no out-of-pocket costs but does not exempt secondary prevention from out-of-pocket costs. Most commercially insured patients with diabetes have high-deductible health plans (HDHPs) that subject key microvascular disease-related services to high out-of-pocket costs. Brief treatment delays can significantly worsen microvascular disease outcomes.

Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction.

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction.