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Midlife and Older Chinese Adults’ Preferences for End-of-Life Care Decision-Making: Patterns and Correlates

Yifan Lou, Jinyu Liu, Deborah Carr, Yaolin Pei, Bei Wu

Research on Aging·2026

We used latent class analysis to identify profiles of midlife and older Chinese adults’ preferences regarding involvement in three dimensions of end-of-life care: information exchange, deliberation, and decisional control. Data are from 548 urban and rural Shanghai residents aged 50+. We used multinomial logistic regression to evaluate associations between individual- and family-oriented beliefs and latent class membership. We identified four profiles. Lead decision-makers (49%) prefer to take charge of information exchange and final decisions, while valuing collaborative discussions. Autonomous decision-makers (19%) prefer making decisions without others’ input. Delegating decision-makers (16%) prefer that family or doctors decide. Acquiescent decision-makers (15%) prefer to follow their family’s wishes if discrepant preferences exist. Among family-oriented values, only strong extended familism beliefs were associated with delegating decision-making. Individuals’ desire for a “good death” was strongly linked to independent decision-making. Practitioners should assess, understand, and develop practices to ensure decision-making preferences are met in Chinese families.

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Exploring Intersectionality Among Latinos Living with HIV: A CHAID Analysis of Health Outcomes and Care Engagement

Leah M. Varga, Morgan Byrne, Greta Bauer, Sarah Watkins, Leah Bauder, Marinella Temprosa, Michael A. Horberg, Suyanna Linhales Barker, Carlos E. Rodriguez-Diaz, Anne Monroe, Amanda Castel

Journal of Racial and Ethnic Health Disparities·2026

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to be intimate with someone who is flawed requires us to expose our own flaws.

gor

Substack·2026

most people say they want love as if it is something that arrives fully formed and then simply stays.

★ 5.0 (2)

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Hamiltonian Monte Carlo for (Physics) Dummies

Arghya Mukherjee, Dootika Vats

arXiv·2026

Sampling-based inference has seen a surge of interest in recent years. Hamiltonian Monte Carlo (HMC) has emerged as a powerful algorithm that leverages concepts from Hamiltonian dynamics to efficiently explore complex target distributions. Variants of HMC are available in popular software packages, enabling off-the-shelf implementations that have greatly benefited the statistics and machine learning communities. At the same time, the availability of such black-box implementations has made it challenging for users to understand the inner workings of HMC, especially when they are unfamiliar with the underlying physical principles. We provide a pedagogical overview of HMC that aims to bridge the gap between its theoretical foundations and practical applicability. This review article seeks to make HMC more accessible to applied researchers by highlighting its advantages, limitations, and role in enabling scalable and exact Bayesian inference for complex models.

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Beyond Normativity

Lewis Williams

Philosophy and Phenomenological Research·2026

Recent years have seen a growing number of philosophers come to defend normative nihilism. Even if their arguments do not induce in many a belief in normative nihilism, there may be grounds on which to be less than certain about the falsity of normative nihilism. How, then, can we accommodate the possibility of normative nihilism when deliberating about what to do? In this article, I defend two theses. First, I distinguish between normative questions and deliberative questions and argue that the question “what am I to do under uncertainty about the truth of normative nihilism?” is best understood as an example of the latter. Second, I propose a novel framework for deliberation under uncertainty about the truth of normative nihilism: maximize expected decision‐relevance. A striking upshot of this article is that there is an important sense in which we need make no mistake by flouting normative considerations under uncertainty about the truth of normative nihilism.

★ 4.0 (1)

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Ideological bias in the production of research findings

George J. Borjas, Nate Breznau

Science Advances·2026

When studying policy-relevant topics, researchers’ policy preferences may shape analytical decisions and results interpretations. Detecting this bias is challenging because the research process is not normally part of an observed experimental setting. Our study exploits an opportunity to observe 158 researchers working independently in 71 teams during an experiment. After being asked their position on immigration policy, they used the same data to answer the same empirical question: Does immigration affect public support for social welfare programs? The researchers estimated 1253 alternative regression models, and the estimated impacts ranged from strongly negative to strongly positive. We find that teams composed of pro-immigration researchers estimated more positive impacts of immigration on public support for social programs, while anti-immigration teams estimated more negative impacts. The differences arise because different teams adopted different model specifications. The underlying research design decisions are the mechanism through which ideology enters the process of producing parameter estimates.

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Newest Papers

Midlife and Older Chinese Adults’ Preferences for End-of-Life Care Decision-Making: Patterns and Correlates

Exploring Intersectionality Among Latinos Living with HIV: A CHAID Analysis of Health Outcomes and Care Engagement

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ORCID

Ideological bias in the production of research findings