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Recent evidence suggests that higher education promotes moral attitudes typical of the progressive left. What aspects of the university experience contribute to this moral change? We conduct an exploratory analysis unpacking how curricular content and peer networks—two aspects of the 'bundle' of social influences that occur in university settings—might affect moral attitudes. Using two waves of data from students at a Canadian university (n = 232), we find some evidence that exposure to content related to social justice and involvement in left-leaning university peer circles can promote more individualistic forms of morality over 'binding' moral concerns for traditional social order, and heighten a more absolutist endorsement of social justice. Taken together, the university experience appears to be morally formative, but not uniformly so: moral change is shaped by a combination of factors implicating both formal and informal aspects of university life which students experience at varying rates. [ABSTRACT FROM AUTHOR]

Medical schools' curricula have expanded over the decades to incorporate important new medical breakthroughs and discoveries. Their current focus and overall structures remain, however, stubbornly captive of early 20th‐century thinking, with changes having been undertaken in a piecemeal fashion. Indeed, since the notable Flexner reform in 1910, medical schools' study plans have suffered successive and typically always partial adjustments which have failed to keep up with scientific, technological and sociological change. This difficulty may be attributable to the well‐known conservatism of medical schools, where updating study plans is a process that invariably encounters numerous barriers to change. These observations were afforded detailed attention some 15 years ago when de Oliveira wrote: 'it is now perfectly demonstrated that public medical schools have not been able to adapt their operation in depth and in due time to the new demands of teaching dictated by an explosive scientific and technological development'. Recent advances in communication and information technologies, as well as the introduction of new pedagogical techniques, have the potential to bring significant benefits to medical practice and healthcare systems, but these have not in the main become properly taught and utilized. The proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was initially regarded as highly speculative, yet systems approaches to biology and medicine are now beginning to provide experience of both health and disease at the molecular, cellular and organ levels. Medicine is a broad scientific field. In contrast to the 19th century, current medical 'sectarianism' is a positive by‐product of rapid and gratifying medical progress, and the multiplicity of new models means that the lines of evidence legitimately bearing on practice and health policymaking are already highly diverse and likely to become ever more variegated over time. Put simply, most sound decisions, by definition, will be evidence‐informed and not evidence‐based, where divergence may be as informative as convergence. Here, the most enduring lesson of history is, perhaps, that clinical medicine is constantly rediscovering its humanistic core. Complexities create opportunities for innovation. In innovative environments, high‐performing organizations are finding ways to create a culture that supports a diverse workforce preparing to deliver different models of care, with direct implications for excellence of patient experience and strong repercussions for medical education. The COVID‐19 crisis saw major increases in the use of telemedicine, virtual office visits and other forms of online contact, and these are likely to increase considerably. This particular transformation will not be easy or comfortable to make. But reconfiguration of medical education seems inevitable, fuelled by online educational technology and the need to transform clinical training to more outpatient settings with promotion based on competency and person‐centeredness, not simply time. As we prepare to enter 2024, this is an exciting time to be working in healthcare. We have more evidence than ever about how to provide high quality, person‐centered care, and to keep patients safe. Shame on us if there is any hesitation about applying this knowledge to make the healthcare experience better for patients and providers. Embracing change and making continuous improvements are essential and urgent priorities for medicine and healthcare and, as we describe in the current article, will become more and more indispensably important in our rapidly changing world. [ABSTRACT FROM AUTHOR]

The recent change in the general social survey (GSS) to a rotating panel design is a landmark development for social scientists. Sociological methodologists have argued that fixed-effects (FE) models are generally the best starting point for analyzing panel data because they allow analysts to control for unobserved time-constant heterogeneity. We review these treatments and demonstrate the advantages of FE models in the context of the GSS. We also show, however, that FE models have two rarely tested assumptions that can seriously bias parameter estimates when violated. We provide simple tests for these assumptions. We further demonstrate that FE models are extremely sensitive to the correct specification of temporal lags. We provide a simulation and a proof to show that the use of incorrect lags in FE models can lead to coefficients that are the opposite sign of the true parameter values.

Obtaining predictions from regression models fit to multiply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how predictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how predictions can be obtained using Rubin's rules, that is, by being estimated separately in each imputed data set and then combined. It then demonstrates that predictions can also be calculated directly from the final analysis model. Both approaches yield identical results when predictions rely solely on linear transformations of the coefficients and calculate standard errors using the delta method and diverge only slightly when using nonlinear transformations. However, calculation from the final model is faster, easier to implement, and generates predictions with a clearer relationship to model coefficients. These principles are illustrated using data from the General Social Survey and with a simulation.