Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Hospital readmissions present an increasingly important challenge for health‐care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post‐discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post‐discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay‐time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi‐methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40–70% of readmissions before they generate an emergency readmission.     

I agreed to go because........examining the Agency of Children within a phenomenon conceptualised as trafficking in Ghana

The legal and policy framework on child trafficking is dismissive of children's agency and regards them as vulnerable personalities. This paper examines the scope of children's agency in their transportation to other communities for fishing in Ghana. Using a phenomenological design, 17 child returnees' views were elicited on their movement for fishing activities. Findings revealed that children's agentive capabilities progressed primarily from consent giving to negotiation. Highlighting the relational and material context within which children's agency unfolds, the study suggests that it is erroneous to generally ascribe the discursive label of ‘innocence’ to children who are considered as trafficked.     

Undoing raciolinguistics

In this commentary, we discuss common pitfalls associated with the study of race and language, focusing specifically on the recent emergence of raciolinguistics as a frame for these efforts. We examine how raciolinguistics can be taken up in ways that silo discussions of race from the rest of linguistics—as something that the “raciolinguists” do—such that careful study of issues including colonialism, power, and societal hierarchies is perpetually pushed to the margins of the field. We also consider how the nominalization of raciolinguistics can suggest that race and language are agreed upon objects in ways that reproduce troublesome essentializations. We show how a raciolinguistic perspective can resist such tendencies by continually interrogating the colonial reproduction and transformation of modern knowledge projects and lifeways across societal contexts, as well as by continually examining the fundamental nature of language, race, and power. We end with what we see as the implications of a raciolinguistic perspective for all of linguistics.     

Red Schools,Blue Schools? Republican and Democratic Student Groups at U.S. Colleges and Universities1

How prevalent are Republican and Democratic student groups, and why are some schools home to Republican and Democratic student groups while other schools are not? Some commentators and scholars suggest that Republican student groups may be less prevalent than Democratic student groups and, when present, will likely be found at “red schools” (rather than “blue schools”) in Republican-leaning areas of the country. However, other scholars argue that both Republican and Democratic student groups should be similar in their overall prevalence and located at a similar set of “engaged schools” (as opposed to “unengaged schools”). Analyzing our original database of Republican and Democratic student organizations across 1,953 four-year, not-for-profit U.S. colleges and universities, we first show that Republican student groups are nearly as common as Democratic student groups: Republican student groups can be found at 39% of campuses, while Democratic student groups are present on 40% of campuses. Employing binary and multinomial logistic regression analyses, we then show that Republican and Democratic student groups tend to be located at the same types of schools, that is, larger, wealthier, public schools that offer political science majors. Our article holds significant implications for theorizing on student organization presence more generally.     

Everyday Race: Millennials,Generation Z,and the Census

The census is a central feature of U.S. governance, providing important information regarding the U.S. population used to determine legislative representation and the allocation of federal funding. The count also involves a measure of race and ethnicity, however the sociopolitical construction of race means census categories have shifted throughout history. Existing data highlight how current classifications do not fully capture the identities of different groups; however, less is known about the views of young adults. Utilizing interview and journal data wherein 84 college students from the Millennial and Generation Z cohorts were asked to reflect on the 2010 and 2020 census race questions, our findings reveal a major gap between census classification and everyday experiences with racial categorization. Our study provides meaningful insights into how young adults think about the census race and ethnicity categories, with implications for future research and census collection.