Limited English Proficiency and Outcomes in the Intensive Care Unit: An Integrated Review.

INTRODUCTION: Language barriers place patients at risk of substandard care. Hospitalized patients with limited English proficiency (LEP) face unique challenges, especially in the intensive care unit (ICU). The purpose of this review is to critique and synthesize quantitative evidence on LEP and ICU outcomes. METHODOLOGY: Quantitative studies published in English between 1999 and 2022 were queried using intentional terminology. RESULTS: Searches yielded 138 results, with 12 meeting inclusion criteria. The analysis resulted in the extrapolation of five themes pertinent to outcomes of ICU patients or families with LEP: (a) knowledge deficit relating to conditions and care; (b) lack of language-appropriate care; (c) alienation from care process; (d) decreased confidence and ownership of care; and (e) relationship to clinical quality indicators. DISCUSSION: Outcomes associated with LEP were largely negative and revealed unmet needs for ICU patients with LEP. More research is needed to improve linguistically and culturally congruent care in the ICU.

Instructor facilitation mediates students' negative perceptions of active learning instruction.

Studies have demonstrated students' resistance to active learning, despite evidence illustrating that their learning is improved relative to students in lectures. Specifically, while active learning and group work are effective at engaging students in their learning process, studies report that students' perceptions of active learning approaches are not always positive. What remains underexplored is whether students' perceptions of active learning improve with effective instructor facilitation and whether there exists differential perceptions between racially minoritized students and represented students. Here, we estimate students' perceptions of effective instructor facilitation as the mediator in the relationship between active learning and perceptions of learning and perceived utility for class activities (task value). Then, we examine differences by racial identification. We collected classroom observation data to empirically categorize courses as active learning or lecture-based and surveyed 4,257 college students across 25 STEM classrooms at a research-intensive university. We first examined the relationship between active learning on student perceptions and found a negative relationship between active learning and perceptions of learning and task value for both racially minoritized students and represented students. Next, we assessed whether students' perceptions of instructor effectiveness in facilitating group activities mediate these negative relationships. We found that, on average, students of all races were more likely to positively perceive instructor facilitation in active learning classes relative to lectures. In turn, the positive perceptions of instructor facilitation partially suppressed the negative relationship between active learning and perceptions of learning and task value. These results demonstrate that effective instructor facilitation can influence both students' self-assessment of learning and perceived utility of the learning activities, and underscores the importance of developing pedagogical competence among college instructors.

Directed Convergence in Stable Percept Acquisition.

We view a perceptual capacity as a nondeductive inference, represented as a function from a set of premises to a set of conclusions. The application of the function to a single premise to produce a single conclusion is called a "percept" or "instantaneous percept." We define a stable percept as a convergent sequence of instantaneous percepts. Assuming that the sets of premises and conclusions are metric spaces, we introduce a strategy for acquiring stable percepts, called directed convergence. We consider probabilistic inferences, where the premise and conclusion sets are spaces of probability measures, and in this context we study Bayesian probabilistic/recursive inference. In this type of Bayesian inference the premises are probability measures, and the prior as well as the posterior is updated nontrivially at each iteration. This type of Bayesian inference is distinguished from classical Bayesian statistical inference where the prior remains fixed, and the posterior evolves by conditioning on successively more punctual premises. We indicate how the directed convergence procedure may be implemented in the context of Bayesian probabilistic/recursive inference. We discuss how the L(infinity) metric can be used to give numerical control of this type of Bayesian directed convergence. Copyright 2001 Academic Press.