A nudge intervention to improve hand hygiene compliance in the hospital.

Hand hygiene among professionals plays a crucial role in preventing healthcare-associated infections, yet poor compliance in hospital settings remains a lasting reason for concern. Nudge theory is an innovative approach to behavioral change first developed in economics and cognitive psychology, and recently spread and discussed in clinical medicine. To assess a combined nudge intervention (localized dispensers, visual reminders, and gain-framed posters) to promote hand hygiene compliance among hospital personnel. A quasi-experimental study including a pre-intervention phase and a post-intervention phase (9 + 9 consecutive months) with 117 professionals overall from three wards in a 350-bed general city hospital. Hand hygiene compliance was measured using direct observations by trained personnel and measurement of alcohol-based hand-rub consumption. Levels of hand hygiene compliance were low in the pre-intervention phase: 11.44% of hand hygiene opportunities prescribed were fulfilled overall. We observed a statistically significant effect of the nudge intervention with an increase to 18.71% (p < 0.001) in the post-intervention phase. Improvement was observed in all experimental settings (the three hospital wards). A statistical comparison across three subsequent periods of the post-intervention phase revealed no significant decay of the effect. An assessment of the collected data on alcohol-based hand-rub consumption indirectly confirms the main result in all experimental settings. Behavioral outcomes concerning hand hygiene in the hospital are indeed affected by contextual, nudging factors to a significant extent. If properly devised, nudging measures can provide a sustainable contribution to increase hand hygiene compliance in a hospital setting.

Bio-inspired protective structures for marine applications.

Materials and structures for marine applications are often required to combine high mechanical performance with lightness, but traditional solutions are not always adequate to the scope. The application of biomimetic science could provide innovative and efficient solutions, whose research is the aim of the present paper. In the current work, bamboo samples were subjected to impact both on the outside and on the inside surface. It was found that the impact strength is correlated to the third power of thickness and that the softer inside layers may act as an energy absorbing structure, which enhance impact resistance. Impact tests were also performed on specimens whose outside surface had been abraded and on whole cylindrical sections. The role of graded and hierarchical structure in impact response, suggested some guidelines for bio-inspired structures design. Four bamboo-inspired structures were designed, based on the idea of combining corrugated panels with different geometrical characteristics to resemble bamboo graded and hierarchical structure. These structures were made using 3D printing and tested in compression. The best performance was obtained by the structure which more closely replicates bamboo's hierarchy. Structure performance could be predicted with reasonable accuracy using buckling theory.

Mind the gap: Physicians' assessment of patients' importance weights in localized prostate cancer.

BACKGROUND: The management of localized prostate cancer is challenging because of the many therapeutic options available, none of which is generally acknowledged as superior to the others in every respect. The selection of the most appropriate treatment should therefore reflect patients' preferences. OBJECTIVE: The purpose of the following study was to pilot a new approach for investigating whether urologists who had previously provided patients with therapeutic advice actually knew their patients' importance weights concerning the relevant aspects of the treatments at issue. METHOD: Participants were patients recently diagnosed with localized prostate cancer (n = 20), urologists (n = 10), and non-medical professionals (architects, n = 10). These last served as a control group for the urologists and were matched to them for age and gender. Patients' importance weights were elicited by two standard methods (Direct Rating and Value Hierarchy). Each urologist was asked to estimate (with Direct Rating) his/her patient's importance weights. The same task was performed by a corresponding architect, who never met the patient and knew only the patient's age. Univariate and bivariate statistical analyses were performed to investigate the association between importance weights as elicited from patients and as estimated by urologists and architects, as well as to assess whether such agreement was attribute-dependent. RESULTS: Participants found both elicitation methods easy to use. The correlation between patients' actual importance weights and urologists' estimates was poor and comparable to that obtained between patients and architects. This result did not depend on the attribute considered, with the sole exception of the attribute "Effectiveness in curing the cancer", which was evaluated as the most important attribute by the majority of participants. CONCLUSION: These findings demonstrate the feasibility of the employed methodology and highlight the need to support preference-sensitive decisions in clinical practice by facilitating the elicitation of patients' importance weights, as well as their communication to physicians.

Double Conjunction Fallacies in Physicians' Probability Judgment.

We report the first empirical data showing a significant amount of double conjunction fallacies in physicians' probability judgments concerning prognosis and diagnosis. Our results support the hypothesis that physicians' probability judgments are guided by assessments of evidential impact between diagnostic conditions and clinical signs. Moreover, we show that, contrary to some influential views, double conjunction fallacies represent an experimentally replicable reasoning bias. We discuss how the phenomenon eludes major current accounts of uncertain reasoning in medicine and beyond and how it relates to clinical practice.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search.

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.

Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time-Consistent?

Inductive reasoning requires exploiting links between evidence and hypotheses. This can be done focusing either on the posterior probability of the hypothesis when updated on the new evidence or on the impact of the new evidence on the credibility of the hypothesis. But are these two cognitive representations equally reliable? This study investigates this question by comparing probability and impact judgments on the same experimental materials. The results indicate that impact judgments are more consistent in time and more accurate than probability judgments. Impact judgments also predict the direction of errors in probability judgments. These findings suggest that human inductive reasoning relies more on estimating evidential impact than on posterior probability.

"First, know thyself": cognition and error in medicine.

Although error is an integral part of the world of medicine, physicians have always been little inclined to take into account their own mistakes and the extraordinary technological progress observed in the last decades does not seem to have resulted in a significant reduction in the percentage of diagnostic errors. The failure in the reduction in diagnostic errors, notwithstanding the considerable investment in human and economic resources, has paved the way to new strategies which were made available by the development of cognitive psychology, the branch of psychology that aims at understanding the mechanisms of human reasoning. This new approach led us to realize that we are not fully rational agents able to take decisions on the basis of logical and probabilistically appropriate evaluations. In us, two different and mostly independent modes of reasoning coexist: a fast or non-analytical reasoning, which tends to be largely automatic and fast-reactive, and a slow or analytical reasoning, which permits to give rationally founded answers. One of the features of the fast mode of reasoning is the employment of standardized rules, termed "heuristics." Heuristics lead physicians to correct choices in a large percentage of cases. Unfortunately, cases exist wherein the heuristic triggered fails to fit the target problem, so that the fast mode of reasoning can lead us to unreflectively perform actions exposing us and others to variable degrees of risk. Cognitive errors arise as a result of these cases. Our review illustrates how cognitive errors can cause diagnostic problems in clinical practice.

Noisy probability judgment, the conjunction fallacy, and rationality: Comment on Costello and Watts (2014).

According to Costello and Watts (2014), probability theory can account for key findings in human judgment research provided that random noise is embedded in the model. We concur with a number of Costello and Watts's remarks, but challenge the empirical adequacy of their model in one of their key illustrations (the conjunction fallacy) on the basis of recent experimental findings. We also discuss how our argument bears on heuristic and rational thinking.

Why quantum probability does not explain the conjunction fallacy.

We agree with Pothos & Busemeyer (P&B) that formal tools can be fruitfully employed to model human judgment under uncertainty, including well-known departures from principles of classical probability. However, existing findings either contradict P&B's quantum probability approach or support it to a limited extent. The conjunction fallacy serves as a key illustration of both kinds of problems.

On the determinants of the conjunction fallacy: probability versus inductive confirmation.

Major recent interpretations of the conjunction fallacy postulate that people assess the probability of a conjunction according to (non-normative) averaging rules as applied to the constituents' probabilities or represent the conjunction fallacy as an effect of random error in the judgment process. In the present contribution, we contrast such accounts with a different reading of the phenomenon based on the notion of inductive confirmation as defined by contemporary Bayesian theorists. Averaging rule hypotheses along with the random error model and many other existing proposals are shown to all imply that conjunction fallacy rates would rise as the perceived probability of the added conjunct does. By contrast, our account predicts that the conjunction fallacy depends on the added conjunct being perceived as inductively confirmed. Four studies are reported in which the judged probability versus confirmation of the added conjunct have been systematically manipulated and dissociated. The results consistently favor a confirmation-theoretic account of the conjunction fallacy against competing views. Our proposal is also discussed in connection with related issues in the study of human inductive reasoning.

Updating: learning versus supposing.

Bayesian orthodoxy posits a tight relationship between conditional probability and updating. Namely, the probability of an event A after learning B should equal the conditional probability of A given B prior to learning B. We examine whether ordinary judgment conforms to the orthodox view. In three experiments we found substantial differences between the conditional probability of an event A supposing an event B compared to the probability of A after having learned B. Specifically, supposing B appears to have less impact on the credibility of A than learning that B is true.