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Abstract Considering the criticality of post-simulation debriefings for skill development, more evidence is needed to establish how specific feedback design features might influence teams’ cognitive and metacognitive processing. The current research therefore investigates the effects of multisource feedback (MSF) and guided facilitation with video review, for both cognitive processing and reflective (meta-cognitive) behaviors during post-simulation debriefings. With a sample of 174 s-year dental students, randomly assigned to 20 teams, the authors conducted high-fidelity simulations of patient emergencies, followed by post-simulation debriefings, using a 2 × 2 factorial design to test the effects of MSF (present vs. absent) and guided facilitation with video review (present vs. absent). According to an ordered network analysis, designed to examine feedback processing levels (individual vs. team) and depth (high vs. low), as well as the presence of metacognitive reflective behaviors (evaluative behaviors, exploration of alternatives, decision-oriented behaviors), teams that received both MSF and guided facilitation demonstrated significantly deeper, team-level processing and more frequent evaluative behaviors. Teams that received only guided facilitation exhibited the highest rates of low-level, individual processing. However, facilitation also produced an additive effect that fostered reflection and a shift from individual- to team-oriented processing. In contrast, MSF alone produced the lowest levels of evaluative behaviors; without facilitation, it does not support team reflection. These results establish that combining MSF with guided facilitation and video review creates synergistic effects for team reflection. Even if MSF can highlight perceived performance discrepancies, teams need facilitation to interpret and learn collaboratively from the feedback. 

ABSTRACT Organisms may simultaneously face thermal, desiccation and nutritional stress under climate change. Understanding the effects arising from the interactions among these stressors is relevant for predicting organisms' responses to climate change and for developing effective conservation strategies. Using both dynamic and static protocols, we assessed for the first time how sublethal desiccation exposure (at 16.7%, 50.0% and 83.3% of LD50) impacts the heat tolerance of foragers from two social bee species found on the Greek island of Lesbos: the managed European honey bee, Apis mellifera, and the wild, ground-nesting sweat bee Lasioglossum malachurum. In addition, we explored how a short-term starvation period (24 h), followed by a moderate sublethal desiccation exposure (50% of LD50), influences honey bee heat tolerance. We found that neither the critical thermal maximum (CTmax) nor the time to heat stupor was significantly impacted by sublethal desiccation exposure in either species. Similarly, starvation followed by moderate sublethal desiccation did not affect the average CTmax estimate, but it did increase its variance. Our results suggest that sublethal exposure to these environmental stressors may not always lead to significant changes in bees' heat tolerance or increase vulnerability to rapid temperature changes during extreme weather events, such as heat waves. However, the increase in CTmax variance suggests greater variability in individual responses to temperature stress under climate change, which may impact colony-level performance. The ability to withstand desiccation may be impacted by unmeasured hypoxic conditions and the overall effect of these stressors on solitary species remains to be assessed. 

ABSTRACT Hydrostatic skeletons, such as an elephant trunk or a squid tentacle, permit the transmission of mechanical work through a soft body. Despite the ubiquity of these structures among animals, we generally do not understand how differences in their morphology affect their ability to transmit muscular work. Therefore, the present study used mathematical modeling, morphometrics, and kinematics to understand the transmission of force and displacement in the tube feet of the juvenile six-rayed star (Leptasterias sp.). An inverse-dynamic analysis revealed that the forces generated by the feet during crawling primarily serve to overcome the submerged weight of the body. These forces were disproportionately generated by the feet at more proximal positions along each ray, which were used more frequently for crawling. Owing to a combination of mechanical advantage and muscle mass, these proximal feet exhibited a greater capacity for force generation than the distal feet. However, the higher displacement advantage of the more elongated distal feet offer a superior ability to extend the feet into the environment. Therefore, the morphology of tube feet demonstrates a gradient in gearing along each ray that compliments their role in behavior. 

IntroductionMachine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital healthcare, due to their ability to detect fine-grain patterns that are not easily perceived by humans. Yet, ML algorithms have also been critiqued for treating individuals differently based on their demography, thus propagating existing disparities. This paper explores gender and race bias in speech-based ML algorithms that detect behavioral and mental health outcomes. MethodsThis paper examines potential sources of bias in the data used to train the ML, encompassing acoustic features extracted from speech signals and associated labels, as well as in the ML decisions. The paper further examines approaches to reduce existing bias via using the features that are the least informative of one’s demographic information as the ML input, and transforming the feature space in an adversarial manner to diminish the evidence of the demographic information while retaining information about the focal behavioral and mental health state. ResultsResults are presented in two domains, the first pertaining to gender and race bias when estimating levels of anxiety, and the second pertaining to gender bias in detecting depression. Findings indicate the presence of statistically significant differences in both acoustic features and labels among demographic groups, as well as differential ML performance among groups. The statistically significant differences present in the label space are partially preserved in the ML decisions. Although variations in ML performance across demographic groups were noted, results are mixed regarding the models’ ability to accurately estimate healthcare outcomes for the sensitive groups. DiscussionThese findings underscore the necessity for careful and thoughtful design in developing ML models that are capable of maintaining crucial aspects of the data and perform effectively across all populations in digital healthcare applications.