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AbstractBackgroundWithin healthcare, the barriers and enablers that influence clinicians’ ability to speak up are well researched. However, despite the receiver of the message being identified as a key barrier to a speaker voicing a concern, there have been very few receiver-focused studies. As a result, little is known about the barriers and enablers that influence message reception. Understanding these can help inform speaking up training and ultimately enhance patient safety through more effective clinical communication.ObjectivesTo identify enabling or inhibiting factors that influence the receiver’s reception and response to a speaking up message, and if the identified barriers and enablers are related to speaker or receiver characteristics.Design and methodsTwenty-two interdisciplinary simulations were video recorded and transcribed. Simulation participants formed the patient discharge team and were receivers of a speaking up message, delivered by a nurse at the patient’s bedside. How the message was delivered (verbose or abrupt wording), was manipulated and counterbalanced across the simulations. Within the post simulation debriefs, barriers and enablers of being a receiver of a message were explored using content analysis.Setting/participantsThis study took place in a large Australian tertiary healthcare setting. Participants were qualified clinicians of varying disciplines and specialties.ResultsA total of 261 barriers and 285 enablers were coded. Results showed that how the message was delivered (differing tone, phases, and manner) influenced what receivers identified as barriers and enablers. Additionally, the receiver’s own cognitive processes, such as making positive attributions of the speaker and attempting to build rapport and collegiality, better enabled message reception and response. Receiver behaviour was negatively impacted by listening to fix, rather than understand, and not knowing in the moment how to manage their own reactions and appropriately frame a response.ConclusionThe debriefings identified key barriers and enablers to receiving a speaking up message that differ from those previously identified for senders of the speaking up message. Current speaking up programs are predominately speaker centric. This study identified that both speaker and receiver behaviour influenced message reception. Therefore, training must place equal attention on both the speaker and receiver and be inclusive of experiential conversational rehearsal of both positive and challenging encounters.

Appeals to the significance of a culture, tradition, or way of life can simultaneously edify and alienate different audiences as they serve an identity-forming and other-excluding function. The imaginary that some cultures are civilized and complex and others natural and simple has been complexly mediated through the historical entanglements and interactions between, on the one hand, colonialism and Eurocentrism and, on the other, varieties of anti-colonial resistance and the postcolonial formation of contemporary national, cultural, and religious identities. It is in this context that claims of the ostensible intrinsic naturalness and “greenness” of a culture or tradition are perceived and debated.

Gamified online quizzes have gained popularity for their potential in enhancing student learning motivations, creating engaging lessons, and improving learning outcomes. Yet, its application and effectiveness in support of student vocabulary learning have only been explored in synchronous learning setting. This study, hence, extended the exploration by examining the implementation of gamified vocabulary learning in an asynchronous mode and analyzing the impacts on students’ learning outcomes in class. The study was carried out for 10 weeks, involving 300 2nd year students at a Thai university. The instrument and measure comprised 500 English academic words made into 10 vocabulary sets on Quizizz.com, and 10 in-class vocabulary tests. The findings of the study revealed that students’ vocabulary learning practice in asynchronous mode had positive correlations with and could significantly predict their in-class vocabulary results. Female students outperformed male students; nonetheless, there was no significant difference across academic majors.

Heidegger’s early philosophical project was identified with a nihilistic philosophy of nothingness after the 1927 publication of Being and Time—with its depiction of the radical existential anxiety of being-towards-death—and his 1929 lecture “What is Metaphysics?”—with its analysis of the loss of all orientation and comportment in the face of an impersonal self-nihilating nothingness. Heidegger’s philosophy of nothingness would be contrasted in both Germany and Japan in the 1930s and 1940s with “Oriental nothingness” by authors such as Kitayama Junyū, a neglected Japanese philosopher active in Germany and an early interpreter of Heidegger and Nishida. In this contribution, I trace how Heidegger’s reflections on nothingness and emptiness (which are distinct yet intertwined expressions) become interculturally entangled with East Asian discourses in the early reception of his thought, particularly in Kitayama and the introduction of Nishida’s philosophy into Germany, and their significance in Heidegger’s “A Dialogue on Language”.

ChatGPT is a sophisticated large-language model able to answer high-level questions in a way that is does not trigger conventional plagiarism detectors. Concerns have been raised that and similar forms of ‘generative AI’ pose a significant threat to academic integrity in higher education. To evaluate this risk in the context of legal education specifically, this project had ChatGPT (using the GPT3.5 model available in January 2023) generate answers to twenty-four different exams from an English-language law school based in a common law jurisdiction. It found that the system performed best on exams that were essay-based and asked students to discuss international legal instruments or general legal principles not necessarily specific to any jurisdiction. It performed worst on exams that featured problem-style or “issue spotting” questions asking students to apply an invented factual scenario to local legislation or jurisprudence. While the project suggests that for the most part conventional law school assessments are for the time being relatively immune from the threat generative AI brings, the project provides only a baseline snapshot of how large-language models tackle assessment in higher education. As both the technology improves and students learn how to harness it, increasingly fewer forms of assessment will be beyond its reach. However, rather than attempt to block students from using AI as part of learning and assessment, this paper instead proposes three ways students may be taught to use it in appropriate and ethical ways. While it is clear that generative AI will change how universities teach and assess (across disciplines), a solution of prevention or denial is no solution at all.

Players cooperating in a line is a special while essential phenomenon in real life collaborating activities such as assembly line production, pipeline supply chain management and other streamlining operational settings. In this paper, we study the scenario of cooperative sewage discharge with multiple participants positioning in a line along a river such that the optimization decision and cooperation strategy are mutually affected by both upstream and downstream players. We make three main contributions accordingly: Firstly, we formalize the sewage discharge problem (SDP) for different groups of players, and use greedy strategy and dynamic programming to design the optimal algorithms to solve the SDP in polynomial time. Secondly, we show that the cooperative game defined on sewage discharge problem, referred to as SDG, has a non-empty core due to its special line-positioning structure. Therefore, a grand stable cooperation is guaranteed. Furthermore, inspired by the fact that the SDG is core non-empty while non-convex, we successfully identify a relaxed concept of convexity -- directional-convexity, which can also serve as a sufficient condition for a cooperative game having a non-empty core.

This paper addresses a key question in economic forecasting: does pure noise truly lack predictive power? Economists typically conduct variable selection to eliminate noises from predictors. Yet, we prove a compelling result that in most economic forecasts, the inclusion of noises in predictions yields greater benefits than its exclusion. Furthermore, if the total number of predictors is not sufficiently large, intentionally adding more noises yields superior forecast performance, outperforming benchmark predictors relying on dimension reduction. The intuition lies in economic predictive signals being densely distributed among regression coefficients, maintaining modest forecast bias while diversifying away overall variance, even when a significant proportion of predictors constitute pure noises. One of our empirical demonstrations shows that intentionally adding 300~6,000 pure noises to the Welch and Goyal (2008) dataset achieves a noteworthy 10% out-of-sample R square accuracy in forecasting the annual U.S. equity premium. The performance surpasses the majority of sophisticated machine learning models.