Grounded in Bandura’s social cognitive theory of moral thought and action, we develop a conceptual model linking supervisors’ perceptions of organizational injustice and abusive supervision with moral disengagement mechanisms acting as the underlying process. Specifically, we elaborate why and how supervisors’ experiences of each type of injustice would trigger their adoption of distinctive moral disengagement mechanisms, which in turn lead to their abusive supervisory conduct. The present conceptual model sheds new light on linking organizational injustice to abusive supervision from a (...) moral perspective. In addition, it also provides important theoretical and managerial implications to our current understanding of why and how abusive supervision happens. (shrink)

Introduction While quizzing during informed consent for research to ensure understanding has become commonplace, it is unclear whether the quizzing itself is problematic for potential participants. In this study, we address this issue in a multinational HIV prevention research trial enrolling injection drug users in China and Thailand. Methods Enrolment procedures included an informed consent comprehension quiz. An informed consent survey followed. Results 525 participants completed the informed consent survey (Heng County, China=255, Xinjiang, China=229, Chiang Mai, Thailand=41). Mean age was (...) 33 and mean educational level was 8 yrs. While quizzing was felt to be a good way to determine if a person understands the nature of clinical trial participation (97%) and participants did not generally find the quiz to be problematic, minorities of respondents felt pressured (6%); anxious (5%); bored (5%); minded (5%); and did not find the questions easy (13%). In multivariate analysis, lower educational level was associated with not minding the quizzing (6–10 yrs vs 0–5 yrs: OR=0.27, p=0.03; more than 11 yrs vs 0–5 yrs: OR=0.18, p=0.03). There were also site differences (Heng County vs Xinjiang) in feeling anxious (OR=0.07; p=<0.01), not minding (OR=0.26; p=0.03), being bored (OR=0.25; p=0.01) and not finding the questions easy (OR=0.10; p=<0.01). Conclusions Quizzing during the informed consent process can be problematic for a minority of participants. These problems may be associated with the setting in which research takes place and educational level. Further research is needed to develop, test and implement alternative methods of ensuring comprehension of informed consent. Trial Registration clinicaltrials.gov number NCT00270257. (shrink)

OBJECTIVE: To report and analyse the pattern of end-of-life decision making for terminal Chinese cancer patients. DESIGN: Retrospective descriptive study. SETTING: A cancer clinical trials unit in a large teaching hospital. PATIENTS: From April 1992 to August 1997, 177 consecutive deaths of cancer clinical trial patients were studied. MAIN MEASUREMENT: Basic demographic data, patient status at the time of signing a DNR consent, or at the moment of returning home to die are documented, and circumstances surrounding these events evaluated. RESULTS: (...) DNR orders were written for 64.4% of patients. Patients in pain (odds ratio 0.45, 95% CI 0.22-0.89), especially if requiring opioid analgesia (odds ratio 0.40, 95% CI 0.21-0.77), were factors associated with a higher probability of such an order. Thirty-five patients were taken home to die, a more likely occurrence if the patient was over 75 years (odds ratio 0.12, 95% CI 0.04-0.34), had children (odds ratio 0.14, 95% CI 0.02-0.79), had Taiwanese as a first language (odds ratio 6.74, 95% CI 3.04-14.93), or was unable to intake orally (odds ratio 2.73, 95% CI 1.26-5.92). CPR was performed in 30 patients, none survived to discharge. CONCLUSIONS: DNR orders are instituted in a large proportion of dying Chinese cancer patients in a cancer centre, however, the order is seldom signed by the patient personally. This study also illustrates that as many as 20% of dying patients are taken home to die, in accordance with local custom. (shrink)

The justice literature has coalesced around the notion that actors (e.g., supervisors) tend to utilize the norm of equity for resource allocation decisions because it is generally considered most fair when employees who contribute more to the organization receive more resources. Yet, actors might sometimes utilize a need norm to allocate resources to those most in need. Studies that have addressed need-based resource allocations have assumed a relatively straightforward conceptualization of need. However, research from related areas suggests that multiple characteristics (...) of the need itself could trigger actors’ use of a need norm to allocate resources. We advance a theoretical framework that outlines various need characteristics that drive actors’ use of a need norm. The framework draws on the processes outlined in attribution theory and integrates those with the content domains addressed in fairness theory. A discussion of the implications for justice, attribution, and fairness theory research follows. (shrink)

In two experiments, subjects’ task was to decide whether a binocularly viewed target word was evaluatively good (e.g., fame, comedy, rescue) or bad (e.g., stress, detest, malaria) in meaning. Just prior to this target word, a priming word was presented to the nondominant eye, and masked by an immediately following presentation of a letter—fragment pattern to the dominant eye. (Masking effectiveness was demonstrated by subjects’ failure to discriminate the left vs. right position of a test series of words.) In Experiment (...) 1, which used evaluatively positive or negative words as priming stimuli, judgment latency for the evaluative decision task was facilitated by primes that agreed in evaluation with targets, and was retarded by primes that disagreed in evaluation with targets. This result demonstrated that the evaluative meaning of priming stimuli was processed under conditions that prevented subjects from detecting their presence. Primes in Experiment 2 were 2-word strings for which the evaluative meaning of individual words was orthogonal to the evaluative sentence meaning (e.g., the two evaluatively negative words, "enemy fails,“ make up an evaluatively positive sentence). Results of Experiment 2 indicated that masked priming effects were influenced by the evaluative meanings of individual words, but not by their sentence meanings. This result supported the conclusion that the processing of undetected, dichoptically masked words is limited to analyses that are not powerful enough to extract sentence meanings. (shrink)

Mengzi is known for his sophisticated views on human nature and moral psychology. These essays explore a range of philosophical ideas at the core of his moral philosophy and relate them to both traditional Chinese and current Western philosophical concerns. The introduction provides historical background and philosophical context, and discusses each of the selections alongside Mengzi's work as a whole.

Most theories of slurs fall into one of two families: those which understand slurring terms to involve special descriptive/informational content (however conveyed), and those which understand them to encode special emotive/expressive content. Our view is that both offer essential insights, but that part of what sets slurs apart is use-theoretic content. In particular, we urge that slurring words belong at the intersection of a number of categories in a sociolinguistic register taxonomy, one that usually includes [+slang] and [+vulgar] and always (...) includes [-polite] and [+derogatory]. Thus, e.g., what distinguishes ‘Chinese’ from ‘chink’ is neither a peculiar sort of descriptive nor emotional content, but rather the fact that ‘chink’ is lexically marked as belonging to different registers than ‘Chinese’. It is, moreover, partly such facts which makes slurring ethically unacceptable. (shrink)

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented (...) to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method. (shrink)

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to (...) that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity. (shrink)