Developing a Research Agenda on Resident-to-Resident Aggression: Recommendations From a Consensus Conference

This article provides an overview of the development of a research agenda on resident-to-resident aggression (RRA) in long-term care facilities by an expert panel of researchers and practitioners. A 1-day consensus-building workshop using a modified Delphi approach was held to gain consensus on nomenclature and an operational definition for RRA, to identify RRA research priorities, and to develop a roadmap for future research on these priorities. Among the six identified terms in the literature, RRA was selected. The top five priorities were: (a) developing/assessing RRA environmental interventions; (b) identification of the environmental factors triggering RRA; (c) incidence/prevalence of RRA; (d) developing/assessing staff RRA education interventions; and (e) identification of RRA perpetrator and victim characteristics. Given the significant harm RRA poses for long-term care residents, this meeting is an important milestone, as it is the first organized effort to mobilize knowledge on this under-studied topic at the research, clinical, and policy levels.     

Where It's At! The Role of Best Friends and Peer Group Members in Young Adults' Alcohol Use

We examined the hypothesis that best friends and members from a broader peer group would not differ in the amount of influence they have on young adults' alcohol consumption and that what counts would be the mere presence of drinking peers in a given context—irrespective of the type of relationship such peers would have with the target young adult. Data were used from a naturalistic observation study that was conducted in a “bar‐lab” among 221 young adults aged 18–25 years. Both hierarchical and multilevel regression analyses showed that group effects (i.e., average group levels of alcohol consumption) explained young adults' drinking. When taking into account these group effects, best friends' alcohol consumption in the bar‐lab was a nonsignificant predictor of young adults' alcohol use—although best friends' questionnaire‐assessed drinking did predict alcohol consumption in the bar‐lab.     

Using vulnerability performance indicators to attain food supply chain robustness

High effectiveness and leanness of modern supply chains (SCs) increase their vulnerability, i.e. susceptibility to disturbances reflected in non-robust SC performances. Both the SC management literature and SC professionals indicate the need for the development of SC vulnerability assessment tools. In this article, a new method for vulnerability assessment, the VULA method, is presented. The VULA method helps to identify how much a company would underperform on a specific Key Performance Indicator in the case of a disturbance, how often this would happen and how long it would last. It ultimately informs the decision about whether process redesign is appropriate and what kind of redesign strategies should be used in order to increase the SC's robustness. The applicability of the VULA method is demonstrated in the context of a meat SC using discrete-event simulation to conduct the performance analysis.     

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value

Abstract

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “The D-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.     

Catholicism and the Economics of Fertility

The economic approach to fertility is an application of the economic theory of consumer behaviour. It is assumed that utility-maximizing decisions regarding children are affected by explicit and implicit prices and income. One of the criticisms of this approach is that social norms tend to be given short shrift. In this paper, we examine the effect of Catholicism on fertility in the United States. Several new findings are presented. Most importantly, it is shown that many studies on Catholicism are flawed because of sample selection bias, which arises because ex-Catholics prefer smaller families than non-Catholics. We also show that religious activity has no effect on fertility if it is treated as an endogenous variable. Further, it is shown that the fertility transition in the United States is partly related to the changing effect of Catholic norms.     

Methods to Explore Uncertainty and Bias Introduced by Job Exposure Matrices

Job exposure matrices (JEMs) are used to measure exposures based on information about particular jobs and tasks. JEMs are especially useful when individual exposure data cannot be obtained. Nonetheless, there may be other workplace exposures associated with the study disease that are not measured in available JEMs. When these exposures are also associated with the exposures measured in the JEM, biases due to uncontrolled confounding will be introduced. Furthermore, individual exposures differ from JEM measurements due to differences in job conditions and worker practices. Uncertainty may also be present at the assessor level since exposure information for each job may be imprecise or incomplete. Assigning individuals a fixed exposure determined by the JEM ignores these uncertainty sources. We examine the uncertainty displayed by bias analyses in a study of occupational electric shocks, occupational magnetic fields, and amyotrophic lateral sclerosis.     

Consumer Behaviour and Order Fulfilment in Online Retailing: A Systematic Review

This paper provides a systematic review of consumer behaviour and order fulfilment in online retailing. The objective of this review is threefold: first, to identify elements of order‐fulfilment operations that are relevant to online consumer behaviour (purchase, repurchase, product return); second, to understand the relationship between order‐fulfilment performance and consumer behaviour; and third, to inspire future research on developing consumer service strategies that takes account of these behavioural responses to order‐fulfilment performance outcomes. The paper is based on a systematic review of literature on online consumer behaviour and order‐fulfilment operations, mainly in the fields of marketing and operations, published in international peer‐reviewed journals between 2000 and September 2015. This study indicates that the current literature on online consumer behaviour focuses mainly on the use of marketing tools to improve consumer service levels. Very little research has been conducted on the use of consumer service instruments to steer consumer behaviour or, consequently, to manage related order‐fulfilment activities better. The study culminates in a framework that encompasses elements of order‐fulfilment operations and their relationship to online consumer behaviour. This paper is the first comprehensive review of online consumer behaviour that takes aspects of order‐fulfilment operations into account from both marketing and operations perspectives.     

Building protest online: engagement with the digitally networked #not1more protest campaign on Twitter

This article examines engagement with digitally networked, politically contentious actions. Maintaining engagement over time is a key challenge for social movements attempting to network digitally. This article argues that proximity serves as a condition to address this challenge, because it configures the personal networks upon which transmission depends. This is a paradox of digital activism: it has the capacity to transcend barriers; however, proximity is essential for sustaining relations over time. Examining Twitter data from the #not1more protest campaign against immigrant deportations in the United States, quantitative and social network analyses show a differentiated development of engagement, which results in a particular geographical configuration with the following attributes. First, there is a robust and connected backbone of core organizers and activists located in particular major cities. Second, local groups engage with the campaign with direct actions in other cities. Third, a large and transitory contingent of geographically dispersed users direct attention to the campaign. We conclude by elaborating how this geographically differentiated configuration helps to sustain engagement with digitally networked action.     

Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.     

FACSGen: A Tool to Synthesize Emotional Facial Expressions Through Systematic Manipulation of Facial Action Units

To investigate the perception of emotional facial expressions, researchers rely on shared sets of photos or videos, most often generated by actor portrayals. The drawback of such standardized material is a lack of flexibility and controllability, as it does not allow the systematic parametric manipulation of specific features of facial expressions on the one hand, and of more general properties of the facial identity (age, ethnicity, gender) on the other. To remedy this problem, we developed FACSGen: a novel tool that allows the creation of realistic synthetic 3D facial stimuli, both static and dynamic, based on the Facial Action Coding System. FACSGen provides researchers with total control over facial action units, and corresponding informational cues in 3D synthetic faces. We present four studies validating both the software and the general methodology of systematically generating controlled facial expression patterns for stimulus presentation.