Challenges to professionalism and union voting intentions: The case of pharmacists

Using data from a national survey of pharmacists who are members of the American Pharmaceutical Association, we examine the union voting intentions of employee pharmacists. We find that union instrumentality regarding professionalism is a primary predictor of union voting intent among these employees. In addition, this predictor mediates the relationship between the level of professionalism at a pharmacist’s current employment situation and his or her expected union vote. Also important to union voting intent are respondent beliefs about union instrumentality regarding pecuniary issues, prior union experience, as well as overall job satisfaction. Implications for employers, unions, and researchers are drawn. We thank Mary Graham, Jann Skelton, Paul Swiercz, Terry Thomason, and participants at the Seventh Bar-gaining Group Conference at Michigan State University for their comments on earlier versions of this paper. This research was made possible by a grant from the American Pharmaceutical Association.     

Factors associated with abuse in residential child care institutions

Confidence in the public care system in the United Kingdom (UK) has been shaken by numerous and widespread scandals surrounding the abuse of children and young people, particularly those in residential child care institutions. This paper examines factors associated with such abuse, including: failings in relation to staff recruitment, training, and supervision; ineffective management and systems of accountability; the development of inappropriate institutional cultures; public ambivalence towards children in care; the slow footed response to the threat posed to children and young people by dangerous men and other youngsters in care; and the long‐term policy failure to develop coherent and integrated systems of child welfare in the UK. Copyright © 2002 John Wiley & Sons, Ltd.     

Rehabilitating the Criminality of Immigrants Under Section 19 of the Canadian Immigration Act1

Immigration has historically been associated with moral entrepreneurship and xenophobia. In periods of high unemployment and global dislocation, immigrants easily become the targets of political commentators who complain of their criminality, morals, demand on public services, and competition for scarce employment. In this exercise, looking at the recidivism of immigrants who come to Canada with a previous, foreign criminal history, quite a different picture emerges. Among this random sample (N=204), 97.5 percent of immigrants granted a rehabilitation waiver under the provisions of the Canadian Immigration Act were not re‐arrested in Canada within a period of about 3.5 years after their landing was approved by the Minister. Of those who were arrested, most of the delinquency was manageable and, in fact, resulted in either an acquittal, diversion or lower‐range sanctions. This is not the kind of imagery complained of by the tabloids or critics in the body politic. It behooves us, then, to exercise care in discussing crime and immigration, as it is a subject easily prone to the creation of “moral panics” and resulting repressive legislation against persons of color.     

Audience effects on solitary faces during imagery: Displaying to the people in your head

Subjects imagined situations in which they reported enjoying themselves either alone or with others. Electromyographic (EMG) activity was recorded bilaterally from regions overlying thezygomatic major muscles responsible for smiling. Controlling for equal rated happiness in the two conditions, subjects showed more smiling in high-sociality than low-sociality imagery. In confirming imaginary audience effects during imagery, these data corroborate hypotheses that solitary facial displays are mediated by the presence of imaginary interactants, and suggest caution in employing them as measures of felt emotion.Avery Gilbert and Amy Jaffey had compelling insights throughout the course of study. We thank Paul Ekman, Carroll Izard, and Paul Rozin for extensive comments on earlier drafts. We also thank Bernard Apfelbaum, Jon Baron, Janet Bavelas, John Cacioppo, Linda Camras, Dean Delis, Rob DeRubeis, Alan Fiske, Stephen Fowler, Greg McHugo, Harriet Oster, David Premack, W. John Smith, and David Williams for their valuable comments and suggestions.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

The effects of leadership change on team escalation of commitment

Although teams benefit from developing plans and processes that boost efficiency and reduce uncertainty, they may become too attached to these plans and escalate commitment when an alternative response is needed. Drawing on theories of team leadership, team processes and escalation of commitment, we propose that a change in leadership can help the team reduce commitment to outdated plans and avoid further escalation over time. Across two studies, we tested and found support for our hypotheses and provide evidence that leadership change can break the cycle of escalation by enhancing leader-driven team reflection and refocusing the team on error correction instead of additional investment. We discuss how the results of these studies extend existing theory and add to our understanding of the important role leaders play in enhancing team adaptation and preventing team escalation.     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.