Child Protection and Radiography: Clinical and Technical Issues

The role of the radiographer in child protection has received little attention. This review considers issues which arise in this field, highlighting the implications for those responsible for the management of child protection systems, for those responsible for training, and for staff in other specialisms and professions within the hospital. The different types of imaging technique are discussed with some comment on their advantages and disadvantages. Forced immobilisation is sometimes required, and consideration is given to the issues which arise for the radiographer and abused child in this process. The article reviews the knowledge needed by radiographers of the clinical and radiological manifestations of abuse. It is noted that there are additional social and emotional issues which are discussed in a further paper. © 1997 John Wiley & Sons, Ltd.     

Linear Regression Limit Theory for Nonstationary Panel Data

This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section (n) and time series (T) observations. The limit theory allows for both sequential limits, wherein T→∞ followed by n→∞, and joint limits where T, n→∞ simultaneously; and the relationship between these multidimensional limits is explored. The panel structures considered allow for no time series cointegration, heterogeneous cointegration, homogeneous cointegration, and near-homogeneous cointegration. The paper explores the existence of long-run average relations between integrated panel vectors when there is no individual time series cointegration and when there is heterogeneous cointegration. These relations are parameterized in terms of the matrix regression coefficient of the long-run average covariance matrix. In the case of homogeneous and near homogeneous cointegrating panels, a panel fully modified regression estimator is developed and studied. The limit theory enables us to test hypotheses about the long run average parameters both within and between subgroups of the full population.     

Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error

In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.     

Permutational Multiple Testing Adjustments With Multivariate Multiple Group Data

We consider the multiple comparison problem where multiple outcomes are each compared among several different collections of groups in a multiple group setting. In this case there are several different types of hypotheses, with each specifying equality of the distributions of a single outcome over a different collection of groups. Each type of hypothesis requires a different permutational approach. We show that under a certain multivariate condition it is possible to use closure over all hypotheses, although intersection hypotheses are tested using Boole's inequality in conjunction with permutation distributions in some cases. Shortcut tests are then found so that the resulting testing procedure is easily performed. The error rate and power of the new method is compared to existing competitors through simulation of correlated data. An example is analyzed, consisting of multiple adverse events in a clinical trial.     

Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies

In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means.     

A Demonstration of Methods for Studying Negotiations Between Physicians and Health Care Managers*

Decision analysis was used to study negotiations in the health care context. This paper found that analytical methods could answer several important questions related to complex negotiations, including whether contracts promote the interest of both parties, whether a decision aid could better meet the priorities of both parties, and whether one negotiator is more successful than the other in repeated negotiations. The paper concluded that micro-health care negotiations can be traced and studied with existing mathematical theories of negotiation.     

How Believing in Ourselves Increases Risk Taking: Perceived Self-Efficacy and Opportunity Recognition

What effect does positive and negative feedback about past risk taking have on the future risk taking of decision makers? The results of an experimental study show that subjects who are led to believe they are very competent at decision making see more opportunities in a risky choice and take more risks. Those who are led to believe they are not very competent see more threats and take fewer risks. The feelings of self-competence and self-confidence on one task did not generalize to a similar task. Perception of opportunities was unexpectedly not related to the perception of threats. As executives bring their personal perceptual biases to firm decision making, our results identify a serious built-in bias in SWOT analysis (the analysis of firms' strengths and weaknesses as related to potential opportunities and threats). Executives who believe that they and their firm are very competent will take more risks and vice versa. Our results also provide evidence that the perceived likelihood of an event depends on whether the event is a loss or a gain. Human decision making is subject to the general bias that outcome expectations are not independent of outcome valuations.     

Planning Service Capacity When Demand Is Sensitive to Delay

This paper presents a model designed to help determine the number of clerks or servers in a retail queuing situation. The model demonstrates a realistic treatment of the effect of waiting time on customer demand and revenues by treating the average waiting time as a form of price. Thus, the demand rate and service level are interrelated. The number of servers is obtained by balancing the cost of additional servers against the revenues and profits arising from greater demand. An example is shown, using data obtained from a supermarket, to demonstrate the insight gained from applying the model. Some extensions and research possibilities are considered, including a treatment of ancillary activities. An appendix examines a continuous version of the model which provides additional insight into the character of the model.     

Anti-homosexual prejudice . . . as opposed to what? Queer theory and the social psychology of anti-homosexual attitudes

This article uses Sedgwick's distinction between minoritizing and universalizing theories of sexuality to analyze variability in social psychologists' studies of anti-homosexual prejudice, focusing on studies of attitudes. Anti-homosexual prejudice was initially defined in conversation with gay liberationists and presumed, among other things, that fear of homoerotic potential was present in all persons. Later social psychologists theorized anti-homosexual prejudice in strict minoritizing terms: as prejudice towards a distinct out-group. In the first section of this paper we discuss corresponding shifts in the conceptualization of anti-homosexual attitudes. Next, using a universalizing framework, we re-interpret experiments on behavioral aspects of anti-homosexual attitudes which were originally conceptualized using a minoritizing framework, and suggest avenues for future research. Finally, we examine how queer theory might enrich this area of social psychological inquiry by challenging assumptions about the politics of doing scientific work and the utility of identity-based sexual politics.     

Can one TV show make a difference? Will & Grace and the Parasocial Contact Hypothesis

Television has an opportunity to influence beliefs about groups with which individuals typically may have little direct social contact. This study describes a synthesis of the Contact Hypothesis and the concept of Parasocial Interaction to pose what we call the Parasocial Contact Hypothesis to test whether exposure to gay men on Will & Grace can influence attitudes toward gay men in general. Based on a study of 245 university students, this study examines the relationships among number and intimacy of gay social contacts, parasocial interaction, viewing frequency of Will & Grace, and scores on Herek's Attitudes Toward Gay Men and Lesbians scale. Increased viewing frequency and parasocial interaction were found to correlate with lower levels of sexual prejudice-a relationship that was most pronounced for those with the least amount of social contact with lesbians and gay men.