‘It's afforded us a huge flexibility’: the impact of ‘Disabled Children's Access to Childcare Pilots’ on families with a disabled child in England

For many parents of disabled children, finding affordable and suitable childcare that meets their needs is very challenging. Research in the UK has shown that parents with disabled children experience barriers not just with cost, but also accessibility, the attitudes of childcare providers and in getting good information about sources of childcare. The Disabled Children's Access to Childcare (DCATCH) pilots was an initiative designed to improve access to childcare for disabled children in England. As part of an evaluation of DCATCH, qualitative interviews were carried out with 38 members of 22 families who had received support from the initiative. All of the families reported positive outcomes and highlighted: a beneficial impact on the parents' capacity to work; enjoyable experiences for the disabled child; increased confidence and independence for both parents and children; and the creation of time for parents to pursue other activities. The results of the DCATCH initiative support other research which argues that whilst cost is a significant factor around childcare choices for families with a disabled child, having confidence in the childcare provider's ability to meet specific needs (including complex health care needs) and providing positive experiences for the disabled child are also key, determining factors.     

Graphical and Numerical Methods for Detecting Influential Observations in Complex Bingham Data

We describe methods to detect influential observations in a sample of pre-shapes when the underlying distribution is assumed to be complex Bingham. One of these methods is based on Cook's distance, which is derived from the likelihood of the complex Bingham distribution. Other method is related to the tangent space, which is based on the local influence for the multivariate normal distribution. A method to detect outliers is also explained. The application of the methods is illustrated in both a real dataset and a simulated sample.     

Asymptotic relative efficiency of wald tests in measurement error models

In this paper, asymptotic relative efficiency (ARE) of Wald tests for the Tweedie class of models with log-linear mean, is considered when the aux¬iliary variable is measured with error. Wald test statistics based on the naive maximum likelihood estimator and on a consistent estimator which is obtained by using Nakarnura's (1990) corrected score function approach are defined. As shown analytically, the Wald statistics based on the naive and corrected score function estimators are asymptotically equivalents in terms of ARE. On the other hand, the asymptotic relative efficiency of the naive and corrected Wald statistic with respect to the Wald statistic based on the true covariate equals to the square of the correlation between the unobserved and the observed co-variate. A small scale numerical Monte Carlo study and an example illustrate the small sample size situation.     

Well balanced: 8 steps to wellness for adults with mental illness and diabetes

Serious mental illness places a tremendous burden on clients, their families, and behavioral and medical health care providers. The co-occurrence of diabetes with mental illness may further compromise daily functioning. Psychiatric nurses can make a significant difference in improving the health and medical outcomes of this client population. A partnership was developed between the University of Rochester School of Nursing and the Western New York Care Coordination Program to evaluate a novel nursing model for adults with both serious mental illness and diabetes mellitus. The Well Balanced program incorporated health promotion, disease management, nursing care management, and evidence-based practice guidelines into 8 Steps to Wellness for a community-based mental health population. During a 16-visit intervention period, psychiatric nurses interacted with 74 clients. As a result of the program, clients experienced improvements in health risk status and in their hemoglobin A1C and reported high satisfaction with the Well Balanced program.     

Examining the correlates of engagement and disengagement coping among help-seeking battered women

This study examined several potential correlates of engagement and disengagement coping, including abuse-related factors, socioeconomic and social coping resources, and childhood trauma variables among a sample of battered women (N = 388). Relationship abuse frequency, particularly psychological aggression, and peritraumatic dissociation were the strongest positive predictors of the use of disengagement coping. Social coping resources, including tangible support and appraisals of social support and belonging, were associated with higher engagement coping and lower disengagement coping. A positive association was also found between interparental domestic violence and disengagement coping, and negative associations were found between both childhood physical and sexual abuse and engagement coping. Results suggest that coping strategies used by battered women are multidetermined and deserve further exploration.     

The production of therapy:the social process of construction of the mother of a sexually abused child

This study qualitatively examined the perspectives of clinical social workers on non-offending mothers of sexually abused children. The study examined whether clinicians still used collusion to explain mothers' behavior, despite research refuting collusion. Findings revealed that, although workers did not use collusion, they still constructed mothers negatively. Multiple contexts of agency practice influenced constructions. Administrative use of authority to implement external constraints led to workers' resistance, which involved humor with gender and ethnic components. The agency's role as a graduate social work teaching site contributed the following: Field instructors transmitted the belief that incest typified severe family difficulties and posed complex assessment and intervention problems. Implications for effective practice are discussed.     

Design of ISD-1: an instrument for social diagnosis in care homes for older persons*

This article describes the design process and main features of an instrument developed for use in the specialist area of intervention in care homes for older persons. The essential aim of ISD-1 (instrument for social diagnosis) is to permit the correct formulation of social diagnoses and to standardise and define the professional language used by social workers. Its content has been organised into 4 dimensions of social diagnosis, divided into 15 sub-dimensions containing 83 diagnostic categories. This work was performed in Spain, in the 24 care homes of the Madrid Social Care Agency of the Community of Madrid, involving the participation of the 40 social workers practising in these centres. ISD-1 is an easily understood and used tool, of potential use for social workers practising in care homes for older persons and capable of being adapted for use in other institutional environments, as well as being capable of adaptation and translation for its application in other countries.     

Empirical evaluation of the implementation of the EMA guideline on missing data in confirmatory clinical trials: Specification of mixed models for longitudinal data in study protocols

In confirmatory clinical trials, the prespecification of the primary analysis model is a universally accepted scientific principle to allow strict control of the type I error. Consequently, both the ICH E9 guideline and the European Medicines Agency (EMA) guideline on missing data in confirmatory clinical trials require that the primary analysis model is defined unambiguously. This requirement applies to mixed models for longitudinal data handling missing data implicitly. To evaluate the compliance with the EMA guideline, we evaluated the model specifications in those clinical study protocols from development phases II and III submitted between 2015 and 2018 to the Ethics Committee at Hannover Medical School under the German Medicinal Products Act, which planned to use a mixed model for longitudinal data in the confirmatory testing strategy. Overall, 39 trials from different types of sponsors and a wide range of therapeutic areas were evaluated. While nearly all protocols specify the fixed and random effects of the analysis model (95%), only 77% give the structure of the covariance matrix used for modeling the repeated measurements. Moreover, the testing method (36%), the estimation method (28%), the computation method (3%), and the fallback strategy (18%) are given by less than half the study protocols. Subgroup analyses indicate that these findings are universal and not specific to clinical trial phases or size of company. Altogether, our results show that guideline compliance is to various degrees poor and consequently, strict type I error rate control at the intended level is not guaranteed.     

Celebrating Joan Acker: Sociologist,fearless feminist,friend

Joan Acker's life reflects a time when middle‐class women were expected to be satisfied with maintaining the home front, serving husbands and children, not having paid‐work careers. After living “the ideal” for 37 years, Acker took a new path by earning a Ph. D. and producing path‐breaking scholarship that challenged taken‐for‐granted beliefs about gender, family, work, and organizations. Acker spoke “truth to power” and was an academic heroine in posing feminist challenges to injustices involving gender, social class, and race/ethnicity, particularly (but not solely) related to the workplace. This overview lets Joan tell her story and offers reflections on her milestone publications as seen by Pat Martin.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.