Stigma and treatment of eating disorders in Ireland: healthcare professionals' knowledge and attitudes

Objectives: This study examines aspects of health professionals’ knowledge and attitudes about eating disorders (EDs) , which might impede the effective detection or treatment of EDs in Ireland. Methods: 1,916 health professionals were invited to participate in a web-based survey. Participants were randomly allocated to view one of five vignettes depicting a young person with symptoms consistent with Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder, Depression or Type 1 Diabetes. Study-specific questions examined participants’ responses to the vignettes and ED knowledge and experience

Eating disorder literacy and stigmatising attitudes towards anorexia, bulimia and binge eating disorder among adolescents

Little research has investigated adolescents’ understanding of eating disorders (EDs) or attitudes towards people affected by EDs. This impedes the development of targeted health promotion interventions. In the current study, 290 adolescents viewed a vignette depicting a target with either Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder, Depression or Type 1 Diabetes. Subsequent questionnaires assessed understanding of and attitudes towards the disorder described . Adolescents recognised the symptoms of depression significantly more frequently than any ED. Relative to depression and Type 1 diabetes, participants held targets with EDs more personally responsible for their illness and ascribed them more negative personality characteristics. The data revealed a particularly unfavourable view of Binge Eating Disorder, which was conceptualised as a failure of self-discipline rather than a medical condition. The results confirm previous findings that EDs are more stigmatised than other mental or physical health conditions and extend the findings to an adolescent cohort

Eating disorder services for young people in Ireland: perspectives of service providers, service users and the general adolescent population

Objectives: This paper illuminates how national eating disorder (ED) policy translates into day-to-day practice by exploring how ED services are experienced by those who deliver and use them. Methods: A mixed-methods approach was used, which combined qualitative and quantitative techniques. The paper collates data from three studies: (i) an interview study exploring the lived experiences of young people with EDs (n =8), their parents (n =5) and their healthcare professionals (n =3); (ii) a national survey of health professionals’ perspectives on existing ED services (n =171); (iii) a nationwide survey of secondary-school students’ eating concerns and patterns of help - seeking (n=290). Results: The qualitative interviews with young people and their parents revealed feelings of isolation and helplessness. Young people expressed interest in patient support groups, while parents desired greater support for the family unit. Parents were highly critical of available services, particularly in relation to access. These criticisms were echoed in the survey of healthcare professionals, who reported many barriers to delivering effective care. Clinicians were almost unanimous in calling for care pathways to be clarified via a standardised treatment protocol. The survey of adolescents indicated widespread reluctance to seek help regarding eating concerns: over one-third expressed concern about their own eating habits, but half of these had not divulged their concerns to anyone. Participants’ preferred pathways of help-seeking revolved around family and friends, and adolescents were unsure about routes of access to professional support. 3 Conclusions: The research demonstrates that many aspects of national ED policy have not been implemented in practice. The paper highlights specific gaps and suggests ways they can be redressed

Mixtures of Shifted Asymmetric Laplace Distributions

A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian distribution. This approach is mathematically elegant and relatively computationally straightforward. Our novel mixture modelling approach is demonstrated on both simulated and real data to illustrate clustering and classification applications. In these analyses, our mixture of shifted asymmetric Laplace distributions performs favourably when compared to the popular Gaussian approach. This work, which marks an important step in the non-Gaussian model-based clustering and classification direction, concludes with discussion as well as suggestions for future work

Low-Temperature Injury to Apples in the Champlain Valley, 1980-81

Some five-to-seven-year-old Mclntosh trees on various rootstocks were severely injured (loss of leaders or scaffold limbs) or killed during the winter of 1980-81. This injury appeared to be related to the December 1980 cold period and was more extensive because of the very cold period in early January. Some of these trees had grown too late into the fall and had not hardened sufficiently to withstand the abrupt temperature drop in December

Unsupervised Learning via Mixtures of Skewed Distributions with Hypercube Contours

Mixture models whose components have skewed hypercube contours are developed via a generalization of the multivariate shifted asymmetric Laplace density. Specifically, we develop mixtures of multiple scaled shifted asymmetric Laplace distributions. The component densities have two unique features: they include a multivariate weight function, and the marginal distributions are also asymmetric Laplace. We use these mixtures of multiple scaled shifted asymmetric Laplace distributions for clustering applications, but they could equally well be used in the supervised or semi-supervised paradigms. The expectation-maximization algorithm is used for parameter estimation and the Bayesian information criterion is used for model selection. Simulated and real data sets are used to illustrate the approach and, in some cases, to visualize the skewed hypercube structure of the components

Model-based clustering via linear cluster-weighted models

A novel family of twelve mixture models with random covariates, nested in the linear $t$ cluster-weighted model (CWM), is introduced for model-based clustering. The linear $t$ CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Maximum likelihood parameter estimation is carried out within the EM framework, and both the BIC and the ICL are used for model selection. A simple and effective hierarchical random initialization is also proposed for the EM algorithm. The novel model-based clustering technique is illustrated in some applications to real data. Finally, a simulation study for evaluating the performance of the BIC and the ICL is presented

Parsimonious Shifted Asymmetric Laplace Mixtures

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An explicit two-stage parameter estimation procedure is described, and the Bayesian information criterion and the integrated completed likelihood are compared for model selection. This novel family of models is applied to real data, where it is compared to its Gaussian analogue within clustering and classification paradigms

Evaluating the articulation of programme theory in practice as observed in Quality Improvement initiatives

Background: The Action-Effect Method(AEM) was co-developed by NIHR CLAHRC Northwest London (CLAHRC NWL) researchers and QI practitioners, building on Driver Diagrams(DD). This study aimed to determine AEM effectiveness in terms of technical aspects (how diagrams produced in practice compared with theoretical ideals) and social aspects (how engagement with the method related to social benefits). Methods Diagrams were scored on criteria developed on theoretical ideals of programme theory. 65 programme theory diagrams were reviewed (21 published Driver Diagrams (External DDs), 22 CLAHRC NWL Driver Diagrams (Internal DDs), and 21 CLAHRC NWL Action-Effect Diagrams(AEDs)). Social functions were studied through ethnographic observation of frontline QI teams in AEM sessions facilitated by QI experts. Qualitative analysis used inductive and deductive coding. Results ANOVA indicated the AEM significantly improved the quality of programme theory diagrams over Internal and External DDs on an average of 5 criteria from an 8-point assessment. Articulated aims were more likely to be patient-focused and high-level in AEDs than DDs. The cause/effect relationships from intervention to overall aim also tended to be clearer and were more likely than DDs to contain appropriate measure concepts. Using the AEM also served several social functions such as facilitating dialogue among multidisciplinary teams, and encouraging teams to act scientifically and pragmatically about planning and measuring QI interventions. Implications: The Action-Effect Method developed by CLAHRC NWL resulted in improvements over Driver Diagrams in articulating programme theory, which has wide-ranging benefits to quality improvement, including encouraging broad multi-disciplinary buy-in to clear aims and pre-planning a rigorous evaluation strategy