Citizenship and Structured Dependency: the implications of policy design for senior political power

This paper argues that the structured dependency thesis must be extended to incorporate political power. It outlines a political framework of analysis with which to identify who gains and who loses from social policy. I argue that public policy for older people is a product not only of social structures but also of political decision-making. The Schneider and Ingram (1993) ‘ target populations’ model is used to investigate how the social construction of groups as dependent equates with lower levels of influence on policy making. In United Kingdom and European research, older people are identified as politically quiescent, but conversely in the United States seniors are viewed as one of the most influential and cohesive interest groups in the political culture. Why are American seniors perceived as politically powerful, while older people in Europe are viewed as dependent and politically weak? This paper applies the ‘target populations’ model to senior policy in the Republic of Ireland to investigate how theoretical work in the United States may be used to identify the significance of senior power in policy development. I conclude that research must recognise the connections between power, politics and social constructions to investigate how state policies can influence the likelihood that seniors will resist structured dependency using political means.

The Engine of Thought Is a Hybrid: Roles of Associative and Structured Knowledge in Reasoning

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.

The student experience in Objective Structured Clinical Examinations (OSCEs): phenomenological analysis

Background and purpose
The dominant psychometric discourse of OSCEs may lead to unexpected problems, such as a checklist-based student performance1 which under emphasises the clinical relationship with student and standardised patient (SP). Such encounters can be dehumanising for SPs2 and have implications for what students learn about relational skills through the assessment process. In this study we explore medical students’ experiences of undertaking OSCEs using a phenomenological frame.
Methodology
Interpretative phenomenological analysis is a form of qualitative methodology which has strong resonance with existentialism and focuses on the lived experience without significant reference to external political or discursive
forces.
Six 4th year undergraduate medical students from Queen’s University Belfast were recruited in December 2013. Maximum variation sampling was used. Students were interviewed by a researcher in the week prior to the
OSCE and then again in the week following the OSCE in Jan 2014. Interviews were minimally structured in order to be open to respondents, rather than adhering to a fixed topic guide, but focussed on participants’ experiences, thoughts and feelings about taking part in OSCEs. Interviews were audio-recorded and
transcribed. Students were also asked to complete a short diary entry in the days prior to the OSCEs and another immediately following. Diary entries were written, emailed or audio-recorded at student’s preference.
Results
Transcripts are currently being analysed by interpretative phenomenological analysis. Preliminary analysis has demonstrated the significance of students’ relationships within the OSCE triad (student, SP and examiner); the effect of the immediate examination environment; realism versus roleplay; students’ perceptions of the purpose of assessment; and coping mechanisms.
Full results will be available by the time of the conference.
Conclusion and Discussion
Understanding the student experience in OSCEs is a crucial step in understanding the complex construction of relationships within the OSCE triad. The focus in OSCEs is typically on standardisation and reliability, but in exploring social interactions we may refocus attention on their inherent potential for learning and effects on both students and patients.
References
1. Hodges B. Medical education and the maintenance of incompetence. Med Teach 2006;28(8):690-6
2. Johnston JL, Lundy G, McCullough M, Gormley GJ. The view from over there: reframing the OSCE through the experience of standardised patient
raters. Med Educ 2013;47(9):899-909

Uncovering the challenges of managing cachexia in advanced cancer: Preliminary results from semi-structured interviews with healthcare professionals in a regional cancer centre

Background: Cancer cachexia is a complex metabolic syndrome characterised by severe and progressive weight loss which is predominantly muscle mass. It is a devastating and distressing complication of advanced cancer with profound bio-psycho-social implications for patients and their families. At present there is no curative treatment for cachexiain advanced cancer therefore the most important healthcare response entails the minimisation of the psycho-social distress associated with it. However the literature suggests healthcare professionals’are missing opportunities to intervene and respond to the multi-dimensional needs of this population.

Objective:The objective of this study was to explore healthcare professionals’ response to cachexia in advanced cancer.

Methods: An interpretative qualitative approach was adopted in this study. A purposive sample of doctors, nurses, specialist nurses and dieticians were recruited from a regional cancer centre between November 2009 and November 2010. Data was collection was twofold: two multi-professional focus groups were conducted first to uncover the main themes and issues in cachexia management. This data then informed the interview schedule for the following 25 individual semi-structured interviews.

Results: Preliminary data analysis of the semi-structured interviews revealed distinct differences between disciplines in their perceptions of cancer cachexia which influenced their response to it in clinical practice. The commonality between disciplines, with the exception of palliative care, was a reliance on the biomedical approach to cancer cachexia management.

Discussion and Conclusions: Cancer cachexia is a complex and challenging syndrome which needs to be addressed from a holistic model of care to reflect the multi-dimensional needs of this patient group. The perspectives of those involved in care delivery is required in order to inform the development of interventions aimed at minimising the distress associated with this devastating syndrome.

Automatically Annotating Structured Web Data Using a SVM-Based Multiclass Classifier

In this paper, we propose a new learning approach to Web data annotation, where a support vector machine-based multiclass classifier is trained to assign labels to data items. For data record extraction, a data section re-segmentation algorithm based on visual and content features is introduced to improve the performance of Web data record extraction. We have implemented the proposed approach and tested it with a large set of Web query result pages in different domains. Our experimental results show that our proposed approach is highly effective and efficient.