Nurses and personal attendants' diabetes knowledge : in regional public residential care

Aim: Identify staff knowledge about diabetes medicines and organisational factors that influence safe medicines use in two large Australian regional public RACs that comply with national accreditation standards.

Background: Diabetes management is complicated in residential aged care facilities (RAC). Managing medicines is complex, especially in older people. Little is known about diabetes-specific medicine knowledge of various care staff (registered nurses (RN), enrolled nurses (EN) and patient care attendants (PCA) working in RAC.

Methods: A triangulation of methods was used to collect the data: anonymous self-complete questionnaire (ADKnowl) staff interviews to clarify practice issues that could affect safe medicine use, and a case file audit to identify medicine-related data. Questionnaires were distributed to all RNs, ENs and PCAs in the two services via nursing management (N=540). The ADKnowl was supplemented with additional questions and vignettes derived from actual case notes in each RAC to assess translation of knowledge into practice. Only medicine related data are reported.

Results: Sixty-eight people returned completed questionnaires (12.5% response rate). Knowledge deficits were identified in administering oral hypoglycaemic agents and insulin, their action and potential adverse events. Most ENs and PCAs did not know why HbA1c was measured. Almost half the RNs and ENs and 80% of PCAs did not know how diabetes comorbidities affect medicine choices. RN achieved higher overall average knowledge scores,74.3%, compared to ENs and PCA, 49%. The interviews suggest lack of time, unclear communication processes, inadequate knowledge about medications and resident behaviour compromises optimal medicine administration. Twenty case files audits were undertaken in each RAC and revealed residents were taking on average nine medicines.

Conclusion: Staff involved in caring for residents with diabetes had suboptimal general and medicine-specific diabetes knowledge to deliver optimal care. System issues and unpredictable resident behaviours made medicine management difficult and compromised safety.

Genetic architecture of sexually selected traits - lessons from guppies

I discuss a number of experiments designed to explore the genetic basis of sexual ornamentation and associated life history traits in guppies. This system is unusual because of the large Y-linked contribution to ornamentation and male fitness, and because the Y-chromosome is implicated in inbreeding depression. I discuss the implications of intralocus sexual conflict for the evolution of sex-linkage in guppies and the potential for such a process to drive the evolutionary erosion of Y-chromosomes.

Further investigation of the psychometric properties of the Insulin Treatment Appraisal Scale among insulin-using and non-insulin-using adults with type 2 diabetes : results from Diabetes MILES-Australia

Negative attitudes towards insulin are commonly reported by people with type 2 diabetes mellitus (T2DM) and can act as a barrier to timely insulin initiation. The Insulin Treatment Appraisal Scale (ITAS) is a widely used 20-item measure of attitudes towards insulin. While designed for completion by both insulin using and non-insulin using adults with T2DM, its psychometric properties have not been investigated separately for these groups. Furthermore, the total score is routinely reported in preference to the published two-factor structure (negative/positive appraisals). Further psychometric validation of the ITAS is required to examine its properties.

A predictive framework for modeling healthcare data with evolving clinical interventions

Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.