The perception of support received from breast care nurses by depressed patients following a diagnosis of breast cancer

INTRODUCTION Due to their specialist training, breast care nurses (BCNs) should be able to detect emotional distress and offer support to breast cancer patients. However, patients who are most distressed after diagnosis generally experience least support from care staff. To test whether BCNs overcome this potential barrier, we compared the support experienced by depressed and non-depressed patients from their BCNs and the other main professionals involved in their care: surgeons and ward nurses. PATIENTS AND METHODS Women with primary breast cancer (n = 355) 2–4 days after mastectomy or wide local excision, self-reported perceived professional support and current depression. Analysis of variance compared support ratings of depressed and non-depressed patients across staff types. RESULTS There was evidence of depression in 31 (9%) patients. Depressed patients recorded less surgeon and ward nurse support than those who were not depressed but the support received by patients from the BCN was high, whether or not patients were depressed. CONCLUSIONS BCNs were able to provide as much support to depressed patients as to non-depressed patients, whereas depressed patients felt less supported by surgeons and ward nurses than did non-depressed patients. Future research should examine the basis of BCNs' ability to overcome barriers to support in depressed patients. Our findings confirm the importance of maintaining the special role of the BCN.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Protocol for ACCESS: a qualitative study exploring barriers and facilitators to accessing the emergency contraceptive pill from community pharmacies in Australia

Introduction The rate of unplanned pregnancy in Australia remains high, which has contributed to Australia having one of the highest abortion rates of developed countries with an estimated 1 in 5 women having an abortion. The emergency contraceptive pill (ECP) offers a safe way of preventing unintended pregnancy after unprotected sex has occurred. While the ECP has been available over-the-counter in Australian pharmacies for over a decade, its use has not significantly increased. This paper presents a protocol for a qualitative study that aims to identify the barriers and facilitators to accessing the ECP from community pharmacies in Australia. Methods and analysis Data will be collected through one-on-one interviews that are semistructured and in-depth. Partnerships have been established with 2 pharmacy groups and 2 women's health organisations to aid with the recruitment of women and pharmacists for data collection purposes. Interview questions explore domains from the Theoretical Domains Framework in order to assess the factors aiding and/or hindering access to ECP from community pharmacies. Data collected will be analysed using deductive content analysis. The expected benefits of this study are that it will help develop evidence-based workforce interventions to strengthen the capacity and performance of community pharmacists as key ECP providers. Ethics and dissemination The findings will be disseminated to the research team and study partners, who will brainstorm ideas for interventions that would address barriers and facilitators to access identified from the interviews. Dissemination will also occur through presentations and peer-reviewed publications and the study participants will receive an executive summary of the findings. The study has been evaluated and approved by the Monash Human Research Ethics Committee.

Mobile sink based fault diagnosis scheme for wireless sensor networks

Network diagnosis in Wireless Sensor Networks (WSNs) is a difficult task due to their improvisational nature, invisibility of internal running status, and particularly since the network structure can frequently change due to link failure. To solve this problem, we propose a Mobile Sink (MS) based distributed fault diagnosis algorithm for WSNs. An MS, or mobile fault detector is usually a mobile robot or vehicle equipped with a wireless transceiver that performs the task of a mobile base station while also diagnosing the hardware and software status of deployed network sensors. Our MS mobile fault detector moves through the network area polling each static sensor node to diagnose the hardware and software status of nearby sensor nodes using only single hop communication. Therefore, the fault detection accuracy and functionality of the network is significantly increased. In order to maintain an excellent Quality of Service (QoS), we employ an optimal fault diagnosis tour planning algorithm. In addition to saving energy and time, the tour planning algorithm excludes faulty sensor nodes from the next diagnosis tour. We demonstrate the effectiveness of the proposed algorithms through simulation and real life experimental results.