Factors influencing the provision of end-of-life care in critical care settings : development and testing of a survey instrument

Aim. To develop and psychometrically test a survey instrument to identify the factors influencing the provision of end-of-life care by critical care nurses. Background. Following a decision to withdraw life-sustaining treatment, critical care nurses remain with the patient and their family providing end-of-life care. Identification of factors influencing the provision of this care can give evidence to inform practice development and support nurses. Design. A cross-sectional survey of critical care nurses. Method. An online survey was developed, reviewed by an expert panel and pilot tested to obtain preliminary evidence of its reliability and validity. In May 2011, a convenience sample of critical care nurses (n = 392, response rate 25%) completed the survey. The analytical approach to data obtained from the 58 items measured on a Likert scale included exploratory factor analysis and descriptive statistics. Results. Exploratory factor analysis identified eight factors influencing the provision of end-of-life care: emotional support for nurses, palliative values, patient and family preferences, resources, organizational support, care planning, knowledge and preparedness. Internal consistency of each latent construct was deemed satisfactory. The results of descriptive statistics revealed a strong commitment to the inclusion of families in end-of-life care and the value of this care in the critical care setting. Conclusion. This paper reports preliminary evidence of the psychometric properties of a new survey instrument. The findings may inform practice development opportunities to support critical care nurses in the provision of endof- life care and improve the care that patients and their families receive.

Low probability differentials and the cryptanalysis of full-round CLEFIA-128

So far, low probability differentials for the key schedule of block ciphers have been used as a straightforward proof of security against related-key differential analysis. To achieve resistance, it is believed that for cipher with k-bit key it suffices the upper bound on the probability to be 2− k . Surprisingly, we show that this reasonable assumption is incorrect, and the probability should be (much) lower than 2− k . Our counter example is a related-key differential analysis of the well established block cipher CLEFIA-128. We show that although the key schedule of CLEFIA-128 prevents differentials with a probability higher than 2− 128, the linear part of the key schedule that produces the round keys, and the Feistel structure of the cipher, allow to exploit particularly chosen differentials with a probability as low as 2− 128. CLEFIA-128 has 214 such differentials, which translate to 214 pairs of weak keys. The probability of each differential is too low, but the weak keys have a special structure which allows with a divide-and-conquer approach to gain an advantage of 27 over generic analysis. We exploit the advantage and give a membership test for the weak-key class and provide analysis of the hashing modes. The proposed analysis has been tested with computer experiments on small-scale variants of CLEFIA-128. Our results do not threaten the practical use of CLEFIA.

Text mining for screening efficiency? Testing within a Cochrane public health review

Background The requirement for dual screening of titles and abstracts to select papers to examine in full text can create a huge workload, not least when the topic is complex and a broad search strategy is required, resulting in a large number of results. An automated system to reduce this burden, while still assuring high accuracy, has the potential to provide huge efficiency savings within the review process. Objectives To undertake a direct comparison of manual screening with a semi‐automated process (priority screening) using a machine classifier. The research is being carried out as part of the current update of a population‐level public health review. Methods Authors have hand selected studies for the review update, in duplicate, using the standard Cochrane Handbook methodology. A retrospective analysis, simulating a quasi‐‘active learning’ process (whereby a classifier is repeatedly trained based on ‘manually’ labelled data) will be completed, using different starting parameters. Tests will be carried out to see how far different training sets, and the size of the training set, affect the classification performance; i.e. what percentage of papers would need to be manually screened to locate 100% of those papers included as a result of the traditional manual method. Results From a search retrieval set of 9555 papers, authors excluded 9494 papers at title/abstract and 52 at full text, leaving 9 papers for inclusion in the review update. The ability of the machine classifier to reduce the percentage of papers that need to be manually screened to identify all the included studies, under different training conditions, will be reported. Conclusions The findings of this study will be presented along with an estimate of any efficiency gains for the author team if the screening process can be semi‐automated using text mining methodology, along with a discussion of the implications for text mining in screening papers within complex health reviews.

Knowledge unlatched: A global library consortium model for funding open access scholarly books: Full report on the Proof of Concept Pilot 2014

This special issue of Cultural Science Journal is devoted to the report of a groundbreaking experiment in re-coordinating global markets for specialist scholarly books and enabling the knowledge commons: the Knowledge Unlatched proof-of-concept pilot. The pilot took place between January 2012 and September 2014. It involved libraries, publishers, authors, readers and research funders in the process of developing and testing a global library consortium model for supporting Open Access books. The experiment established that authors, librarians, publishers and research funding agencies can work together in powerful new ways to enable open access; that doing so is cost effective; and that a global library consortium model has the potential dramatically to widen access to the knowledge and ideas contained in book-length scholarly works.

Testing and modifying a tool to measure nurses’ beliefs about aromatherapy

Aromatherapy has been found to have some effectiveness in treating conditions such as postoperative nausea and vomiting, however unless clinicians are aware of and convinced by this evidence, it is unlikely they will choose to use it with their patients. The aim of this study was to test and modify an existing tool, Martin and Furnham’s Beliefs About Aromatherapy Scale in order to make it relevant and meaningful for use with a population of nurses and midwives working in an acute hospital setting. A Delphi process was used to modify the tool and then it was tested in a population of nurses and midwives, then exploratory factor analysis was conducted. The modified tool is reliable and valid for measuring beliefs about aromatherapy in this population.

Large scale monitoring of crowds and building utilisation: A new database and distributed approach

Public buildings and large infrastructure are typically monitored by tens or hundreds of cameras, all capturing different physical spaces and observing different types of interactions and behaviours. However to date, in large part due to limited data availability, crowd monitoring and operational surveillance research has focused on single camera scenarios which are not representative of real-world applications. In this paper we present a new, publicly available database for large scale crowd surveillance. Footage from 12 cameras for a full work day covering the main floor of a busy university campus building, including an internal and external foyer, elevator foyers, and the main external approach are provided; alongside annotation for crowd counting (single or multi-camera) and pedestrian flow analysis for 10 and 6 sites respectively. We describe how this large dataset can be used to perform distributed monitoring of building utilisation, and demonstrate the potential of this dataset to understand and learn the relationship between different areas of a building.

The Piper Fatigue Scale-Revised: Translation and psychometric evaluation in Spanish-speaking breast cancer survivors

Background Cancer-related fatigue (CRF) is the most common and distressing symptom reported by breast cancer survivors. The primary aim of this study was to translate and evaluate psychometrically for the first time a Spanish version of the Piper Fatigue Scale-Revised (S-PFS-R). Methods One hundred and eleven women with stage I–IIIA breast cancer who had completed their primary cancer therapy in the previous 6 months with the exception of hormone therapy completed the S-PFS-R, the Profile of Mood States (POMS) Fatigue (POMS-F) and Vigor subscales (POMS-V), and bilateral force handgrip testing. Data analysis included test–retest reliability, construct validity, criterion-related validity, and exploratory factor analyses. Results Test–retest reliability was satisfactory (r > 0.86), and all subscales showed moderate to high construct validity estimates [corrected item-subscale correlations (Pearson r = ≥ 0.65)]. The exploratory factor analysis revealed four dimensions with 75.5 % of the common variance explained. The S-PFS-R total score positively correlated with the POMS-F subscale (r = 0.50–0.78) and negatively with the POMS-V subscale (r = −0.13 to −0.44) confirming criterion-related validity. Negative correlations among force handgrip testing, subscales, and total scores were weak (r = −0.26 to −0.29). Conclusions The Spanish version of PFS-R shows satisfactory psychometric properties in a sample of breast cancer survivors. This is the first study to translate the PFS-R into Spanish and further testing is warranted.

Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator

The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.

Who pays for standardised testing? A cost-benefit study of mandated testing in three Queensland secondary schools

This paper reports on an Australian study that explored the costs and benefits of the National Assessment Programme, Literacy and Numeracy (NAPLAN) testing, both tangible and intangible, of Year 9 students in three Queensland schools. The study commenced with a review of pertinent studies and other related material about standardised testing in Australia, the USA and UK. Information about NAPLAN testing and reporting, and the pedagogical impacts of standardised testing were identified, however little about administrative costs to schools was found. A social constructivist perspective and a multiple case study approach were used to explore the actions of school managers and teachers in three Brisbane secondary schools. The study found that the costs of NAPLAN testing to schools fell into two categories: preparation of students for the testing; and administration of the tests. Whilst many of the costs could not be quantified, they were substantial and varied according to the education sector in which the school operated. The benefits to schools of NAPLAN testing were found to be limited. The findings have implications for governments, curriculum authorities and schools, leading to the conclusion that, from a school perspective, the benefits of NAPLAN testing do not justify the costs.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

Random Breath Testing Operation and Effectiveness in 1985

Random breath testing (RBT) was introduced in South Australia in 1981 with the intention of reducing the incidence of accidents involving alcohol. In April 1985, a Select Committee of the Upper House which had been established to “review the operation of random breath testing in this State and any other associated matters and report accordingly” presented its report. After consideration of this report, the Government introduced extensive amendments to those sections of the Motor Vehicles Act (MVA) and Road Traffic Act (RTA) which deal with RBT and drink driving penalties. The amended section 47da of the RTA requires that: “(5) The Minister shall cause a report to be prepared within three months after the end of each calendar year on the operation and effectiveness of this section and related sections during that calendar year. (6) The Minister shall, within 12 sitting days after receipt of a report under subsection (5), cause copies of the report to be laid before each House of Parliament.” This is the first such report. Whilst it deals with RBT over a full year, the changed procedures and improved flexibility allowed by the revision to the RTA were only introduced late in 1985 and then only to the extent that the existing resources would allow.

The development and preliminary testing of an instrument for assessing fatigue self-management outcomes in patients with advanced cancer

Background: Fatigue is one of the most distressing and commonly experienced symptoms in patients with advanced cancer. Although the self-management (SM) of cancer-related symptoms has received increasing attention, no research instrument assessing fatigue SM outcomes for patients with advanced cancer is available. Objectives: to describe the development and preliminary testing of an interviewer administered instrument for assessing the frequency, and perceived levels of effectiveness and self-efficacy associated with fatigue SM behaviors in patients with advanced cancer. Methods: The development and testing of the Self-efficacy in Managing Symptoms Scale- Fatigue Subscale for Patients with Advanced Cancer (SMSFS-A) involved a number of procedures: item-generation using a comprehensive literature review and semi-structured interviews, content validity evaluation using expert panel reviews, and face validity and test-retest reliability evaluation using pilot testing. Results: Initially, 23 items (22 specific behaviors with one global item) were generated from the literature review and semi-structured interviews. After two rounds of expert panel review, the final scale was reduced to 17 items (16 behaviors with one global item). Participants in the pilot test (n=10) confirmed that the questions in this scale were clear and easy to understand. Bland-Altman analysis showed agreement of results over a one-week interval. Conclusions: The SMSFS-A items were generated using multiple sources. This tool demonstrated preliminary validity and reliability. Implications for practice: The SMSFS-A has the potential to be used for clinical and research purposes. Nurses can use this instrument for collecting data to inform the initiation of appropriate fatigue SM support for this population.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.