'Talk to me': a mixed methods study on preferred physician behaviours during end-of-life communication from the patient perspective

Background
Despite the recognized importance of end-of-life (EOL) communication between patients and physicians, the extent and quality of such communication is lacking.

Objective
We sought to understand patient perspectives on physician behaviours during EOL communication.

Design
In this mixed methods study, we conducted quantitative and qualitative strands and then merged data sets during a mixed methods analysis phase. In the quantitative strand, we used the quality of communication tool (QOC) to measure physician behaviours that predict global rating of satisfaction in EOL communication skills, while in the qualitative strand we conducted semi-structured interviews. During the mixed methods analysis, we compared and contrasted qualitative and quantitative data.

Setting and Participants
Seriously ill inpatients at three tertiary care hospitals in Canada.

Results
We found convergence between qualitative and quantitative strands: patients desire candid information from their physician and a sense of familiarity. The quantitative results (n = 132) suggest a paucity of certain EOL communication behaviours in this seriously ill population with a limited prognosis. The qualitative findings (n = 16) suggest that at times, physicians did not engage in EOL communication despite patient readiness, while sometimes this may represent an appropriate deferral after assessment of a patient's lack of readiness.

Conclusions
Avoidance of certain EOL topics may not always be a failure if it is a result of an assessment of lack of patient readiness. This has implications for future tool development: a measure could be built in to assess whether physician behaviours align with patient readiness.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

Secure data networks for electrical distribution applications

Smart Grids are characterized by the application of information communication technology (ICT) to solve electrical energy challenges. Electric power networks span large geographical areas, thus a necessary component of many Smart Grid applications is a wide area network (WAN). For the Smart Grid to be successful, utilities must be confident that the communications infrastructure is secure. This paper describes how a WAN can be deployed using WiMAX radio technology to provide high bandwidth communications to areas not commonly served by utility communications, such as generators embedded in the distribution network. A planning exercise is described, using Northern Ireland as a case study. The suitability of the technology for real-time applications is assessed using experimentally obtained latency data.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Statistical data correction for unreliable memories

In this paper, we introduce a statistical data-correction framework that aims at improving the DSP system performance in presence of unreliable memories. The proposed signal processing framework implements best-effort error mitigation for signals that are corrupted by defects in unreliable storage arrays using a statistical correction function extracted from the signal statistics, a data-corruption model, and an application-specific cost function. An application example to communication systems demonstrates the efficacy of the proposed approach.

IEEE C37.118-2 Synchrophasor Communication Framework

Synchrophasors have become an important part of the modern power system and numerous applications have been developed covering wide-area monitoring, protection and control. Most applications demand continuous transmission of synchrophasor data across large geographical areas and require an efficient communication framework. IEEE C37.118-2 evolved as one of the most successful synchrophasor communication standards and is widely adopted. However, it lacks a predefined security mechanism and is highly vulnerable to cyber attacks. This paper analyzes different types of cyber attacks on IEEE C37.118-2 communication system and evaluates their possible impact on any developed synchrophasor application. Further, the paper also recommends an efficent security mechanism that can provide strong protection against cyber attacks. Although, IEEE C37.118-2 has been widely adopted, there is no clear understanding of the requirements and limitations. To this aim, the paper also presents detailed performance evaluation of IEEE C37.118-2 implementations which could help determine required resources and network characteristics before designing any synchrophasor application.

Atomic data for modelling fusion and astrophysical plasmas

Trends and focii of interest in atomic modelling and data are identified in connection with recent observations and experiments in fusion and astrophysics. In the fusion domain, spectral observations are included of core, beam penetrated and divertor plasma. The helium beam experiments at JET and the studies with very heavy species at ASDEX and JET are noted. In the astrophysics domain, illustrations are given from the SOHO and CHANDRA spacecraft which span from the solar upper atmosphere, through soft x-rays from comets to supernovae remnants. It is shown that non-Maxwellian, dynamic and possibly optically thick regimes must be considered. The generalized collisional-radiative model properly describes the collisional regime of most astrophysical and laboratory fusion plasmas and yields self-consistent derived data for spectral emission, power balance and ionization state studies. The tuning of this method to routine analysis of the spectral observations is described. A forward look is taken as to how such atomic modelling, and the atomic data which underpin it, ought to evolve to deal with the extended conditions and novel environments of the illustrations. It is noted that atomic physics influences most aspects of fusion and astrophysical plasma behaviour but the effectiveness of analysis depends on the quality of the bi-directional pathway from fundamental data production through atomic/plasma model development to the confrontation with experiment. The principal atomic data capability at JET, and other fusion and astrophysical laboratories, is supplied via the Atomic Data and Analysis Structure (ADAS) Project. The close ties between the various experiments and ADAS have helped in this path of communication.

Quantification of the smoking-associated cancer risk with rate advancement periods: meta-analysis of individual participant data from cohorts of the CHANCES consortium

BACKGROUND: Smoking is the most important individual risk factor for many cancer sites but its association with breast and prostate cancer is not entirely clear. Rate advancement periods (RAPs) may enhance communication of smoking related risk to the general population. Thus, we estimated RAPs for the association of smoking exposure (smoking status, time since smoking cessation, smoking intensity, and duration) with total and site-specific (lung, breast, colorectal, prostate, gastric, head and neck, and pancreatic) cancer incidence and mortality.

METHODS: This is a meta-analysis of 19 population-based prospective cohort studies with individual participant data for 897,021 European and American adults. For each cohort we calculated hazard ratios (HRs) for the association of smoking exposure with cancer outcomes using Cox regression adjusted for a common set of the most important potential confounding variables. RAPs (in years) were calculated as the ratio of the logarithms of the HRs for a given smoking exposure variable and age. Meta-analyses were employed to summarize cohort-specific HRs and RAPs.

RESULTS: Overall, 140,205 subjects had a first incident cancer, and 53,164 died from cancer, during an average follow-up of 12 years. Current smoking advanced the overall risk of developing and dying from cancer by eight and ten years, respectively, compared with never smokers. The greatest advancements in cancer risk and mortality were seen for lung cancer and the least for breast cancer. Smoking cessation was statistically significantly associated with delays in the risk of cancer development and mortality compared with continued smoking.

CONCLUSIONS: This investigation shows that smoking, even among older adults, considerably advances, and cessation delays, the risk of developing and dying from cancer. These findings may be helpful in more effectively communicating the harmful effects of smoking and the beneficial effect of smoking cessation.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.