How does involvement of a hospice nurse specialist impact on the experience on informal caring in palliative care? Perspectives of middle-aged partners bereaved through cancer

The aim of this study was to retrospectively explore partners' understandings and experiences in relation to caring for a loved one with a terminal illness, with a particular focus on the role of the hospice nurse specialist (HNS). Participants were purposively sampled and recruited through HNS gatekeepers. Seven middle-aged, bereaved partners participated in semi-structured, qualitative interviews. The interviews were audio recorded and transcribed verbatim and data were analysed using thematic content analysis. Five main themes emerged regarding the impact of the HNS on informal caring: ‘the ambivalence of caring’, ‘the HNS as a “confidante” in caring’, ‘the HNS as a “champion” in support’, ‘the work of the HNS – an unseen benefit’ and ‘being prepared for death and bereavement’. Findings from this study offer new insights into how involvement of a HNS impacts on the ability of carers to perform their role as an informal caregiver. Results highlight a crucial need for carers to have a clear understanding of all aspects of the HNS role so that full benefit is derived from their input. Recruitment of experienced and knowledgeable nurses is paramount, but equally important for carers is the supportive aspect of the role for which nurses need to demonstrate excellent communication skills and an intuitive, caring approach.

Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

The relationship between informal caregiving and mortality: an analysis using the ONS Longitudinal Study of England and Wales.

BACKGROUND:

Many studies have suggested that caregiving has a detrimental impact on health. However, these conclusions are challenged by research which finds evidence of a comparative survivorship advantage, as well as work which controls for group differences in the demand for care.

METHODS:

We use a large record linkage study of England and Wales to investigate the mortality risks of carers identified in the 2001 Census. The analysis focuses on individuals aged 35-74 living with others in private households and a distinction is made between those providing 1-19 and 20 or more hours of care per week. Logit models identify differences in carers' health at baseline and postcensal survival is analysed using Cox proportional hazards models.

RESULTS:

12.2% of study members reported providing 1-19 h of care and 5.4% reported providing 20 or more hours. While carers were significantly more likely to report poorer health at baseline, survival analyses suggested that they were at a significantly lower risk of dying. This comparative advantage also held when the analyses were restricted to individuals living with at least one person with poor health.

CONCLUSIONS:

The comparative mortality advantage revealed in this analysis challenges common characterisations of carers' health and draws attention to important differences in the way carers are defined in existing analyses. The survival results are consistent with work using similar data for Northern Ireland. However, the study also affords more uniform conclusions about carers' baseline health and this provides grounds for questioning existing hypotheses about the reasons for this advantage.

Assessing the needs of informal caregivers to cancer survivors: a review of the instruments

Objective: Cancer may impact negatively on an informal caregiver's health long after treatment has ended. This review identifies the self-report measures currently in use to measure caregivers need for support and determines their scientific soundness and clinical utility.

Method: A systematic electronic database search of Medline, CINAHL, PsychINFO, BNI ProQuest was conducted. The psychometric properties and clinical utility of needs assessment tools for caregivers of cancer survivors (excluding advanced disease) were extracted and summarised.

Results: Seven cancer survivor caregiver needs assessment tools were identified. Data on instrument development was well reported, although variability was noted in their structure and content. The majority demonstrated some degree of reliability and validity; only two were evaluated for test–retest reliability (CaSPUN and SPUNS) with only the SPUNS showing a high degree of reliability over time. The Health Care Needs Survey (HCNS), Needs Assessment of Family Caregivers-Cancer (NAFC-C) and Cancer Caregiving Tasks Consequences and Needs Questionnaire (CaTCoN) have been validated at various stages of the cancer continuum. Minimal data was available on responsiveness.

Conclusion: All assessment tools identified require further psychometric analysis. For research purposes, the use of the SPUNS (with its acceptable test–retest reliability) appears most appropriate; although its length may be of concern for clinical use; therefore, the shorter SCNS-P&C is likely to be more suitable for use clinically. At present, the NAFC-C demonstrates a great potential in both the research and clinical environments; however, it requires further psychometric testing before it can be fully recommended. Further analysis is necessary on ideal response formats and the meaning of a total needs score.

Size Matters: The Effect of the Scramble for Africa on Informal Institutions and Development

We argue that the partition of ethnic groups following the Scramble for Africa does not itself matter for development in Africa. It matters only when the partitioned groups are relatively small because small groups lack political representation which may promote ethnic mobilization and foster support for informal (rather than formal) institutions which then may a ect development. Furthermore, the analysis of data from the Afrobarometer shows that the persistence of informal/tribal institutions related to property rights and the rule of law is one of the possible channels through which the size of the partitioned group a ects development.

Evidence reasoning for event inference in smart transport video surveillance

In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

Global Sensitivity Analysis for MAP Inference in Graphical Models

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.

Probabilistic Inference in Credal Networks: New Complexity Results

Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models (e.g., singly connected topologies). These results clearly distinguish networks that admit efficient inferences and those where inferences are most likely hard, and settle several open questions regarding their computational complexity. We show that these results remain valid even if we disallow the use of zero probabilities. We also show that the computation of bounds on the probability of the future state in a hidden Markov model is the same whether we assume epistemic irrelevance or strong independence, and we prove an analogous result for inference in Naive Bayes structures. These inferential equivalences are important for practitioners, as hidden Markov models and Naive Bayes networks are used in real applications of imprecise probability.

IDS: A Divide-and-Conquer Algorithm for Inference in Polytree-Shaped Credal Networks

A credal network is a graph-theoretic model that represents imprecision in joint probability distributions. An inference in a credal net aims at computing an interval for the probability of an event of interest. Algorithms for inference in credal networks can be divided into exact and approximate. The selection of an algorithm is based on a trade off that ponders how much time someone wants to spend in a particular calculation against the quality of the computed values. This paper presents an algorithm, called IDS, that combines exact and approximate methods for computing inferences in polytree-shaped credal networks. The algorithm provides an approach to trade time and precision when making inferences in credal nets