Consultation and illness behaviour in response to symptoms: a comparison of models from different disciplinary frameworks and suggestions for future research directions

We all get ill and social scientific interest in how we respond – the study of illness behaviour – continues unabated. Existing models are useful, but have been developed and applied within disciplinary silos, resulting in wasted intellectual and empirical effort and an absence of accumulation of knowledge across disciplines. We present a critical review and detailed comparison of three process models of response to symptoms: the Illness Action Model, the Common Sense Model of the Self-Regulation of Health and Illness and the Network Episode Model. We suggest an integrated framework in which symptoms, responses and actions are simultaneously interpreted and evaluated in the light of accumulated knowledge and through interactions. Evaluation may be subconscious and is influenced by the extent to which the symptoms impose themselves, expectations of outcomes, the resources available and understanding of symptoms' salience and possible outcomes. Actions taken are part of a process of problem solving through which both individuals and their immediate social network seek to (re)achieve ‘normality’. Response is also influenced by social structure (directly and indirectly), cultural expectations of health, the meaning of symptoms, and access to and understandings of the legitimate use of services. Changes in knowledge, in embodied state and in emotions can all be directly influential at any point. We do not underestimate the difficulty of operationalising an integrated framework at different levels of analysis. Attempts to do so will require us to move easily between disciplinary understandings to conduct prospective, longitudinal, research that uses novel methodologies to investigate response to symptoms in the context of affective as well as cognitive responses and interactions within social networks. While challenging such an approach would facilitate accumulation of knowledge across disciplines and enable movement beyond description to change in individual and organisational responses

D2D Data Offloading in Vehicular Environments with Optimal Delivery Time Selection

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular networks, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. We present an analytical framework able to predict the system performance, in terms of energy consumption, using tools from the theory of point processes, validating it through simulations, and provide a thorough performance evaluation of the proposed CDMS, in terms of energy consumption and spectrum use. Our performance analysis compares the energy consumption and spectrum use obtained with the proposed scheme with the performance of two benchmark systems. The first one is a plain classic cellular scheme, the second is a D2D data offloading scheme (that we proposed in previous works) in which the D2D transmissions are performed as soon as there is a device with the required content within the maximum D2D transmission range..

Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels

In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights (possibly) dependent on the radio channels and we pose special attention to the effect of the propagation delay occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is reached with exponential convergence speed for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We provide a closed form expression for the global consensus, showing that the effect of delays is, in general, the introduction of a bias in the final decision. Finally, we exploit our closed form expression to devise a double-step consensus mechanism able to provide an unbiased estimate with minimum extra complexity, without the need to know or estimate the channel parameters.Comment: To be published on IEEE Transactions on Signal Processin

Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Nonreciprocal Channels

In this paper we propose and analyze a distributed algorithm for achieving globally optimal decisions, either estimation or detection, through a self-synchronization mechanism among linearly coupled integrators initialized with local measurements. We model the interaction among the nodes as a directed graph with weights dependent on the radio interface and we pose special attention to the effect of the propagation delays occurring in the exchange of data among sensors, as a function of the network geometry. We derive necessary and sufficient conditions for the proposed system to reach a consensus on globally optimal decision statistics. One of the major results proved in this work is that a consensus is achieved for any bounded delay condition if and only if the directed graph is quasi-strongly connected. We also provide a closed form expression for the global consensus, showing that the effect of delays is, in general, to introduce a bias in the final decision. The closed form expression is also useful to modify the consensus mechanism in order to get rid of the bias with minimum extra complexity.Comment: Conference paper. Journal version submitted to IEEE Transactions on Signal Processing, January 10, 2007. Paper accepted for the publication on the VIII IEEE Workshop on Signal Processing Advances in Wireless Communications, (SPAWC 2007), January 22, 200

Raymond S. Duff y August B. Hoilingshead, Sickness and Society (Nueva York: Harper and Row, 1968), 390 PP

Obra ressenyada: Raymond S. DUFF y August B. HOIKINGSHEAD, Sickness and Society. Nueva York: Harper and Row, 1968

Society and the Balance of Professional Dominance, and Patient Autonomy in Medical Care

Symposium: Emerging Paradigms in Bioethic

Relationship between corneal temperature and i0ntraocular pressure in healthy Individuals. a clinical thermographic analysis

To study the geographical distribution of corneal temperature (CT) and its influence on the intraocular pressure (IOP) of healthy human volunteers. Materials and Methods. Fifteen subjects (7 M, 8 F), 33.8 +/- 17.4 years old, were enrolled in this pilot, cross-sectional study. Measurements of CT were taken after one hour with closed eyelids (CET) or closed eyelids with a cooling mask (cm-CET) and compared to baseline. Results. If compared to baseline, after CET, average CT significantly increased by 0.56 degrees C in the RE and by 0.48 degrees C in the LE (p < 0.001) and IOP concomitantly significantly increased by 1.13 mm Hg and 1.46 mm Hg, respectively, in each eye (p < 0.001). After cm-CET, average CT significantly decreased by 0.11 degrees C and 0.20 degrees C, respectively, in the RE and LE (RE p = 0.04; LE p = 0.024), followed by a significant IOP decrease of 2.19 mm Hg and 1.54 mm Hg, respectively, in each eye (RE p < 0.001; LE = 0.0019). Conclusion. Significant variations of CT occurred after CET and cm-CET and were directly correlated with significant differences of IOP. It can be speculated that both oxidative stress and sympathetic nerve fiber stimulation by temperature oscillations may affect the regulation of AH vortex flow and turnover, thus influencing IOP values

Social Network Dynamics and Biographical Disruption: The Case of “First-Timers” with Mental Illness

This study examines how dynamics surrounding biographical disruptions compare to more routine fluctuations in personal social networks. Using data from the Indianapolis Network Mental Health Study, the authors track changes in patients’ social networks over three years and compare them to a representative sample of persons with no self-reported mental illness. Overall, individuals at the onset of treatment report larger and more broadly functional social networks than individuals in the population at large. However, the number of network ties among the latter increases over time, whereas network size decreases slightly among people using mental health services. As individuals progress through treatment, less broadly supportive ties drop out of extended networks, but a core safety net remains relatively intact. The findings in this case provide evidence that social network dynamics reflect changing needs and resources: persons labeled with psychiatric disorders learn to manage illness, with functionality driving social interaction in times of biographical disruption