Factors affecting the disclosure of diabetes by ethnic minority patients: a qualitative study among Surinamese in the Netherlands

<p>Abstract</p> <p>Background</p> <p>Diabetes and related complications are common among ethnic minority groups. Community-based social support interventions are considered promising for improving diabetes self-management. To access such interventions, patients need to disclose their diabetes to others. Research on the disclosure of diabetes in ethnic minority groups is limited. The aim of our study was to explore why diabetes patients from ethnic minority populations either share or do not share their condition with people in their wider social networks.</p> <p>Methods</p> <p>We conducted a qualitative study using semi-structured interviews with 32 Surinamese patients who were being treated for type 2 diabetes by general practitioners in Amsterdam, the Netherlands.</p> <p>Results</p> <p>Most patients disclosed their diabetes only to very close family members. The main factor inhibiting disclosure to people outside this group was the Surinamese cultural custom that talking about disease is taboo, as it may lead to shame, gossip, and social disgrace for the patient and their family. Nevertheless, some patients disclosed their diabetes to people outside their close family circles. Factors motivating this decision were mostly related to a need for facilities or support for diabetes self-management.</p> <p>Conclusions</p> <p>Cultural customs inhibited Surinamese patients in disclosing their diabetes to people outside their very close family circles. This may influence their readiness to participate in community-based diabetes self-management programmes that involve other groups. What these findings highlight is that public health researchers and initiatives must identify and work with factors that influence the disclosure of diabetes if they are to develop community-based diabetes self-management interventions for ethnic minority populations.</p

Training compliance control yields improvements in drawing as a function of beery scores

Many children have difficulty producing movements well enough to improve in sensori-motor learning. Previously, we developed a training method that supports active movement generation to allow improvement at a 3D tracing task requiring good compliance control. Here, we tested 7–8 year old children from several 2nd grade classrooms to determine whether 3D tracing performance could be predicted using the Beery VMI. We also examined whether 3D tracing training lead to improvements in drawing. Baseline testing included Beery, a drawing task on a tablet computer, and 3D tracing. We found that baseline performance in 3D tracing and drawing co-varied with the visual perception (VP) component of the Beery. Differences in 3D tracing between children scoring low versus high on the Beery VP replicated differences previously found between children with and without motor impairments, as did post-training performance that eliminated these differences. Drawing improved as a result of training in the 3D tracing task. The training method improved drawing and reduced differences predicted by Beery scores

Mapping Proprioception across a 2D Horizontal Workspace

Relatively few studies have been reported that document how proprioception varies across the workspace of the human arm. Here we examined proprioceptive function across a horizontal planar workspace, using a new method that avoids active movement and interactions with other sensory modalities. We systematically mapped both proprioceptive acuity (sensitivity to hand position change) and bias (perceived location of the hand), across a horizontal-plane 2D workspace. Proprioception of both the left and right arms was tested at nine workspace locations and in 2 orthogonal directions (left-right and forwards-backwards). Subjects made repeated judgments about the position of their hand with respect to a remembered proprioceptive reference position, while grasping the handle of a robotic linkage that passively moved their hand to each judgement location. To rule out the possibility that the memory component of the proprioceptive testing procedure may have influenced our results, we repeated the procedure in a second experiment using a persistent visual reference position. Both methods resulted in qualitatively similar findings. Proprioception is not uniform across the workspace. Acuity was greater for limb configurations in which the hand was closer to the body, and was greater in a forward-backward direction than in a left-right direction. A robust difference in proprioceptive bias was observed across both experiments. At all workspace locations, the left hand was perceived to be to the left of its actual position, and the right hand was perceived to be to the right of its actual position. Finally, bias was smaller for hand positions closer to the body. The results of this study provide a systematic map of proprioceptive acuity and bias across the workspace of the limb that may be used to augment computational models of sensory-motor control, and to inform clinical assessment of sensory function in patients with sensory-motor deficits

Evaluating Forecasting Methods

Ideally, forecasting methods should be evaluated in the situations for which they will be used. Underlying the evaluation procedure is the need to test methods against reasonable alternatives. Evaluation consists of four steps: testing assumptions, testing data and methods, replicating outputs, and assessing outputs. Most principles for testing forecasting methods are based on commonly accepted methodological procedures, such as to prespecify criteria or to obtain a large sample of forecast errors. However, forecasters often violate such principles, even in academic studies. Some principles might be surprising, such as do not use R-square, do not use Mean Square Error, and do not use the within-sample fit of the model to select the most accurate time-series model. A checklist of 32 principles is provided to help in systematically evaluating forecasting methods

The impact of future socio-economic and climate changes on agricultural land use and the wider environment in East Anglia and North West England using a metamodel system

This paper describes a procedure to use a model interactively to investigate future land use by studying a wide range of scenarios defining climate, technological and socio-economic changes. A full model run of several hours has been replaced by a metamodel version which takes a few seconds, and provides the user with an immediate visual output and with the ability to examine easily which factors have the greatest effect. The Regional Impact Simulator combines a model of agricultural land use choices linked with models of urban growth, flooding risk, water quality and consequences for wildlife to estimate plausible futures of agricultural land on a timescale of 20–50 years. The model examines the East Anglian and North West regions of the United Kingdom at a grid resolution of 5 × 5 km, and for each scenario estimates the most likely cropping and its profitability at each location, and classifies land use as arable, intensive or extensive grassland or abandoned. From a modelling viewpoint the metamodel approach enables iteration. It is thus possible to determine how product prices change so that production meets demand. The results of the study show that in East Anglia cropping remains quite stable over a wide range of scenarios, though grassland is eliminated in scenarios with the 2050s High climate scenario – almost certainly due to the low yield in the drier conditions. In the North West there is a very much greater range of outcomes, though all scenarios suggest a reduction in grassland with the greatest in the 2050s High climate scenario combined with the “Regional Stewardship” (environmental) socio-economic scenario. The effects of the predicted changes in land use on plant species showed suitability for species to vary greatly, particularly between the socio-economic scenarios, due to detrimental effects from increases in nitrogen fertilisation. A complete simulation with the Regional Impact Simulator takes around 15 seconds (computer-dependent), which users who responded felt was adequate or better than adequate. The main areas for future improvement, such as the speed of the system, user interaction and the accuracy and detail of the modelling, are c