Hope in action—facing cardiac death: A qualitative study of patients with life-threatening disease

Coping with existential challenges is important when struck by serious disease, but apart from cancer and palliative care little is known about how patients deal with such issues and maintain hope. To explore how patients with life-threatening heart disease experience hope when coping with mortality and other existential challenges, we conducted a qualitative study with semi-structured interviews. We made a purposive sample of 11 participants (26–88 years) who had experienced life-threatening disease: eight participants with serious heart disease, two with cancer, and one with severe chronic obstructive pulmonary disease. Analysis was by systematic text condensation. The findings showed that hope could enhance coping and diminish existential distress when patients were confronted with mortality and other existential challenges. Hope was observed as three types of dynamic work: to shift perception of mortality from overwhelming horror toward suppression or peaceful acceptance, to foster reconciliation instead of uncertainty when adapting to the new phase of life, and to establish go-ahead spirit instead of resignation as their identity. Meaning of life could, hence, be sustained in spite of serious threats to the persons' future, everyday life, and self-conception. The work of hoping could be supported or disturbed by relationships with family, friends, and health care professionals. Hope can be regarded as an active, dynamic state of existential coping among patients with life-threatening disease. Physicians may support this coping and thereby provide personal growth and alleviation of existential distress by skillfully identifying, acknowledging, and participating in the work of hoping performed by the patient

Sensory Communication

Contains table of contents for Section 2, an introduction and reports on twelve research projects.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00126National Institutes of Health Grant R01-DC00270U.S. Air Force - Office of Scientific Research Contract AFOSR-90-0200National Institutes of Health Grant R29-DC00625U.S. Navy - Office of Naval Research Grant N00014-88-K-0604U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-1814U.S. Navy - Naval Training Systems Center Contract N61339-93-M-1213U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0055U.S. Navy - Naval Training Systems Center Contract N61339-93-C-0083U.S. Navy - Office of Naval Research Grant N00014-92-J-4005U.S. Navy - Office of Naval Research Grant N00014-93-1-119

Convolution identities for Dunkl orthogonal polynomials from the osp(1|2) Lie superalgebra

Contains fulltext : 208890.pdf (publisher's version ) (Open Access) Contains fulltext : 208890.pdf (preprint version ) (Open Access

Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass

Inferences for forest-related spatial problems can be enhanced using remote sensing-based maps constructed with nearest neighbours techniques. The non-parametric k-nearest neighbours (k-NN) technique calculates predictions as linear combinations of observations for sample units that are nearest in a space of auxiliary variables to population units for which predictions are desired. Implementations of k-NN require four choices: a distance or similarity metric, the specific auxiliary variables to be used with the metric, the number of nearest neighbours, and a scheme for weighting the nearest neighbours. The study objective was to compare optimized k-NN configurations with respect to confidence intervals for airborne laser scanning-assisted estimates of mean volume or biomass per unit area for study areas in Norway, Italy, and the USA. Novel features of the study include a new neighbour weighting scheme, a statistically rigorous method for selecting feature variables, simultaneous optimization with respect to all four k-NN implementation choices and comparisons based on confidence intervals for population means. The primary conclusions were that optimization greatly increased the precision of estimates and that the results of optimization were similar for the k-NN configurations considered. Together, these two conclusions suggest that optimization itself is more important than the particular k-NN configuration that is optimized