158 research outputs found
The effects of an interdisciplinary employment program on paid employment and mental health among persons with severe mental disorders
Purpose: This study evaluates the effects of the interdisciplinary employment program ‘Work As Best Care (WABC)’ on employment participation and mental health of persons with severe mental disorders.Methods: WABC is a ‘work first’ employment program for unemployed persons with severe mental disorders in which employment professionals work closely together with mental health professionals. In a longitudinal non-randomized controlled study, participants of WABC (n = 35) are compared with participants of the control group (n = 37), who received regular employment support. Participants were followed for 1 year and filled out questionnaires on individual characteristics and health at baseline, after 6 and 12 months. This information was enriched with monthly register data on employment status from 2015 until 2020. Difference-in-differences analyses were performed to investigate changes in employment participation among participants of WABC and the control group. A generalized linear mixed-effects model was used to compare changes in mental health (measured on 0–100 scale) between the two groups. Results: Before WABC, employment participation was 22.0%points lower among participants of WABC compared to the control group. After starting WABC, employment participation increased with 15.3%points per year among participants of WABC, compared to 5.6%points in the control group. Among all participants of WABC, no change in mental health was found (β 1.0, 95% CI − 3.4; 5.5). Only female participants of WABC showed a significant change in mental health (β 8.0, 95% CI 2.6; 13.4). Conclusion: To enhance employment participation of persons with severe mental disorders, an interdisciplinary ‘work-first’ approach in which professionals of employment services and mental health services work in close collaboration, is of paramount importance.</p
The effects of an interdisciplinary employment program on paid employment and mental health among persons with severe mental disorders
Purpose: This study evaluates the effects of the interdisciplinary employment program ‘Work As Best Care (WABC)’ on employment participation and mental health of persons with severe mental disorders.Methods: WABC is a ‘work first’ employment program for unemployed persons with severe mental disorders in which employment professionals work closely together with mental health professionals. In a longitudinal non-randomized controlled study, participants of WABC (n = 35) are compared with participants of the control group (n = 37), who received regular employment support. Participants were followed for 1 year and filled out questionnaires on individual characteristics and health at baseline, after 6 and 12 months. This information was enriched with monthly register data on employment status from 2015 until 2020. Difference-in-differences analyses were performed to investigate changes in employment participation among participants of WABC and the control group. A generalized linear mixed-effects model was used to compare changes in mental health (measured on 0–100 scale) between the two groups. Results: Before WABC, employment participation was 22.0%points lower among participants of WABC compared to the control group. After starting WABC, employment participation increased with 15.3%points per year among participants of WABC, compared to 5.6%points in the control group. Among all participants of WABC, no change in mental health was found (β 1.0, 95% CI − 3.4; 5.5). Only female participants of WABC showed a significant change in mental health (β 8.0, 95% CI 2.6; 13.4). Conclusion: To enhance employment participation of persons with severe mental disorders, an interdisciplinary ‘work-first’ approach in which professionals of employment services and mental health services work in close collaboration, is of paramount importance.</p
Exploring the psychological effects of global health rotations on first year medical students through the analysis of student reflection.
Effect of alpha-7 nicotinic acetylcholine receptor activation on beta-amyloid induced recognition memory impairment. Possible role of neurovascular function
Structure and Stability of an Amorphous Metal
Using molecular dynamics simulations, with a realistic many-body
embedded-atom potential, and a novel method to characterize local order, we
study the structure of pure nickel during the rapid quench of the liquid and in
the resulting glass. In contrast with previous simulations with pair
potentials, we find more crystalline order and fewer icosahedra for slower
quenching rates, resulting in a glass less stable against crystallization. It
is shown that there is not a specific amorphous structure, only the arrest of
the transition from liquid to crystal, resulting in small crystalline clusters
immersed in an amorphous matrix with the same structure of the liquid.Comment: 4 pages, 4 ps figs., to appear in Phys. Rev. Let
Engineering characterization of ground motion. Task I. Effects of characteristics of free-field motion on structural response
This report presents the results of the first task of a two-task study on the engineering characterization of earthquake ground motion for nuclear power plant design. The overall objective of this study is to develop recommendations for methods for selecting design response spectra or acceleration time histories to be used to characterize motion at the foundation level of nuclear power plants. Task I of the study develops a basis for selecting design response spectra, taking into account the characteristics of free-field ground motion found to be significant in causing structural damage
Pressure-induced changes in the electronic structure of americium metal
We have conducted electronic-structure calculations for Am metal under pressure to investigate the behavior of the 5f-electron states. Density-functional theory (DFT) does not reproduce the experimental photoemission spectra for the ground-state phase where the 5f electrons are localized, but the theory is expected to be correct when 5f delocalization occurs under pressure. The DFT prediction is that peak structures of the 5f valence band will merge closer to the Fermi level during compression indicating presence of itinerant 5f electrons. Existence of such 5f bands is argued to be a prerequisite for the phase transitions, particularly to the primitive orthorhombic AmIV phase, but does not agree with modern dynamical-mean-field theory (DMFT) results. Our DFT model further suggests insignificant changes of the 5f valence under pressure in agreement with recent resonant x-ray emission spectroscopy, but in contradiction to the DMFT predictions. The influence of pressure on the 5f valency in the actinides is discussed and is shown to depend in a non-trivial fashion on 5f band position and occupation relative to the spd valence bands
Fall Classification by Machine Learning Using Mobile Phones
Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls
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