Is adherence therapy an effective adjunct treatment for patients with schizophrenia spectrum disorders? A systematic review and meta-analysis

Background Poor adherence to medication in schizophrenia spectrum disorders leads to inadequate symptom control. Adherence therapy (AT) is an intervention that seeks to reduce patients’ psychiatric symptoms by enhancing treatment adherence. We aimed to systematically review the trial evidence of the effectiveness of AT on improving clinical outcomes in these patients. Method Systematic review and meta-analysis of published RCTs. We included studies testing AT as an adjunct intervention against treatment as usual or a comparator intervention in the general adult psychiatric population. The primary outcome of interest was improvement in psychiatric symptoms. Results We included six studies testing AT in schizophrenia spectrum disorders published since 2006. A meta-analysis showed AT significantly reduced psychiatric symptoms compared to usual treatment over a follow-up period of less than 1 year. We found no significant effects of AT on patients’ adherence and adherence attitudes. Conclusions AT is an effective adjunctive treatment for people with schizophrenia spectrum disorders

The Lantern, 2016-2017

• Our Lady of Perpetual Virginity • Essential Terms for the Audience • Stories Untold • Jesus Camp • The Second Avenue Schmear • Driving to the Beach • Thanks, Alice • Decay • Peanut Butter Rhapsody • Transactions • Traffic • Sissy • Melting Wings • Ocean • Small Town Summer • Third Story • Family Trees • Mixed, Just Like Me • Sour Graves • How Sweet the Sound • Goodnight, Halfmoon • I\u27m Going to Ask Him How • Music • Pizza • Manhoodlike • Meditations From a Bunk Bed in a Home on Mount Pocono • Soft • Twilight\u27s Palette • The Oracle • Cynicism • River Ganges • Pinata Body and Hearing the Gun Shot • Song With No Music • Of Mornings Considering Womanhood • 10 Hours in Philadelphia • To Cut • Sachrang • Bavarian Wave Swinger • Irish Rain • Remembrances, Well • The Roses • Buttermilk • The Universe Will Always Listen if You Ask Her, Which is Why I Like Her More Than God • A Lukewarm Light • A Thought of Death • Hobson • Decaying Light • Window Women • Dead Bee • The Imagery • For Rent • Mona Lisa MMXVIhttps://digitalcommons.ursinus.edu/lantern/1185/thumbnail.jp

The Lantern, 2015-2016

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Snowpack characteristics and modelling in the marginal snowfields of southeast Australia

Seasonal snowfields provide water resources to large populations. Snowfield observations may consist of a comprehensive collection of variables at point locations, or a limited set of variables distributed over a spatial domain from remote sensing. Both types of observations have significant limitations when attempting to characterise marginal snowfields that display large spatial heterogeneity and interannual variability. Using both observations and modelling this thesis examines the marginal snowfields of southeast Australia to improve understanding of snow water resources. In this thesis, snow density observations were used to identify the climate influences dominant in variability in physical snow properties in the Australian snowfields compared to northern hemisphere counterparts; satellite data was used to develop robust methods for monitoring snow cover in the marginal snowfields of Australia; and these findings were used to develop a spatially distributed snow model that performed well for the region.Using a large sample of snow density data and climate observations precipitation was found to be the strongest driver of seasonal snow densification rates, with air temperatures and melt-refreeze events also locally significant. Interannual variability in snow density comprised a large proportion of the variance within Australia and the western US. Using daily remote sensing retrievals from NASA's MODIS-Terra sensor, a regionalised snow detection algorithm was developed to build a snow cover dataset for Australia. Significant declines in snow cover, season duration and a shift towards earlier snowmelt date were observed, suggesting a link to observed warming trends in the area. Using a temperature-index snow model, multiple simulations throughout southeast Australia were conducted to examine physical links between melt parameters, site features and climate characteristics. Results show that the choice of snowmelt algorithm was less important for SWE estimation than other factors such as snowfall undercatch in the forcing data. The spatial application of this model highlights considerable pre-spring snowmelt and annual variability in the Australian snow pack. Overall this research highlights challenges encountered in marginal snowfields where current limitations in snow research are exacerbated. The research is novel for the region and makes significant contribution towards water resource planning in a warming climate

Spatial and temporal variability in seasonal snow density

Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.Kathryn J. Bormann, Seth Westra, Jason P. Evans and Matthew F. McCab