Objective: Randomized controlled trials (RCT) have shown supported employment (SE) to be an evidence-based practice (EBP) for people with psychiatric disabilities. Whether SE implemented under "real-world" conditions achieves outcomes comparable to RCTs is an important question for the psychiatric rehabilitation field. We examined employment outcomes achieved by SE programs in Massachusetts, and in particular examined whether fidelity to EBP standards was associated with outcomes.
Method: We examined outcomes for 3,474 clients served by 21 programs between 1997 and 2006, using multiple sources of data, including a client tracking database maintained by the SE programs as well as program site visits to assess fidelity to EBP standards. Using Generalized Estimating Equations, we modeled associations of client factors (demographics, diagnosis), program fidelity and other program factors to: (a) obtaining a job within 1 year of program enrollment; and among those obtaining jobs, (b) working 20 hours/week or more; and (c) earning $9/hr or more.
Results: There were 51% of clients who obtained a job within 1 year of enrollment. Clients served by high fidelity programs were more likely to obtain jobs (OR = 1.45) and to work 20 hr/week or more (OR = 1.52); fidelity was unrelated to wages.
Conclusions And Implications For Practice: This study contributes to the evidence that real-world programs can implement SE with fidelity and achieve outcomes on par with those found in RCTs, and that fidelity makes a difference in the outcomes programs achieve. High fidelity programs may be most effective in helping clients acquire jobs and maximize the hours they work.
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http://dx.doi.org/10.1037/prj0000097 | DOI Listing |
Background In low-income countries, clinicians trained through a context-specific trauma surgery fellowship program (TFP) can help reduce injury-related mortality to levels closer to those observed in higher-resource settings. Successful implementation, however, hinges on buy-in from local clinicians. We therefore assessed clinician support for a potential TFP in Uganda, considering perceived need, curricular recommendations, barriers, and motivating factors.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
January 2025
Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona, USA. Electronic address:
Background: There are no approved oral disease-modifying treatments for Alzheimer's disease (AD).
Objectives: The objective of this study was to assess efficacy and safety of blarcamesine (ANAVEX®2-73), an orally available small-molecule activator of the sigma-1 receptor (SIGMAR1) in early AD through restoration of cellular homeostasis including autophagy enhancement.
Design: ANAVEX2-73-AD-004 was a randomized, double-blind, placebo-controlled, 48-week Phase IIb/III trial.
Pilot Feasibility Stud
January 2025
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
J Clin Med
December 2024
Department of Postgraduate Nursing, State University of Maringá, Avenida Colombo, 5790-Campus Universitário, Maringá 87020-900, Brazil.
Evidence suggests that older adults who survived COVID-19 were exposed to greater functional dependence in their daily living activities. This study aims to examine the prevalence of functional dependence and associated factors among Brazilian older people with functional dependence 12 months after COVID-19 infection. A cross-sectional study was carried out involving people aged 60 years or older in the state of Paraná, Brazil.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!