The Influence of Burnout, Resilience, and Resources to Support Clinical Practice Among Newly Licensed Nurses Intended Job Plans.

Objective: Survey newly licensed nurses (NLNs) with respect to burnout, resilience, perception of the work environment, and intended short- and long-term job plans.

Background: NLN retention is a significant contributing factor to the nursing shortage.

Methods: A 126-item survey measuring demographics, burnout, resilience, work environment, and job plans distributed via email, using Listservs to recruit nurses licensed for 5 years or less.

"It's put a routine and regimen in my life" - Participant experiences with a programme of community walking and home-based circuit training for intermittent claudication.

Introduction: The WALKSTRONG trial includes a programme of community walking and home-based circuit training which has been developed for people with intermittent claudication (IC). The aim of the present study was to determine the acceptability of the programme for those who took part, by gleaning their opinions and experiences.

Methods: All participants eligible for the WALKSTRONG trial were approached regarding completing a semi-structured interview, selected from three groups: A) programme completers, B) programme withdrawers and C) programme decliners.

Molecular heterogeneity in urothelial carcinoma and determinants of clinical benefit to PD-L1 blockade.

Checkpoint inhibitors targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have revolutionized cancer therapy across many indications including urothelial carcinoma (UC). Because many patients do not benefit, a better understanding of the molecular mechanisms underlying response and resistance is needed to improve outcomes. We profiled tumors from 2,803 UC patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab by RNA sequencing (RNA-seq), a targeted DNA panel, immunohistochemistry, and digital pathology.

The development of early warning scores or alerting systems for the prediction of adverse events in psychiatric patients: a scoping review.

Background: Adverse events in psychiatric settings present ongoing challenges for both patients and staff. Despite advances in psychiatric interventions and treatments, research on early warning scores and tools to predict patient deterioration is limited. This review provides a summary of the few tools that have been developed in a psychiatric setting, comparing machine learning (ML) and nonmachine learning/traditional methodologies.