Brobergvaldez4136
Nurses' perception towards job satisfaction and willingness to recommend their workplace are relevant to a number of areas including the quality of nursing care delivery. Hence, an increasing number of scholars seek to understand the factors that influence these two concepts. Yet, inclusiveness and openness to innovation are under-investigated.
The paper focuses on the relative importance that factors like propensity towards innovation, working conditions and inclusion have on nurses' job satisfaction and their willingness to recommend their workplace.
A large sample of nurses was extracted from the organizational climate survey carried out in all healthcare authorities of nine Italian Regions through the years 2016-2018. Descriptive and multilevel regressions were carried out to investigate the factors that influence nurses' job satisfaction and their willingness to recommend the hospital in which they work in, analysing both overall and in specific age classes.
When recommending a workplace, nurses e age classes. Findings identify the levers to be used in order to attract nurse workforce and increase nurses' job satisfaction. These levers are partially different for young and senior nurses.Forbes, Wright, Markon, and Krueger claim that psychopathology network characteristics have "limited" or "poor" replicability, supporting their argument primarily with data from two waves of an observational study on depression and anxiety. They developed "direct metrics" to gauge change across networks (e.g., change in edge sign), and used these results to support their conclusion. Three key flaws undermine their critique. First, nonreplication across empirical datasets does not provide evidence against a method; such evaluations of methods are possible only in controlled simulations when the data-generating model is known. Second, they assert that the removal of shared variance necessarily decreases reliability. This is not true. Depending on the causal model, it can either increase or decrease reliability. Third, their direct metrics do not account for normal sampling variability, leaving open the possibility that the direct differences between samples are due to normal, unproblematic fluctuations. As an alternative to their direct metrics, we provide a Bayesian re-analysis that quantifies uncertainty and compares relative evidence for replication (i.e., equivalence) versus nonreplication (i.e., nonequivalence) for each network edge. This approach provides a principled roadmap for future assessments of network replicability. Our analysis indicated substantial evidence for replication and scant evidence for nonreplication.
Clinical outcomes of patients diagnosed with high-risk acute myeloid leukemia (AML) are poor, and relapse or refractoriness is main cause of treatment failure, even in those who underwent standard allogeneic stem cell transplantation (allo-SCT). Therefore, innovative or additional approaches are necessary to overcome refractoriness to the graft-versus-leukemia (GVL) effect immediately after allo-SCT.
Hypomethylating agents (HMA) present a feasible option that can be adopted during the post-transplant phase. Moreover, combination strategies based on HMA may induce a synergistic effect by promoting anti-leukemic effects that overcome residual leukemic burden, and it is a well-tolerated therapeutic option for high-risk disease. Relevant literatures published in the last 30years were searched from PubMed to review the topic of AML, allo-SCT, and HMAs.
Post-transplant therapy is strongly needed to improve the outcomes of allogeneic transplantation for certain AML patients classified with high-risk disease. selleckchem In that sense, prophylactic and preemptive HMAs are a promising additive therapy for allogeneic recipients.
Post-transplant therapy is strongly needed to improve the outcomes of allogeneic transplantation for certain AML patients classified with high-risk disease. In that sense, prophylactic and preemptive HMAs are a promising additive therapy for allogeneic recipients.
The effectiveness of Cognitive Behavioral Therapy for Insomnia (CBT-I) for alleviating sleep problems is well established. However, few studies have explored its impact on work productivity and activity.
Seventy-seven currently employed adults with insomnia disorder (59 females) recruited to a randomized trial of digital versus face-to-face CBT-I.
The general health version of the Work Productivity and Activity Impairment questionnaire was used to measure absenteeism, presenteeism, total work impairment, and activity impairment. We assessed changes in work productivity and activity pre-to-post-therapy for the total sample and then for subgroups categorized according to response or remission of insomnia disorder (evaluated using the Insomnia Severity Index).
Study participants showed significant improvements in presenteeism (
=.001; Cohen's
=0.46), total work impairment (
<.001;
=0.48), and activity (
<.001;
=0.66), but not absenteeism (
=.51;
=0.084) between baseline and follow-u studies which should strive to include objective measurement of daytime activity and work performance more frequently.
The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge.
Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment.
We model the driver's decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator.
Human data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics.
