Good things peak in pairs: a note on the bimodality coefficient.
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ABSTRACT:
SUBMITTER: Pfister R
PROVIDER: S-EPMC3791391 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIntensive care unit (ICU) personnel have an elevated prevalence of job-related burn-out and post-traumatic stress disorder, which can ultimately impact patient care. To strengthen healthcare workers' skills to deal with stressful events, it is important to focus not only on minimising suffering but also on increasing happiness, as this entails many more benefits than simply feeling good. Thus, the purpose of this study was to explore the content of the 'good things' reported by healthcare workers participating in the 'Three Good Things' intervention.MethodsIn a tertiary care medical centre, a sample of 89 neonatal ICU (NICU) healthcare professionals registered for the online intervention. Of these, 32 individuals eventually participated fully in the 14-day online Three Good Things intervention survey. Daily emails reminded participants to reflect on and respond to the questions: "What are the three things that went well today?" and "What was your role in bringing them about?" To analyse their responses, we applied a thematic analysis, which was guided by our theoretical understanding of resilience.ResultsInvolving more than 1300 statements, the Three Good Things responses of the 32 study participants, including registered nurses, physicians and neonatal nurse practitioners, led to the identification of three main themes: (1) having a good day at work; (2) having supportive relationships and (3) making meaningful use of self-determined time.ConclusionsThe findings show the personal and professional relevance of supportive relationships strengthened by clear communication and common activities that foster positive emotions. The Three Good Things exercise acknowledges the importance of self-care in healthcare workers and appears to promote well-being, which might ultimately strengthen resilience.
| S-EPMC5623381 | biostudies-literature
Project description:ObjectivesHigh rates of healthcare worker (HCW) burn-out have led many to label it an 'epidemic' urgently requiring interventions. This prospective pilot study examined the efficacy, feasibility and evaluation of the 'Three Good Things' (3GT) intervention for HCWs, and added burn-out and work-life balance to the set of well-being metrics.Methods228 HCWs participated in a prospective, repeated measures study of a web-based 15-day long 3GT intervention. Assessments were collected at baseline and 1, 6 and 12-month post-intervention. The primary measure of efficacy was a derivative of the emotional exhaustion subscale of the Maslach Burnout Inventory. The secondary measures were validated instruments assessing depression symptoms, subjective happiness, and work-life balance. Paired samples t-tests and Cohen's d effect sizes for correlated samples were used to examine the efficacy of the intervention.Results3GT participants exhibited significant improvements from baseline in emotional exhaustion, depression symptoms and happiness at 1 month, 6 months and 12 months, and in work-life balance at 1 month and 6 months (effect sizes 0.16-0.52). Exploratory subgroup analyses of participants meeting 'concerning' criteria at baseline revealed even larger effects at all assessment points (0.55-1.57). Attrition rates were similar to prior 3GT interventions.Conclusion3GT appears a promising low-cost and brief intervention for improving HCW well-being.Ethics and disseminationThis study is approved by the Institutional Review Board of Duke University Health System (Pro00063703). All participants are required to give their informed consent prior to any study procedure.
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Project description:We examine salient trends of influenza pandemics in Australia, a rapidly urbanizing nation. To do so, we implement state-of-the-art influenza transmission and progression models within a large-scale stochastic computer simulation, generated using comprehensive Australian census datasets from 2006, 2011, and 2016. Our results offer a simulation-based investigation of a population's sensitivity to pandemics across multiple historical time points and highlight three notable trends in pandemic patterns over the years: increased peak prevalence, faster spreading rates, and decreasing spatiotemporal bimodality. We attribute these pandemic trends to increases in two key quantities indicative of urbanization: the population fraction residing in major cities and international air traffic. In addition, we identify features of the pandemic's geographic spread that we attribute to changes in the commuter mobility network. The generic nature of our model and the ubiquity of urbanization trends around the world make it likely for our results to be applicable in other rapidly urbanizing nations.
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Project description:Historically, time preferences are modelled by assuming constant discounting, which implies a constant level of impatience. The prevailing empirical finding, however, is decreasing impatience (DI), meaning that levels of impatience decrease over time. Theoretically, such changes in impatience are crucial to understand behavior and self-control problems. Very few methods exist to measure DI without being restricted to or confounded by certain assumptions about the discounting function or utility curve. One such measure is the recently introduced DI-index, which has been applied to both monetary and health outcomes. The DI-index quantifies the deviation from constant impatience and is flexible enough to capture both increasing and decreasing impatience. In this study, we apply the DI-index to measure impatience for health outcomes in a reference-dependent framework. That is, we measure impatience for both health gains and health losses compared to a reference-point, in individual and societal settings, using a within-subjects design (n = 98). We allowed for both positive and negative discounting, since negative discounting has been observed for losses (i.e. preferring to incur losses earlier rather than later) in earlier work. To capture changes in time inconsistency when subjects show negative discounting (i.e. patience), we modify the DI-index to a decreasing (im)patience (DIP)-index, which can be applied without loss of generality. As in earlier work, we observe large heterogeneity in time consistency; i.e., a mix of decreasing, increasing and constant (im)patience. Across all DIP-indices elicited, increasing impatience was the modal preference for those satisfying impatience, and decreasing patience for those satisfying patience. No systematic differences were observed between health gains and losses or between societal and individual outcomes. This suggests that for health outcomes both patient and impatient individuals assign more importance to time differences delayed further in the future.
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Project description:Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.
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Project description: Not available
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Project description:The aim of our study was to test the effectiveness of the "three good things for others" intervention. We used the randomized controlled trial method, with four measurements (pretest, posttest, follow-up after 2 weeks, follow-up after 4 weeks) and with random assignment of participants to experimental and placebo control groups. We investigated the effects of the intervention on prosocial behavior, and in addition on positive and negative affect, and positive orientation (a general tendency to approach reality in a positive way). The results showed an increase in positive affect and a decrease in negative affect in the experimental group a day after the intervention. These effects, however, did not endure over the next 2 or 4 weeks. We also observed a statistically significant increase in prosocial behavior in the placebo control group, in which participants were engaged in a task of recalling childhood memories. The results are discussed and recommendations for future studies are proposed.
| S-EPMC8170032 | biostudies-literature
Project description:Infants born at very low gestational age contribute disproportionately to neonatal morbidity and mortality. Advancements in antenatal steroid therapies and surfactant replacement have favored the survival of infants with ever-more immature lungs. Despite such advances in medical care, cardiopulmonary and neurological impairment prevail in constituting the major adverse outcomes for neonatal intensive care unit survivors. With no single effective therapy for either the prevention or treatment of such neonatal disorders, the need for new tools to treat and reduce risk of further complications associated with extreme preterm birth is urgent. Mesenchymal stem/stromal cell (MSC)-based approaches have shown promise in numerous experimental models of lung injury relevant to neonatology. Recent studies have highlighted that the therapeutic potential of MSCs is harnessed in their secretome, and that the therapeutic vector therein is represented by the exosomes released by MSCs. In this review, we summarize the development and significance of stem cell-based therapies for neonatal diseases, focusing on preclinical models of neonatal lung injury. We emphasize the development of MSC exosome-based therapeutics and comment on the challenges in bringing these promising interventions to clinic.
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Project description:Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.
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Project description:PDBsum (http://www.ebi.ac.uk/pdbsum) provides summary information about each experimentally determined structural model in the Protein Data Bank (PDB). Here we describe some of its most recent features, including figures from the structure's key reference, citation data, Pfam domain diagrams, topology diagrams and protein-protein interactions. Furthermore, it now accepts users' own PDB format files and generates a private set of analyses for each uploaded structure.
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