Validation of the test need for cognition: a study in behavioral accounting

This study aimed to validate the Need for Cognition scale (NFC) in behavioral accounting. In addition, we sought to measure the possible correlations between the level of need for cognition and the existence of cognitive biases in decisions in accounting and financial information. Two validations were performed to carry out the process of full validation – criterion and construct. The analysis was done by the examination of a sample comprised by 128 graduation students. The statistical technique used for the validation of this test was a factorial analysis for it has the ability to determine the degree of influence of a particular variable in the explanation of a factor, and the processing logistic regression was used for the explanation of possible values as a function of known values or independent variables. The results of the construct of validity showed the legitimacy of the NFC as a unidimensional scale excluding three outputs of its original scale, since the criterion validity of the results confirmed the impact of the level of cognition in maximizing the occurrence of heuristics in managerial decisions

Dissonance-Based Interventions for the Prevention of Eating Disorders: Using Persuasion Principles to Promote Health

The limited efficacy of prior eating disorder (ED) prevention programs led to the development of dissonance-based interventions (DBI) that utilize dissonance-based persuasion principles from social psychology. Although DBIs have been used to change other attitudes and behaviors, only recently have they been applied to ED prevention. This article reviews the theoretical rationale and empirical support for this type of prevention program. Relative to assessment-only controls, DBIs have produced greater reductions in ED risk factors, ED symptoms, future risk for onset of threshold or subthreshold EDs, future risk for obesity onset, and mental health utilization, with some effects persisting through 3-year follow-up. DBIs have also produced significantly stronger effects than alternative interventions for many of these outcomes, though these effects typically fade more quickly. A meta-analysis indicated that the average effects for DBIs were significantly stronger than those for non-DBI ED prevention programs that have been evaluated. DBIs have produced effects when delivered to high-risk samples and unselected samples, as well as in efficacy and effectiveness trials conducted by six independent labs, suggesting that the effects are robust and that DBIs should be considered for the prevention of other problems, such as smoking, substance abuse, HIV, and diabetes care

The Influence of Epigenetic Modifications on Metabolic Changes in White Adipose Tissue and Liver and Their Potential Impact in Exercise

Background: Epigenetic marks are responsive to a wide variety of environmental stimuli and serve as important mediators for gene transcription. A number of chromatin modifying enzymes orchestrate epigenetic responses to environmental stimuli, with a growing body of research examining how changes in metabolic substrates or co-factors alter epigenetic modifications.Scope of Review: Here, we provide a systematic review of existing evidence of metabolism-related epigenetic changes in white adipose tissue (WAT) and the liver and generate secondary hypotheses on how exercise may impact metabolism-related epigenetic marks in these tissues.Major Conclusions: Epigenetic changes contribute to the complex transcriptional responses associated with WAT lipolysis, hepatic de novo lipogenesis, and hepatic gluconeogenesis. While these metabolic responses may hypothetically be altered with acute and chronic exercise, direct testing is needed.</jats:p

Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups

Abstract Objective Deep significance clustering (DICE) is a self-supervised learning framework. DICE identifies clinically similar and risk-stratified subgroups that neither unsupervised clustering algorithms nor supervised risk prediction algorithms alone are guaranteed to generate. Materials and Methods Enabled by an optimization process that enforces statistical significance between the outcome and subgroup membership, DICE jointly trains 3 components, representation learning, clustering, and outcome prediction while providing interpretability to the deep representations. DICE also allows unseen patients to be predicted into trained subgroups for population-level risk stratification. We evaluated DICE using electronic health record datasets derived from 2 urban hospitals. Outcomes and patient cohorts used include discharge disposition to home among heart failure (HF) patients and acute kidney injury among COVID-19 (Cov-AKI) patients, respectively. Results Compared to baseline approaches including principal component analysis, DICE demonstrated superior performance in the cluster purity metrics: Silhouette score (0.48 for HF, 0.51 for Cov-AKI), Calinski-Harabasz index (212 for HF, 254 for Cov-AKI), and Davies-Bouldin index (0.86 for HF, 0.66 for Cov-AKI), and prediction metric: area under the Receiver operating characteristic (ROC) curve (0.83 for HF, 0.78 for Cov-AKI). Clinical evaluation of DICE-generated subgroups revealed more meaningful distributions of member characteristics across subgroups, and higher risk ratios between subgroups. Furthermore, DICE-generated subgroup membership alone was moderately predictive of outcomes. Discussion DICE addresses a gap in current machine learning approaches where predicted risk may not lead directly to actionable clinical steps. Conclusion DICE demonstrated the potential to apply in heterogeneous populations, where having the same quantitative risk does not equate with having a similar clinical profile. </jats:sec