Intolerance of uncertainty and negative emotions among high school art students during COVID-19 pandemic: a moderated mediation analysis.

Objectives: A number of high school art students experience negative emotions during their preparation for the art college entrance examination, characterized by worries and fear of uncertainty. Therefore, how individual difference factors, such as intolerance of uncertainty, affect the negative emotions of students needs to be examined. Inspired by the integrative model of uncertainty tolerance, the current study seeks to explain the association between intolerance of uncertainty and negative emotions by testing the potential mediating role of psychological capital and the moderating role of family functioning. Patients and methods: A total of 919 Chinese high school art students (Mage = 18.50 years, range = 16-22) participated from November 2022 to December 2022. Convenience sampling strategies were used. The participants were asked to complete the measures of intolerance of uncertainty scale, psychological capital questionnaire, depression anxiety stress scale, and family adaptability and cohesion evaluation scale. The data were analyzed using Pearson's r correlations and moderated mediation analysis. Results: Results showed that intolerance of uncertainty was positively associated with negative emotions but negatively associated with psychological capital, which in turn, was negatively associated with negative emotions. Psychological capital mediated the indirect link of intolerance of uncertainty with negative emotions. Family functioning buffered the impact of psychological capital on negative emotions. Conclusion: This study can enhance our understanding of the intolerance of uncertainty on negative emotions and provide insights on interventions for high school art students' negative emotions for educators. The interventions targeting intolerance of uncertainty, psychological capital and family functioning may be beneficial in reducing the effect of intolerance of uncertainty on negative emotions faced by high school art students.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

The origins of unpredictability in life outcome prediction tasks.

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

How and why do doctors communicate diagnostic uncertainty: An experimental vignette study.

BACKGROUND: Diagnostic uncertainty is common, but its communication to patients is under-explored. This study aimed to (1) characterise variation in doctors' communication of diagnostic uncertainty and (2) explore why variation occurred. METHODS: Four written vignettes of clinical scenarios involving diagnostic uncertainty were developed. Doctors were recruited from five hospitals until theoretical saturation was reached (n = 36). Participants read vignettes in a randomised order, and were asked to discuss the diagnosis/plan with an online interviewer, as they would with a 'typical patient'. Semi-structured interviews explored reasons for communication choices. Interview transcripts were coded; quantitative and qualitative (thematic) analyses were undertaken. RESULTS: There was marked variation in doctors' communication: in their discussion about differential diagnoses, their reference to the level of uncertainty in diagnoses/investigations and their acknowledgement of diagnostic uncertainty when safety-netting. Implicit expressions of uncertainty were more common than explicit. Participants expressed both different communication goals (including reducing patient anxiety, building trust, empowering patients and protecting against diagnostic errors) and different perspectives on how to achieve these goals. Training in diagnostic uncertainty communication is rare, but many felt it would be useful. CONCLUSIONS: Significant variation in diagnostic uncertainty communication exists, even in a controlled setting. Differing communication goals-often grounded in conflicting ethical principles, for example, respect for autonomy versus nonmaleficence-and differing ideas on how to prioritise and achieve them may underlie this. The variation in communication behaviours observed has important implications for patient safety and health inequalities. Patient-focused research is required to guide practice. PATIENT OR PUBLIC CONTRIBUTION: In the design stage of the study, two patient and public involvement groups (consisting of members of the public of a range of ages and backgrounds) were consulted to gain an understanding of patient perspectives on the concept of communicating diagnostic uncertainty. Their feedback informed the formulations of the research questions and the choice of vignettes used.

The relationship between unpredictability in childhood and depression among college students: the mediating roles of coping style and resilience.

BACKGROUND: According to previous studies, unpredictability in childhood could significantly increase the risk of depression in adulthood. Only a few studies have explored the relationship between these two variables in China. This paper aims to explore the relationship between unpredictability in childhood and depression and examine the mediating roles of coping styles and resilience. METHODS: We investigated 601 college students, who had an average age of 19.09 (SD = 2.78) years. Participants completed questionnaires regarding unpredictability in childhood, coping style, resilience, and depression. We analyzed survey data using the bias-corrected bootstrap method. RESULTS: The findings revealed a significant positive association between unpredictability in childhood and depression among college students. Mature coping style, immature coping style, and resilience were found to mediate this relationship independently. Furthermore, the study unveiled a serial mediation process, wherein both mature and immature coping styles, followed by resilience, sequentially mediate the relationship between unpredictability in childhood and depression, underscoring the complex interplay between these variables. CONCLUSIONS: The results indicated that the risk of depression among college students who have experienced unpredictable childhood should be valued. Attention to coping styles and resilience should be paid to decrease depression among college students who have experienced unpredictable childhood.

Readiness for return to work and its influencing factors among head and neck cancer patients: a cross-sectional study.

PURPOSE: This study aims to investigate the Readiness for Return-to-Work (RRTW) of patients with head and neck tumours and to analyse the relationships among self-efficacy, disease uncertainty, psychosocial adaptation, and RRTW in head and neck cancer (HNC) patients. METHODS: A cross-sectional study was conducted with 259 HNC patients with a discharge length of ≥1 month at a tertiary hospital in Liaoning Province. The research tools included a self-designed general information questionnaire, the Readiness for Return-to-Work (RRTW) Scale, the General Self-Efficacy Scale (GSES), the Mishel Uncertainty in Illness Scale (MUIS), and the Self-Reporting Psychosocial Adjustment to Illness Scale (PAIS-SR). Descriptive statistical analysis, the rank sum test, Spearman correlation analysis, and ordered multiple and dichotomous logistic regression analyses were used. RESULTS: The overall RRTW among HNC patients was low (41.9%). HNC patients who did not return to work were mainly in the precontemplation stage (38.1%) and contemplation stage (29.9%). HNC patients who returned to work were mainly in the active maintenance stage (64.2%). Children's status (OR = 0.218, 95% CI 0.068-0.703), self-efficacy (OR = 1.213, 95% CI 1.012-1.454), unpredictability (OR = 0.845, 95% CI 0.720-0.990), occupational environment (OR = 0.787, 95% CI 0.625-0.990), and family environment (OR = 0.798, 95% CI 0.643-0.990) influence the RRTW of HNC patients who have not returned to work. Educational level (OR = 62.196, 95% CI 63.307-68.567), children's status (OR = 0.058, 95% CI 1.004-2.547), self-efficacy (OR = 1.544, 95% CI 3.010-8.715), unpredictability (OR = 0.445, 95% CI 1.271-2.280), and psychological status (OR = 0.340, 95% CI 1.141-2.401) influence the RRTW of HNC patients who have returned to work. CONCLUSION: Children's status, education level, self-efficacy, illness uncertainty, and psychosocial adjustment are crucial to RRTW. This study provides a theoretical basis for formulating intervention measures aimed at improving the RRTW of patients.

Market liquidity and volatility: Does economic policy uncertainty matter? Evidence from Asian emerging economies.

This research investigates the complex interaction between liquidity and volatility while considering Economic Policy Uncertainty (EPU) as a moderating factor. Using a comprehensive dataset that incorporates various liquidity measures such as market resilience, depth, and breadth, the study examines how changes in liquidity impact volatility in four Asian incipient economies: China, Pakistan, India, and South Korea. By utilizing sophisticated econometric techniques, particularly the System Generalized Method of Moment (GMM), the findings demonstrate a statistically significant inverse relationship between liquidity and volatility. These findings imply that, within the Asian context, lower levels of volatility are correlated with higher market liquidity. By incorporating EPU into the model, the research acknowledges the significant role of economic factors in shaping market dynamics. Stakeholders, decision-makers, and investors can gain valuable insights from this analysis of variables influencing market stability in Asian emerging economies. The study's outcomes can guide policymakers in formulating strategies that promote market stability and improve market microstructure.

Correlation between uncertainty stress and depression among healthcare professionals in China: a nationwide cross-sectional survey.

OBJECTIVE: This study investigated the correlation between uncertainty stress (US) and depression among healthcare professionals (HCPs) in China. DESIGN, SETTINGS AND PARTICIPANTS: A cross-sectional online survey was conducted by recruiting HCPs from three provinces in China (central, eastern and western) through purposive sampling between 29 September 2022 and 18 January 2023. US was measured using the Life Stress Questionnaire and depression was measured using the Patient Health Questionnaire-9. In total, 2976 questionnaires were deemed valid. PRIMARY AND SECONDARY OUTCOME: This study examined the prevalence of US and depression among HCPs in China; the correlating sociodemographic traits; and the correlation between US and depression. RESULTS: The prevalence of US and depression among HCPs in China was 26.54% (790 out of 2976) and 71.63% (2132 out of 2976). Binary logistic analysis revealed that individuals with graduate degrees (OR: 1.83; 95% CI 1.07 to 3.11; p<0.05), central China (OR: 1.75; 95% CI 1.36 to 2.24; p<0.01), primary medical institutes (OR: 1.33; 95% CI 1.03 to 1.72; p<0.05), secondary medical institutes (OR: 1.30; 95% CI 1.01 to 1.68; p<0.05), an annual income of less than ¥50 000 (OR: 1.85; 95% CI 1.26 to 2.73; p<0.01) and an income range of ¥50 000-¥99 999 (OR: 1.49; 95% CI 1.10 to 2.03; p<0.05) were associated with a higher likelihood of US. The adjusted logistic regression model demonstrated that HCPs with higher US had a greater likelihood of depression (adjusted OR: 5.02; 95% CI 3.88 to 6.50; p<0.01). The increase in the US score was paralleled by an increased depression score (beta (B): 1.32; 95% CI 1.25 to 1.39; p<0.01). CONCLUSION: These findings reveal a significant correlation between US and depression among HCPs and suggest that improving the management of US may help reduce the prevalence of depression among HCPs.

Influence of Illness Uncertainty on Health Behavior in Individuals with Coronary Artery Disease: A Path Analysis.

PURPOSE: This study aimed to investigate the influence of uncertainty-related factors on the health behavior of individuals with coronary artery disease (CAD) based on Mishel's uncertainty in illness theory (UIT). METHODS: We conducted a cross-sectional study and path analysis to investigate uncertainty and factors related to health behavior. The study participants were 228 CAD patients who visited the outpatient cardiology department between September 2020 and June 2021. We used SPSS 25.0 and AMOS 25.0 software to analyze the data. RESULTS: The final model demonstrated a good fit with the data. Eleven of the twelve paths were significant. Uncertainty positively affected danger and negatively affected self-efficacy and opportunity. Danger had a positive effect on perceived risk. Opportunity positively affected social support, self-efficacy, perceived benefit and intention, whereas it negatively affected perceived risk. Social support, self-efficacy, perceived benefit and intention had a positive effect on health behavior. We found that perceived benefit and intention had the most significant direct effects, whereas self-efficacy indirectly affected the relationship between uncertainty and health behavior. CONCLUSION: The path model is suitable for predicting the health behavior of CAD patients who experience uncertainty. When patients experience uncertainty, interventions to increase their self-efficacy are required first. Additionally, we need to develop programs that quickly shift to appraisal uncertainty as an opportunity, increase perceived benefits of health behavior, and improve intentions.

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions.

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

Large environmental changes reduce valence-dependent belief updating.

When updating beliefs, humans tend to integrate more desirable information than undesirable information. In stable environments (low uncertainty and high predictability), this asymmetry favors motivation towards action and perceived self-efficacy. However, in changing environments (high uncertainty and low predictability), this process can lead to risk underestimation and increase unwanted costs. Here, we examine how people (n = 388) integrate threatening information during an abrupt environmental change (mandatory quarantine during the COVID-19 pandemic). Given that anxiety levels are associated with the magnitude of the updating belief asymmetry; we explore its relationship during this particular context. We report a significant reduction in asymmetrical belief updating during a large environmental change as individuals integrated desirable and undesirable information to the same extent. Moreover, this result was supported by computational modeling of the belief update task. However, we found that the reduction in asymmetrical belief updating was not homogeneous among people with different levels of Trait-anxiety. Individuals with higher levels of Trait-anxiety maintained a valence-dependent updating, as it occurs in stable environments. On the other hand, updating behavior was not associated with acute anxiety (State-Anxiety), health concerns (Health-Anxiety), or having positive expectations (Trait-Optimism). These results suggest that highly uncertain environments can generate adaptive changes in information integration. At the same time, it reveals the vulnerabilities of individuals with higher levels of anxiety to adapt the way they learn.

Global Governance for Personalized Medicine, Politics of Uncertainty, and Pierre Bourdieu.

How we choose to respond to uncertainty matters for robust and responsible science. New laws and consensus reports are popular instruments for global governance of emerging technology and attendant uncertainty. However, the sociologist Pierre Bourdieu noted that "[t]he judicial situation operates like a neutral space that neutralizes the stakes in any conflict through the de-realization and distancing implicit in the conversion of a direct struggle between parties into a dialogue between mediators." Put in other words, while law and legal modes of reasoning are certainly useful for conflict resolution and closure, their overprivileging in emerging technology and uncertainty governance can potentially bring about depoliticization by transforming the struggles and dissent necessary for democratic governance into a "dialogue between mediators." Hence, the critical sociological gaze offered by Bourdieu is particularly relevant for democratization of global governance of multiomics technologies and timely with the current uptake of personalized medicine. For example, in May 2023, the Romanian government introduced a law to give patients the right to personalized medicine. Personalized medicine is related to the larger umbrella concept and field of theranostics, the fusion of therapeutics and diagnostics. It is therefore timely to reflect on a "right for theranostics in planetary health," considering the potential for future pandemics and ecological crises in the 21st century. Rather than forcing consensus or convergence in an innovation ecosystem, dissent grounded in rigorous political theory, sociology of law and critical legal studies can strengthen democratization and global governance for personalized medicine and multiomics technologies.

Uncertainty quantification of the lattice Boltzmann method focussing on studies of human-scale vascular blood flow.

Uncertainty quantification is becoming a key tool to ensure that numerical models can be sufficiently trusted to be used in domains such as medical device design. Demonstration of how input parameters impact the quantities of interest generated by any numerical model is essential to understanding the limits of its reliability. With the lattice Boltzmann method now a widely used approach for computational fluid dynamics, building greater understanding of its numerical uncertainty characteristics will support its further use in science and industry. In this study we apply an in-depth uncertainty quantification study of the lattice Boltzmann method in a canonical bifurcating geometry that is representative of the vascular junctions present in arterial and venous domains. These campaigns examine how quantities of interest-pressure and velocity along the central axes of the bifurcation-are influenced by the algorithmic parameters of the lattice Boltzmann method and the parameters controlling the values imposed at inlet velocity and outlet pressure boundary conditions. We also conduct a similar campaign on a set of personalised vessels to further illustrate the application of these techniques. Our work provides insights into how input parameters and boundary conditions impact the velocity and pressure distributions calculated in a simulation and can guide the choices of such values when applied to vascular studies of patient specific geometries. We observe that, from an algorithmic perspective, the number of time steps and the size of the grid spacing are the most influential parameters. When considering the influence of boundary conditions, we note that the magnitude of the inlet velocity and the mean pressure applied within sinusoidal pressure outlets have the greatest impact on output quantities of interest. We also observe that, when comparing the magnitude of variation imposed in the input parameters with that observed in the output quantities, this variability is particularly magnified when the input velocity is altered. This study also demonstrates how open-source toolkits for validation, verification and uncertainty quantification can be applied to numerical models deployed on high-performance computers without the need for modifying the simulation code itself. Such an ability is key to the more widespread adoption of the analysis of uncertainty in numerical models by significantly reducing the complexity of their execution and analysis.

Parameter uncertainty analysis of the equivalent lung dose coefficient for the intake of radon in mines: A review.

Radon presents significant health risks due to its short-lived progeny. The evaluation of the equivalent lung dose coefficient is crucial for assessing the potential health effects of radon exposure. This review focuses on the uncertainty analysis of the parameters associated with the calculation of the equivalent lung dose coefficient attributed to radon inhalation in mines. This analysis is complex due to various factors, such as geological conditions, ventilation rates, and occupational practices. The literature review systematically examines the sources of radon and its health effects among underground miners. It also discusses the human respiratory tract model used to calculate the equivalent lung dose coefficient and the associated parameters leading to uncertainties in the calculated lung dose. Additionally, the review covers the different methodologies employed for uncertainty quantification and their implications on dose assessment. The text discusses challenges and limitations in current research practices and provides recommendations for future studies. Accurate risk assessment and effective safety measures in mining environments require understanding and mitigating parameter uncertainties.

Reinforcement learning for decision-making under deep uncertainty.

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China.

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Decision-making under conditions of explicit risk and uncertainty in autistic and typically developing adolescents and young adults.

Adolescence has been characterized as a period of risky and possibly suboptimal decision-making, yet the development of decision-making in autistic adolescents is not well understood. To investigate decision-making in autism, we evaluated performance on 2 computerized tasks capturing decision-making under explicit risk and uncertainty in autistic and non-autistic adolescents/young adults ages 12-22 years. Participants completed the Game of Dice Task (32 IQ-matched participant pairs) to assess decision-making under explicit risk and the modified Iowa Gambling Task (35 IQ-matched pairs) to assess decision-making under uncertainty. Autistic participants overall made riskier decisions than non-autistic participants on the Game of Dice Task, and the odds of making riskier decisions varied by age and IQ. In contrast, the autistic group showed comparable levels of learning over trial blocks to the non-autistic group on the modified Iowa Gambling Task. For both tasks, younger autistic participants performed poorer than their non-autistic counterparts, while group differences diminished in older ages. This age-related pattern suggests positive development during adolescence on risk assessment and decision-making in autism but also implies differential developmental trajectories between groups. These findings also suggest differential performance by the risk type, with additional complex influences of IQ and fluid cognition, which warrants further investigations.

The perpetual need of randomized clinical trials: challenges and uncertainties in emulating the REDUCE-AMI trial.

Trial emulations in observational data analyses can complement findings from randomized clinical trials, inform future trial designs, or generate evidence when randomized studies are not feasible due to resource constraints and ethical or practical limitations. Importantly, trial emulation designs facilitate causal inference in observational data analyses by enhancing counterfactual thinking and comparisons of real-world observations (e.g. Mendelian Randomization) to hypothetical interventions. In order to enhance credibility, trial emulations would benefit from prospective registration, publication of statistical analysis plans, and subsequent prospective benchmarking to randomized clinical trials prior to their publication. Confounding by indication, however, is the key challenge to interpreting observed intended effects of an intervention as causal in observational data analyses. We discuss the target trial emulation of the REDUCE-AMI randomized clinical trial (ClinicalTrials.gov ID NCT03278509; beta-blocker use in patients with preserved left ventricular ejection fraction after myocardial infarction) to illustrate the challenges and uncertainties of studying intended effects of interventions without randomization to account for confounding. We furthermore directly compare the findings, statistical power, and clinical interpretation of the results of the REDUCE-AMI target trial emulation to those from the simultaneously published randomized clinical trial. The complexity and subtlety of confounding by indication when studying intended effects of interventions can generally only be addressed by randomization.

How Chronic Pain Patients' and Physicians' Communication Influences Patients' Uncertainty: A Pre- and Post-Consultation Study.

Chronic pain is a health problem that is difficult to diagnose, treat, and manage, partly owing to uncertainty surrounding ambiguous causes, few treatment options, and frequent misunderstandings in clinical encounters. Pairing uncertainty management theory with medical communication competence, we predicted that both physicians and patients are influential to patients' uncertainty appraisals and uncertainty management. We collected pre- and post-consultation data from 200 patients with chronic neck and spine/back pain and their physicians. Patients' reports of their physician's communication were a consistent predictor of their post-consultation uncertainty outcomes. Physicians' reports of both their own and patients' communication competence were associated with patients' positive uncertainty appraisals. Physicians' reports of patients' communication competence were also associated with reductions in patients' uncertainty. Findings illustrate how both interactants' perceptions of communication competence-how they view their own (for physicians) and the other's-are associated with patients' post-consultation outcomes.