Uncertainties experienced by nursing professionals who contracted Covid-19: a priori, everyone is human.

OBJECTIVE: To analyze the uncertainties experienced by nursing professionals who contracted COVID-19. METHODS: This qualitative research was conducted with 20 nursing professionals who fell ill from COVID-19. Data collection was carried out through semi-structured interviews; the data were then organized using thematic analysis and discussed in the context of Merle Mishel's Reconceptualized of Uncertainty in Illness Theory. RESULTS: The antecedents of the disease had a strong influence on how nursing professionals who contracted COVID-19 perceived uncertainty. The media coverage of the increasing number of cases, the collapse of the healthcare system, and the high mortality rate contributed to associating the disease with fear and panic. FINAL CONSIDERATIONS: Viewing it from the perspective of the disease's antecedents, the illness of a nursing professional from COVID-19 underscores that before being professionals, they are human beings just like anyone else, undergoing adversities and facing the possibilities associated with being ill.

Psychological resilience in the relationship between family function and illness uncertainty among family members of trauma patients in the intensive care unit.

BACKGROUND: Severe trauma accounts for a main factor inducing mortality for individuals aged < 45 years in China, which requires admission to intensive care unit (ICU) to receive comprehensive treatment. Family members of patients with unanticipated and life-threatening trauma during their ICU stays often experience psychosocial distress due to illness uncertainty. Previous research has shown that family function and psychological resilience are associated with illness uncertainty, respectively. However, little is known about the current situation and interacting mechanism between family function, psychological resilience, and illness uncertainty of family members for ICU trauma patients. Therefore, this study focused on exploring the current situation and relationships between these three factors in family members for ICU trauma patients. METHODS: The convenience sampling approach was adopted in the present cross-sectional survey, which involved 230 family members for ICU trauma patients from 34 hospitals in Chongqing, China. Related data were extracted with self-reporting questionnaires, which included sociodemographic characteristic questionnaire, the Family Adaptability, Partnership, Growth, Affection and Resolve Scale (APGAR), the 10-item Connor-Davidson Resilience Scale (10-CD-RISC) and the Mishel's Illness Uncertainty Scale for Family Members (MUIS-FM). Pearson correlation analysis was conducted to examine the correlations between various variables. Additionally, a structural equation model was adopted to assess the mediating effect of psychological resilience on family function and illness uncertainty. RESULTS: According to our results, family members for ICU trauma patients experienced high illness uncertainty with moderate family dysfunction and low psychological resilience. Family function directly affected illness uncertainty and indirectly affected illness uncertainty through psychological resilience in family members of ICU trauma patients. CONCLUSIONS: Family function and psychological resilience are the protective factors for reducing illness uncertainty. Healthcare providers should take effective measures, including family-functioning improvement and resilience-focused interventions, for alleviating illness uncertainty in family members of ICU trauma patients.

Staying isolated indoors means that nobody sees me": ontological (in)security and living with significant appearance concerns before, during, and 'since' COVID-19.

PURPOSE: Though a worldwide period of uncertainty (COVID-19) has 'ended', there exists a legacy of maladaptive experiences among people with significant appearance concerns (SAC) that requires care and attention. METHODS: Using Giddens' concept of ontological security, we explored how people experienced their SAC before, during and "since" COVID-19. Qualitative surveys allowed us to capture diverse perspectives from individuals transnationally, analysed with deductive reflexive thematic analysis using ontological security as our theoretical foundation. RESULTS: Themes named "More Mirror(ed) Time" and "Locked Out, Shut Down, and Shut Out" gave a contextual grounding for the embodied experiences of this group through times of social restrictions, and the theme "Redefining Relevance" explored the continued legacy of COVID-19 - and continued global uncertainties such as economic hardship and warfare - that impact the wellbeing of people with SAC. CONCLUSIONS: People with SAC are still 'locked out' from essential healthcare support as those providing healthcare are overworked, under-resourced and rely on efficient interactive methods such as tele-health that may be triggers for people with SAC. Care providers may consider expanding appearance concerns verbiage, look to involve trusted others in the care-seeking process, and utilize modalities beyond digital health to support people with SAC.

Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management.

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Exploring patient experiences of surveillance for pancreatic cystic neoplasms: a qualitative study.

BACKGROUND: Pancreatic cystic neoplasms (PCN) are considered premalignant conditions to pancreatic adenocarcinoma with varying degrees of cancerous potential. Management for individuals who do not require surgical treatment involves surveillance to assess for cancerous progression. Little is known about patients' experience and the impact of living with surveillance for these lesions. AIMS: To explore the experiences of patients living with surveillance for PCNs. METHODS: Semi-structured qualitative interviews were conducted with patients under surveillance for pancreatic cystic neoplasms in the UK. Age, gender, time from surveillance and surveillance method were used to purposively sample the patient group. Data were analysed using reflexive thematic analysis. RESULTS: A PCN diagnosis is incidental and unexpected and for some, the beginning of a disruptive experience. How patients make sense of their PCN diagnosis is influenced by their existing understanding of pancreatic cancer, explanations from clinicians and the presence of coexisting health concerns. A lack of understanding of the diagnosis and its meaning for their future led to an overarching theme of uncertainty for the PCN population. Surveillance for PCN could be seen as a reminder of fears of PCN and cancer, or as an opportunity for reassurance. CONCLUSIONS: Currently, individuals living with surveillance for PCNs experience uncertainty with a lack of support in making sense of a prognostically uncertain diagnosis with no immediate treatment. More research is needed to identify the needs of this population to make improvements to patient care and reduce negative experiences.

Quantifying uncertainty in trans-membrane stresses and moments in simulation.

The lateral stress profile of a lipid bilayer constitutes a valuable link between molecular simulation and mesoscopic elastic theory. Even though it is frequently calculated in simulations, its statistical precision (or that of observables derived from it) is often left unspecified. This omission can be problematic, as uncertainties are prerequisite to assessing statistical significance. In this chapter, we provide a comprehensive yet accessible overview of the statistical error analysis for the lateral stress profile. We detail two relatively simple but powerful techniques for generating error bars: block-averaging and bootstrapping. Combining these methods allows us to reliably estimate uncertainties, even in the presence of both temporal and spatial correlations, which are ubiquitous in simulation data. We illustrate these techniques with simple examples like stress moments, but also more complex observables such as the location of stress profile extrema and the monolayer neutral surface.

Uncertainty and Communication Preferences Among Patients Undergoing Lung Transplant Evaluation: A Mixed-Methods Study.

OBJECTIVE: Higher uncertainty is associated with poorer quality of life and may be impacted by clinician communication about the future. We determined how patients undergoing lung transplant evaluation experience uncertainty and communication about the future from clinicians. METHODS: We performed a convergent parallel mixed-methods study using a cross-sectional survey and semistructured interviews. Patients undergoing lung transplant evaluation at the University of Colorado and the University of Washington answered questions about future communication and completed the Mishel Uncertainty in Illness Scale-Adult (MUIS-A; range 33-165, higher scores indicate more uncertainty). Interviews were analyzed using content analysis. Integration of survey and interview results occurred during data interpretation. RESULTS: A total of 101 patients completed the survey (response rate: 47%). Twelve survey participants completed interviews. In the survey, most patients identified changing family roles as important (76%), which was infrequently discussed with clinicians (31%). Most patients (86%) worried about the quality of their life in the future, and 74% said that not knowing what to expect in the future prevented them from making plans. The mean MUIS-A score was 85.5 (standard deviation 15.3). Interviews revealed three themes: (1) uncertainty of the future distresses participants; (2) participants want practical information from clinicians; and (3) communication preferences vary among participants. CONCLUSION: Participants experienced distressing uncertainty and wanted information about the future. Communication topics that were important to participants were not always addressed by physicians. Clinicians should address how chronic lung disease and lung transplant can directly impact patients' lives and support patients to cope with uncertainty.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Objective.Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model's output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termedLocal Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation.Approach.This method leverages a trained model's localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood.Main results.(1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p< 0.05 vsp> 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%.Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing.

BACKGROUND: Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering sequence homology and extreme polymorphism of HLA genes. RESULTS: We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers. CONCLUSIONS: Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io .

Examining the role of pre-visit anxiety on patient uncertainty and breast cancer patient-provider communication.

OBJECTIVE: Effective communication between cancer patients and providers is critical for addressing psychological distress, reducing uncertainty, and promoting patient well-being. This is particularly relevant during medical appointments that may elicit uncertainty, such as surgical consultations for newly diagnosed women with breast cancer. This study aimed to evaluate how pre-appointment anxiety and illness uncertainty affect patient-provider communication in breast cancer surgical consultations and subsequent post-appointment well-being. Breast cancer patient anxiety has been studied as an outcome of provider communication, though less is known about the extent to which preexisting anxiety or uncertainty act as antecedents to effective patient-provider communication. METHODS: This study analyzed videorecorded breast cancer surgical consultations (N = 51) and corresponding patient surveys to understand how pre-appointment anxiety influences pre-appointment patient uncertainty, patient-provider communication during the appointment, and subsequent post-appointment uncertainty. RESULTS: The proposed model achieved good fit to the data such that more pre-appointment anxiety was associated with more pre-appointment uncertainty, more pre-appointment anxiety was associated with more empathic opportunities per minute, and more empathic opportunities were associated with less post-appointment uncertainty. CONCLUSIONS: Results indicate breast cancer patients with anxiety pre-appointment are at-risk for more illness uncertainty and are more likely to explicitly provide empathic opportunities. This supports the need for added attention to empathic opportunities to not only address patients emotionally but to also assess whether a patient may be at higher risk of having preexisting anxiety.

Uncertainty quantification for the random HIV dynamical model driven by drug adherence.

In HIV drug therapy, the high variability of CD4+ T cells and viral loads brings uncertainty to the determination of treatment options and the ultimate treatment efficacy, which may be the result of poor drug adherence. We develop a dynamical HIV model coupled with pharmacokinetics, driven by drug adherence as a random variable, and systematically study the uncertainty quantification, aiming to construct the relationship between drug adherence and therapeutic effect. Using adaptive generalized polynomial chaos, stochastic solutions are approximated as polynomials of input random parameters. Numerical simulations show that results obtained by this method are in good agreement, compared with results obtained through Monte Carlo sampling, which helps to verify the accuracy of approximation. Based on these expansions, we calculate the time-dependent probability density functions of this system theoretically and numerically. To verify the applicability of this model, we fit clinical data of four HIV patients, and the goodness of fit results demonstrate that the proposed random model depicts the dynamics of HIV well. Sensitivity analyses based on the Sobol index indicate that the randomness of drug effect has the greatest impact on both CD4+ T cells and viral loads, compared to random initial values, which further highlights the significance of drug adherence. The proposed models and qualitative analysis results, along with monitoring CD4+ T cells counts and viral loads, evaluate the influence of drug adherence on HIV treatment, which helps to better interpret clinical data with fluctuations and makes several contributions to the design of individual-based optimal antiretroviral strategies.

Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights.

Deep learning holds great potential for expediting the discovery of new polymers from the vast chemical space. However, accurately predicting polymer properties for practical applications based on their monomer composition has long been a challenge. The main obstacles include insufficient data, ineffective representation encoding, and lack of explainability. To address these issues, we propose an interpretable model called the Polymer Graph Convolutional Neural Network (PGCNN) that can accurately predict various polymer properties. This model is trained using the RadonPy data set and validated using experimental data samples. By integrating evidential deep learning with the model, we can quantify the uncertainty of predictions and enable sample-efficient training through uncertainty-guided active learning. Additionally, we demonstrate that the global attention of the graph embedding can aid in discovering underlying physical principles by identifying important functional groups within polymers and associating them with specific material attributes. Lastly, we explore the high-throughput screening capability of our model by rapidly identifying thousands of promising candidates with low and high thermal conductivity from a pool of one million hypothetical polymers. In summary, our research not only advances our mechanistic understanding of polymers using explainable AI but also paves the way for data-driven trustworthy discovery of polymer materials.

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study.

The measurement of productivity change in decision-making units (DMUs) is crucial for assessing their performance and supporting efficient decision-making processes. In this paper, we propose a new approach for measuring productivity change using the Malmquist productivity index (MPI) within the context of two-stage network data envelopment analysis (TSNDEA) under data uncertainty. The two-stage network structure represents a realistic model for DMUs in various fields, such as insurance companies, bank branches, and mutual funds. However, traditional DEA models do not adequately address the issue of data uncertainty, which can significantly impact the accuracy of productivity measurements. To address this limitation, we integrate the MPI methodology with an uncertain programming framework to tackle data uncertainty in the productivity change measurement process. Our proposed approach enables the evaluation of productivity change by capturing both technical efficiency and technological progress over time. By incorporating fuzzy mathematical programming into the DEA framework, we model the inherent uncertainty in input and output data more effectively, enhancing the robustness and reliability of productivity measurements. The utilization of the proposed approach provides decision-makers with a comprehensive analysis of productivity change in DMUs, allowing for better identification of efficiency improvements or potential areas for enhancement. The findings from our study can enhance the decision-making process and facilitate more informed resource allocation strategies in real-world applications.

Dynamic adaptive decision support for strategic decision-making in healthcare organizations.

PURPOSE: The main objective of this study was to design a dynamic adaptive decision support model for healthcare organizations facing deep uncertainties by considering promising dynamic adaptive approaches. The main argument for this is that healthcare organizations have to make strategic decisions under deep uncertainty, but lack an approach to deal with this. DESIGN/METHODOLOGY/APPROACH: A Dynamic Adaptive Decision Support model (DADS) is designed using the Design Science Research methodology. The evaluation of an initial model leads, through two case studies on ongoing and strategic decision-making, to the final design of this needed model for healthcare organizations. FINDINGS: The research reveals the relevance of the designed dynamic and adaptive tool to support strategic decision-making for healthcare organizations. The final design of DADS innovates Decision Making under Deep Uncertainty (DMDU) approaches in an organizational context for ongoing and strategic decision-making. ORIGINALITY/VALUE: The designed model applies the Dynamic Adaptive Policy Pathways approach in an organizational context and more specifically in health care organizations. It further integrates Corporate Real Estate Management knowledge and experience to develop a most needed tool for decision-makers in healthcare. This is the first DADS designed for an organization facing deep uncertainties in a rapidly changing healthcare environment and dealing with ongoing and strategic decision-making.

The evolution of social behaviors and risk preferences in settings with uncertainty.

Humans update their social behavior in response to past experiences and changing environments. Behavioral decisions are further complicated by uncertainty in the outcome of social interactions. Faced with uncertainty, some individuals exhibit risk aversion while others seek risk. Attitudes toward risk may depend on socioeconomic status; and individuals may update their risk preferences over time, which will feedback on their social behavior. Here, we study how uncertainty and risk preferences shape the evolution of social behaviors. We extend the game-theoretic framework for behavioral evolution to incorporate uncertainty about payoffs and variation in how individuals respond to this uncertainty. We find that different attitudes toward risk can substantially alter behavior and long-term outcomes, as individuals seek to optimize their rewards from social interactions. In a standard setting without risk, for example, defection always overtakes a well-mixed population engaged in the classic Prisoner's Dilemma, whereas risk aversion can reverse the direction of evolution, promoting cooperation over defection. When individuals update their risk preferences along with their strategic behaviors, a population can oscillate between periods dominated by risk-averse cooperators and periods of risk-seeking defectors. Our analysis provides a systematic account of how risk preferences modulate, and even coevolve with, behavior in an uncertain social world.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties.

Goal-directed navigation requires continuously integrating uncertain self-motion and landmark cues into an internal sense of location and direction, concurrently planning future paths, and sequentially executing motor actions. Here, we provide a unified account of these processes with a computational model of probabilistic path planning in the framework of optimal feedback control under uncertainty. This model gives rise to diverse human navigational strategies previously believed to be distinct behaviors and predicts quantitatively both the errors and the variability of navigation across numerous experiments. This furthermore explains how sequential egocentric landmark observations form an uncertain allocentric cognitive map, how this internal map is used both in route planning and during execution of movements, and reconciles seemingly contradictory results about cue-integration behavior in navigation. Taken together, the present work provides a parsimonious explanation of how patterns of human goal-directed navigation behavior arise from the continuous and dynamic interactions of spatial uncertainties in perception, cognition, and action.

Intolerance of Uncertainty, Job Satisfaction and Work Performance in Turkish Healthcare Professionals: Mediating Role of Psychological Capital.

Objective: Psychological capital refers to internal resources including self-efficacy, hope, optimism and resilience to overcome adverse life events. The current study sought to examine the mediating role of psychological capital in the relationship between intolerance of uncertainty and job satisfaction and work performance in healthcare professionals. Methods: Participants were 302 healthcare professionals [48% females; M(SD) age = 34.0 (7.5)] and completed measures of intolerance of uncertainty, psychological capital, work performance, and job satisfaction. Results: The findings indicated that intolerance of uncertainty was negatively correlated with psychological capital, work performance, and job satisfaction, whereas psychological capital was positively correlated with job satisfaction and work performance. More importantly, the findings revealed that these relationships were mediated by psychological capital. Conclusion: The results provide several contributions that help to understand the role of psychological capital in the relationship between intolerance to uncertainty and job satisfaction and work performance.

Uncertainty in banking and debt financing of firms in Vietnam.

This study explores the effects of banking uncertainty on firms' debt financing. Employing data from 2007 to 2022 of Vietnam-a bank-based economy, we document that banking uncertainty negatively impacts corporate debt. The impact firmly holds across various debt maturities and sources, with the most predominant driver witnessed in bank debt. We also investigate the potential underlying mechanism linking banking uncertainty to debt financing, thereby validating the working of three crucial channels, including increased costs of debt, substitution of trade credit, and contractions in firm investment. Furthermore, conducting extended analysis, we find that debt financing exhibits more pronounced reactions to banking uncertainty for firms with closer ties to banks or during macroeconomic shocks, as captured by the financial crisis and the COVID-19 pandemic. Our findings survive after robustness checks by alternative measurement, static and dynamic econometric models, and endogeneity controls.