Uncertainty Appraisal Research Articles

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

Development of Theory of Uncertainty on COVID-19: Theory Derivation Based on Uncertainty in Illness Theory

Purpose: The purpose of this study is to develop the Theory of Uncertainty on COVID-19 using the strategy of theory derivation. Methods: Theory derivation was carried out in the following steps: review the literature to explore the phenomena related to uncertainty on COVID-19; select a parent theory that provides valuable concepts and a useful structure for derivation, and identify the concepts and structure of the parent theory to use in derivation; modify and redefine the concepts and structure of the parent theory to create a derived theory. In the literature review process, relevant findings were synthesized to support the propositions of the derived theory. Results: The Theory of Uncertainty on COVID-19 was derived from the Uncertainty in Illness Theory to make it relevant and applicable to a specific aspect of uncertainty on COVID-19, health-related uncertainty perceived by a person who has not contracted COVID-19. It is a middle-range theory targeting the general population and consists of a linear and unidirectional model centered on three themes: antecedents of uncertainty, appraisal of uncertainty, and coping with uncertainty. Conclusion: The Theory of Uncertainty on COVID-19 will be able to contribute to efforts to manage perceived uncertainty on pandemic diseases and improve individual biopsychosocial health in the future.

Spectral induced polarization tomography inversion: Hybridizing homotopic continuation with Bayesian inversion

Induced polarization tomography offers the potential to better characterize the subsurface structures by considering spectral content from data acquisition over a broad frequency range. Spectral induced polarization (SIP) tomography is generally defined as a nonlinear inverse problem commonly solved through deterministic gradient-based methods. To this end, the spectral parameters, i.e., direct current resistivity, chargeability, relaxation time, and frequency exponent, are resolved by individually or simultaneously inverting all frequency data, followed by fitting a generalized Cole-Cole model to the inverted complex resistivities. Due to the high correlation between the Cole-Cole model parameters and a lack of knowledge about the initial approximation of the spectral parameters, using the classical least-squares methods may lead to inaccurate solutions and impede reliable uncertainty analysis. To cope with these limitations, we introduce a new approach based on a hybrid application of a globally convergent homotopic continuation method and a Bayesian inference to reconstruct the distribution of the subsurface spectral parameters. The homotopic optimization, owing to its fast and global convergence, is first implemented to invert multifrequency SIP data sets aimed at retrieving the complex-valued resistivity. Then, Bayesian inversion based on a Markov chain Monte Carlo (MCMC) sampling method, along with a priori information including the lower and upper bounds of the prior distributions, is used to invert the complex resistivity for the Cole-Cole model parameters. By applying the MCMC inversion algorithm, a full nonlinear uncertainty appraisal can be provided. We numerically evaluate the performance of our method using synthetic and real data examples in the presence of topographical effects. Numerical results prove that the homotopic continuation method outperforms the classic, smooth inversion algorithm in the sense of approximation accuracy and computational efficiency. In addition, we determine that our hybrid inversion strategy provides reliable representations of the main features and structure of the earth’s subsurface in terms of the spectral parameters.

The relationship between uncertainty in illness and psychological adjustment to chronic illness.

Using Mishel's Uncertainty in illness theory, this integrative review sought to identify how uncertainty in illness and psychological adjustment to illness are associated with chronic illness to guide development of interventions to support psychological adjustment. A search was conducted via EBSCOHost to answer the question "What is the relationship between uncertainty in illness and psychological adjustment to chronic illness?" Two key factors were identified as having an impact on psychological adjustment to chronic illness in the presence of uncertainty in illness including uncertainty appraisal and the type of coping strategies used in response to the appraisal. Individuals with a chronic illness diagnosis are more likely to experience increased levels of uncertainty in illness and decreased psychological adjustment to illness. A better understanding of these two concepts is important to augment and or guide the development of interventions to support psychological adjustment.

Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies

Abstract Recent metaheuristic approaches are extensively and intensively being implemented to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a newly established metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After recognizing the modal features of the objective function tailored and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty appraisal tests have been further implemented to comprehend the reliability of solutions obtained. The solutions retrieved by HGS have been unbiasedly compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly used particle swarm optimization algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, stably, robustly, and coherently with the available geophysical, geological, and borehole verification, as HGS is able to better explore the model space without compromising its capability to efficiently approach the global minimum. This novel global optimization method can thus be considered as a promising tool in geothermal energy investigations and the study of volcanic activities.

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.

How do you happen to feel? The effect of certainty–uncertainty appraisals of incidental emotions on consumer affective forecasting error

AbstractConsumers frequently forecast how their choices will make them feel in future consumption situations. One possible influence during such affective forecasting is their current emotional state. Although the impact of incidental emotions on consumer decision making is well established, limited research has examined the impact of incidental emotions on affective forecasting. The purpose of the present research is to contribute to the understanding of the effects of certainty–uncertainty, as a key appraisal dimension of incidental emotions, on the process and outcome of affective forecasting. The results of four experimental studies demonstrate that experiencing uncertainty associated incidental emotions, such as fear and hope, during affective forecasting, leads to smaller forecasting error compared with experiencing certainty emotions, such as anger and happiness. Furthermore, heuristic processing, as a result of the certainty versus uncertainty appraisals, plays a mediating role in the effect of certainty–uncertainty on forecasting error. The findings of the present research extend the effects of the certainty–uncertainty appraisals in the context of consumer affective forecasting and elucidate the impact of incidental emotions on decision making.

Refining Mishel's Reconceptualized Uncertainty in Illness Theory: Explaining the Development of Uncertainty in Chronic Illness.

Background and Purpose: Mishel's Reconceptualized Uncertainty in Illness Theory describes the changed, more positive appraisal of uncertainty over time in a chronic disease. Therefore, Mishel referred to "probabilistic thinking" and "self-organization." The description of these concepts remained highly abstract, limiting the understanding of how change of uncertainty comes about. We aimed to elaborate on this gap and at refining the theory. Methods: We conducted a study consisting of three parts: (a) concept analyses of "probabilistic thinking" and "self-organization," (b) longitudinal qualitative study to investigate uncertainty experience over time, and (c) triangulation of (a) and (b) to develop theoretical propositions. Results: We developed five theoretical propositions in syllogistic form: (a) if persons experience uncertainty, they think probabilistically to assess the existentiality of potential consequences, (b) if they expect existential consequences, they experience uncertainty as a threat, (c) if the existentiality of uncertainty diminishes, then individuals accept uncertainty as an inherent part of illness, (d) if they accept uncertainty, they cognitively reframe it in a positive way in order to promote recovery, and (e) if persons reexperience uncertainty, they reassess the existentiality of potential consequences. Implications for Practice: We propose "health belief" as a mechanism driving "cognitive reframing" to explain the interrelation between uncertainty and a more positive experience. "Existential uncertainty" offers a new perspective on preventing a change in uncertainty experience. The new concepts can provide guidance to take measures to reduce existential uncertainty and promote health beliefs to change the experience of uncertainty from a negative to a more positive one.

'I was uncertain, but I was acting on it': A longitudinal qualitative study of medical students' responses to uncertainty.

Uncertainty is ubiquitous within medical practice. Accordingly, how individuals respond to uncertainty, termed uncertainty tolerance (UT), is increasingly considered a medical graduate competency. Despite this, aspects of the UT construct are debated, which may relate to research focused on measuring UT, rather than understanding students' experiences. Therefore, we asked (1) how do medical students describe their responses to uncertainty, (2) how (if at all) do described responses change over time and (3) how do described responses contribute to understanding the UT construct. Engaging an interpretivist worldview, we conducted a longitudinal qualitative study throughout 2020 with 41 clinical medical students at an Australian medical school. Participants completed reflective diary entries across six in-semester time-points (n = 41, 40, 39, 38, 37 and 35) and semi-structured interviews at the end of both semesters (n = 20 per semester). We analysed data using framework analysis. Although participants communicated accepting health care uncertainties, described cognitive appraisals of uncertainty ranged from threatening (e.g. challenging credibility) to opportunistic (e.g. for learning and growth). Emotions in response to uncertainty were predominately described in negative terms, including worry and anxiety. Participants described a range of maladaptive and adaptive behavioural responses, including avoiding versus actively engaging with uncertainty. Despite describing typically negative emotions across time, participants' cognitive and behavioural response descriptions shifted from self-doubt and avoidance, towards acceptance of, and engagement despite uncertainty. Students' descriptions of responses to uncertainty suggest existing UT conceptualisations may not holistically reflect medical students' experiences of what it means to be uncertainty 'tolerant', especially pertaining to conceptualisations of 'tolerance' centred on emotions (e.g. stress) rather than how uncertainty is ultimately managed. Extending from this study, the field could consider redefining characteristics of uncertainty 'tolerance' to focus on adaptive cognitive and behavioural responses, rather than emotional responses as key indicators of 'tolerance'.

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

AbstractGeomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

Path analysis of illness uncertainty and psychosocial adaptation of patients with Marfan syndrome.

Marfan syndrome (MFS) is a genetic disorder that causes sudden or chronic cardiovascular problems, which can be fatal. Since MFS patients require regular close medical observation, it is important to understand the factors and pathways associated with psychosocial adaptation to the disease. This study aimed to identify the relationships among illness uncertainty, uncertainty appraisal, and psychosocial adaptation in MFS patients using path analysis. This descriptive cross-sectional survey study was conducted from October 2020 to March 2021, in compliance with STROBE guidelines. Using data from 179 participants aged older than 18 years, we constructed a hypothetical path model to identify determinants of illness uncertainty, uncertainty appraisal, and psychosocial adaptation. In the path analysis, disease severity, illness uncertainty, anxiety, and social support were significant factors influencing MFS patients' psychosocial adaptation. Disease severity and illness uncertainty exerted direct effects, while anxiety and social support exerted both direct and indirect (through illness uncertainty) effects. Finally, anxiety showed the greatest total effect. These findings are useful for enhancing MFS patients' psychosocial adaptation. Medical professionals should focus on managing disease severity, decreasing anxiety, and increasing social support.

Effects of Uncertainty, Appraisal of Uncertainty, and Self-Efficacy on the Quality of Life of Elderly Patients with Lung Cancer Receiving Chemotherapy: Based on Mishel's Theory of Uncertainty.

Background and Objectives: The purpose of this study is to enhance the quality of life in elderly patients with lung cancer by understanding relations of uncertainty, appraisal of uncertainty, self-efficacy, and quality of life targeting elderly patients with lung cancer receiving anticancer therapy, and also analyzing the factors affecting the quality of life based on Mishel's theory. Materials and Methods: The subjects were a total of 112 lung cancer patients aged 65 or older receiving anticancer therapy. The data was collected by using self-report questionnaires targeting patients in hemato-oncology at Chungbuk National University Hospital. The data were analyzed using descriptive statistics, a t-test, an analysis of variance, Pearson's correlational coefficients, and hierarchical regression analysis. Results: In stage 1, anticancer therapy (chemotherapy) (β = -0.34, p < 0.001), economic condition (low) (β = -0.30, p < 0.001), the number of anticancer therapies (three times or more) (β = -0.29, p < 0.001), and education (graduation from high school or higher) (β = 0.18, p = 0.033) were influencing factors (F = 0.52, p < 0.001). In stage 2, self-efficacy (β = 0.41, p < 0.001), appraisal of uncertainty: danger (β = -0.29, p < 0.001), appraisal of uncertainty: opportunity (β = 0.18, p = 0.018), the number of anticancer therapies (three times or more) (β = -0.17, p = 0.006), and anticancer therapy (chemotherapy) (β = -0.14, p = 0.031) were influencing factors, which showed 74.2% explanatory power (F = 26.17, p < 0.001). Conclusions: In order to improve the quality of life of subjects, it would be necessary to develop interventions for raising their self-efficacy by considering their degree of education, economic condition, the types and numbers of anticancer therapies, and understanding of the appraisal of uncertainty about the disease is assessed as an opportunity factor or a danger factor.

Anxiety, depression, and uncertainty appraisal and factors affecting uncertainty risk and opportunity appraisal of health care workers in Korea during the COVID-19 outbreak

ABSTRACT Background Due to the prolonged period of COVID-19, the uncertainty related to COVID-19 is bound to increase for healthcare workers (HCWs) in tertiary medical institutions as much as for the HCWs in dedicated hospitals. Purpose To assess anxiety, depression, and uncertainty appraisal, and to determine the factors affecting uncertainty risk and opportunity appraisal experienced by HCWs at the forefront of COVID-19 treatment. Method This was a descriptive, cross-sectional study. The participants were HCWs at a tertiary medical center in Seoul. HCWs included medical (doctors, nurses) and non-medical (nutritionists, pathologists, radiologists, office workers, etc.) personnel. Self-reported structured questionnaires (patient health questionnaire, generalized anxiety disorder scale, and uncertainty appraisal) were obtained. Finally, responses from 1337 people were used to evaluate factors affecting uncertainty risk and opportunity appraisal using a quantile regression analysis. Results The average ages of medical and non-medical HCWs were 31.69 ± 7.87 and 38.66 ± 11.42 years, and the proportion of females was high. The rates of moderate to severe depression (23.23%) and anxiety (6.83%) were higher in medical HCWs. The uncertainty risk score was higher than the uncertainty opportunity score for all the HCWs. Factors that increased uncertainty opportunity were a decrease in depression in medical HCWs and a decrease in anxiety in non-medical HCWs. Increase in age was directly proportional to uncertainty opportunity in both groups. Conclusion There is a need to devise a strategy to reduce uncertainty among HCWs who inevitably face various infectious diseases that appear in the near future. In particular, since there are various types of non-medical as well as medical HCWs in medical institutions who can prepare an intervention plan that comprehensively considers the characteristics of each occupation and the distribution of risks and opportunities of uncertainty will be able to improve the quality of life of HCWs and further promote the health of the people.

Critical review of the OSPAR risk-based approach for offshore-produced water discharges.

The management of produced water (PW) discharges from offshore oil and gas installations in the North Atlantic is under the auspices of OSPAR (Oslo/Paris convention for Protection of the Marine Environment of the North-East Atlantic). In 2010, OSPAR introduced the risk-based approach (RBA) for PW management. The RBA includes a hazard assessment estimating PW ecotoxicity using two approaches: whole-effluent toxicity (WET) and substance-based (SB). Set against the framework of the WET and SB approach, we conducted a literature review on the magnitude and cause of PW ecotoxicity, respectively, and on the challenges of estimating these. A large variability in the reported magnitude of PW WET was found, with EC50 or LC50 values ranging from <1% to >100%, and a median of 11% (n = 301). Across the literature, metals, hydrocarbons, and production chemicals were identified as causing ecotoxicity. However, this review reveals how knowledge gaps on PW composition and high sample and species dependency of PW ecotoxicity make clear identification and generalization difficult. It also highlights how limitations regarding the availability and reliability of ecotoxicity data result in large uncertainties in the subsequent risk estimates, which is not adequately reflected in the RBA output (e.g., environmental impact factors). Thus, it is recommended to increase the focus on improving ecotoxicity data quality before further use in the RBA, and that WET should play a more pronounced role in the testing strategy. To increase the reliability of the SB approach, more attention should be paid to the actual composition of PW. Bioassay-directed chemical analysis, combining outcomes of WET and SB in toxicity identification evaluations, may hold the key to identifying drivers of ecotoxicity in PW. Finally, an uncertainty appraisal must be an integrated part of all reporting of risk estimates in the RBA, to avoid mitigation actions based on uncertainties rather than reliable ecotoxicity estimations. Integr Environ Assess Manag 2023;19:1172-1187. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

An Exploratory Qualitative Study of Visitors’ Cognitive Appraisals in a Meteorological Landscape Uncertainty Scenario

The meteorological landscape has always been the main selling point for some destinations, but the uncertain supply of the meteorological landscape also poses challenges for destination management. Little is known about visitors’ emotional and behavioral tendencies elicited by this uncertainty under landscape viewing; therefore, it is critical for academics and practitioners to understand visitors’ emotional and behavioral changes after the viewing experience and strategize marketing plans to improve revisit intention. Grounded upon cognitive appraisal theory, this study conducted in-depth interviews with 31 visitors who had had meteorological landscape viewing experiences to solicit their perceptions and appraisals of uncertainty. Results revealed that the uncertainty influenced visitors’ entire cognitive appraisal process and resulted in four types of revisit intentions. Specifically, even if visitors have a negative emotion after the viewing, they may choose to revisit. Accordingly, theoretical and practical implications for destination management are offered.

Mishel's uncertainty in illness theory: Informing nursing diagnoses and care planning.

Patients with glioblastoma multiforme (GBM) face uncertainty during the illness trajectory that decreases their quality of life. Uncertainty manifests as an altered state of mind in patients with GBM and increases the burden of managing their diagnosis. The uncertainty in illness theory (UIT) can be used to guide nursing practice and create care plans to decrease this uncertainty. The UIT and conceptual model were used to pair diagnoses from NANDA-I Nursing Diagnoses to form care plans for patients with GBM. In a stepwise approach, concepts from the UIT were organized and cross-walked into an integrated theory-driven practice framework to enhance care planning. The UIT helps define and identify circumstances of uncertainty in patients experiencing illness, and Nursing Diagnoses provide engagement and outcomes for those uncertainty-based diagnoses. Some examples of the UIT guiding Nursing Diagnoses includes a patient's cognitive level guiding a Nursing Diagnosis care plan of Risk for Acute Confusion. Social Support can guide the care plan of Risk for Loneliness, and appraisal of uncertainty can guide a care plan for Ineffective Individual Coping. Using the UIT to guide theory-driven practice links interventions and outcomes in patients with GBM. The UIT can guide NANDA-I Nursing Diagnoses to assist nurses in creating a nursing care plan for nursing engagement to decrease uncertainty and increase the quality of life in patients with GBM. Theory-driven research defines the application of the UIT in GBM and provides foundational knowledge for theory-driven practice to occur.

Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT”

AbstractModels based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.

Developing a theoretical definition of self-organization: A principle-based concept analysis in the context of uncertainty in chronic illness.

AimTo develop a theoretical definition of self‐organization to increase the understanding of the Reconceptualized Uncertainty in Illness Theory (RUIT).BackgroundMishel described the change of the uncertainty appraisal over time in people with a chronic illness by means of the RUIT. Therefore, she introduced the concept of self‐organization. However, its meaning is difficult to comprehend because its descriptions remained highly abstract.DesignA principle‐based concept analysis.Data SourceEntries of lexicons and journal publications, explicitly or implicitly addressing self‐organization in the context of any social phenomenon.Review MethodsWe conducted a conceptually driven literature search in lexicons and four databases and performed citation tracking.ResultsSelf‐organization stands for a transition between psychological instability and psychological adjustment. It is conditioned by illness‐related obstacles or uncertainties that are perceived as life‐threatening. This adaptation process shows overlaps with cognitive reframing and is promoted by time, resilience, social support, and positive development of the disease. It leads to empowerment and a new perspective of life and uncertainty.ConclusionsWe enhanced the understanding of the RUIT by developing a theoretical definition of self‐organization on a lower level of abstraction and by proposing a new approximation for the operationalization by means of cognitive reframing.

Uncertainty appraisal provides useful information for the management of a manual grape harvest

This contribution presents a novel approach to characterise uncertainty in the manual grape harvest of a winery in Tuscany (Italy). After identifying the potential sources of variability arising from randomness, weather, and management options, a model to define useful output variables is built. These output variables include the discrepancy in the harvest date of the vineyards ( harvest date discrepancy) , the discrepancy in the required workforce across harvest dates (labour discrepancy), and, finally, the potential deficit of working hours throughout the grape harvest campaign (labour deficit) . The range spanned by these variables is first assessed through a Monte Carlo uncertainty analysis wherein the model is repeated approximately 16,000 times with variable combinations of the input parameters per their probability distribution. The assessed uncertainty is then apportioned to the input parameters through a global sensitivity analysis. In turn, a regional sensitivity analysis characterises the circumstances producing a deficit of working hours, which corresponds to sufferance in the grape harvest campaign. The discussed approach could be implemented in a user-friendly decision-support tool for risk characterisation and efficient grape harvest management. • We developed a model to define sensitive variables in manual grape harvest. • We characterise their uncertainty through Monte Carlo simulations. • Output uncertainty was apportioned onto inputs with a global sensitivity analysis. • Criticalities were identified and analysed through regional sensitivity analysis. • The approach proposed could support decision making in grape harvest management.

Managing Uncertainty for and With Family: Communication Strategies and Motivations in Familial Uncertainty Management for Hereditary Cancer.

The management of uncertainty is integral to health and illness. Individuals manage uncertainty about their health through communication enmeshed in family systems, but existing theorizing focuses on individuals without accounting for family processes. An iterative qualitative analysis of 42 dyadic, family interviews (N = 84) revealed (a) moments in the context of hereditary cancer that involved individual-centered and familial uncertainty appraisal and management, (b) family members' communication strategies to prompt relatives to engage familial uncertainty, and (c) the communicative (re)creation and negotiation of family models for uncertainty management. The findings illuminate tensions that individuals encounter across their lifespan as they appraise and manage uncertainty about hereditary cancer risks. This study extends uncertainty management theory to encompass familial uncertainty management and contributes insights useful for the management of hereditary cancer.