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Non-technical skills (NTS) challenges experienced by surgeons may degrade performance, ultimately impacting the safety and quality of care delivered to patients. The objectives of this work were to develop a framework for NTS coaching for surgeons and implement a coaching program utilizing the developed NTS coaching framework. Leveraging adult learning and self-determination theories, a specialty-agnostic NTS coaching framework was developed for individual coaching sessions with robotic surgeons. The framework was used to deliver NTS coaching sessions to robotic surgeons. Surgeon's robotic procedures were recorded, and expert raters assessed their NTS using the Non-Technical Skills for Surgeons tool. Measures of surgeon satisfaction, learning outcomes, and performance improvement were determined. Cohen's d statistic was used to estimate the effect size of the coaching intervention. To pilot the program, ten robotic-assisted surgeries (five pre-coaching and five post-coaching) were observed from five practicing robotic surgeons who were recruited from a large academic healthcare system. Expert raters' assessment of surgeons' NTS revealed several exemplar and non-exemplar behaviors. Surgeons were satisfied with the coaching, rating its quality very highly on all NTS dimensions. On a Likert scale of 1 (strongly disagree) to 5 (strongly agree), surgeons had a stronger agreement that the coaching could improve their situation awareness (4.0±.5) and leadership (4.8±.2) skills compared to their decision making and communication (3.8±.7). From the post-coaching observations, coaching had medium-to-large effect on situation awareness (d =.65) and leadership (d =.41), small effect on communication and teamwork (d =.14), and no effect on decision-making. Overall, the coaching intervention had a medium effect on total NTS (d =.33). We presented a quality improvement initiative to enhance the NTS of surgeons by implementing a coaching program that leverages our developed NTS framework. Recognizing the importance of NTS in surgeries, our initiative shows a commitment to continuous improvement of patient safety and quality of care. [ABSTRACT FROM AUTHOR]

The average lifespan of particles, a crucial parameter in nuclear physics, is essential for identification purposes. Modern particle detectors excel at recognizing individual radioactive nuclei arrivals and their subsequent decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radio active process. This study focuses on the meticulous surveillance of the average keV emission from partially observed events within the domain of nuclear physics. To accomplish this, the methodology employs the statistical approach known as Distance Weighted Mean (DWM), integrated with the application of censored control charts. The utilization of censored control charts allows for the effective management of incomplete data, enabling researchers to make informed decisions despite potential limitations in observation. We propose a DWM based exponentially weighted moving average-cumulative sum (DWM-EC) control chart for monitoring kiloelectron-volt E(keV) data. The proposed charts is developed for Weibull lifetimes under type-I censoring. For the construction of an efficient control charting structure, we employed the conditional median (CM) methods. The goal is to find changes in the mean of Weibull lifetimes with censored data with known and estimated parameter conditions. The performance of the proposed DWM-EC chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on E(keV) related to the alpha decays of 177 Lutetium isotope is also discussed. [ABSTRACT FROM AUTHOR]

While great methodological strides have been made in the area of decision making research, decisions that rely on subjective stimuli, such as personality traits, still pose a challenge for researchers, partly because it is difficult to define a standard of accuracy for such choices–they lack a "ground truth". In studies on value-based decisions, this same problem has been circumvented by comparing uncertain subjective decisions against a separately assessed judgment of value–a "standard". Here we apply this method in a task of verbal personality trait judgment, and show how a separately assessed standard judgment can be used to precisely control stimulus presentation and analyze subjective personality choices via the method of reverse correlation. Per trial, a series of quasi-randomly sampled adjectives was shown, which participants categorized as more descriptive of either themselves of another person well known to them. Participants also indicated their confidence in the response. Each trial's difficulty was controlled based on the previously assessed standard judgment. Analysis of the behavioral data shows several decision-general properties during these personality judgments, such as symmetrical choices, steeper choice functions for confident trials, and a positive evidence bias during confidence judgment. We discuss how these findings can shed light on the cognitive processes involved in personality perception. The task and results together may help bridge the gap between research on complex, social forms of judgment and findings on more basic decision processes. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the performance of logistic distribution centers (LDCs) by Quality Function Deployment (QFD) based on the Best-Worst Method (BWM). The originalities of this paper include: (1) exploring five constructs with 20 customer requirement attributes (CRAs) for LDCs' operations based on the SERQUAL model, (2) using the QFD model to identify five primary divisions and 18 corresponding service technical requirements (STRs) for LDCs' operations, (3) recognizing the top five STRs should be prioritized for the allocation of limited resources from House of Quality (HoQ), including cargo order (7.15%), value-added activities (6.68%), document preparation (6.31%), consolidating and assembling (6.10%), and document management (6.00%), (4) applying the Best-Worst Method (BWM) to estimate CRAs' relative weight. The proposed research model can provide a methodological reference to the relevant literature in association with logistics operations and multiple-criteria decision analysis (MCDA). [ABSTRACT FROM AUTHOR]

Risky decision-making putatively contributes to nonsuicidal self-injury (NSSI) yet empirical support for this association is inconsistent. Studies have not simulated socioemotional contexts most closely linked to NSSI, which may partially explain mixed findings. Accordingly, we examined the association between NSSI and risky decision-making following the receipt of criticism from a close other, a key interpersonal context. The study included 286 university students (Mage = 21.11) oversampled for a lifetime history of NSSI. Participants completed a modified Iowa Gambling Task, in which they chose to play or pass on "good" and "bad" decks associated with monetary gains and losses. Participants also completed the Criticism Gambling Task, which was identical to the Iowa Gambling Task except critical audio comments preceded each block of trials. Based on results of multilevel growth curve analyses, decreases in risky decision-making were steeper on the Iowa Gambling Task compared to the Criticism Gambling Task, suggesting poorer learning in the context of criticism. Further, how past-month NSSI was related to changes in risky decision-making across blocks differed between the two tasks, b = -0.004, t(3140.00) = 2.48, p =.013. On the Iowa Gambling Task, all participants decreased their risky decision-making, whereas on the Criticism Gambling Task, higher past-month NSSI frequencies were associated with riskier decision-making. Our findings support associations between risky decision-making and NSSI in negative socioemotional contexts, consistent with functional models of NSSI. [ABSTRACT FROM AUTHOR]

Humans sometimes have an insight that leads to a sudden and drastic performance improvement on the task they are working on. Sudden strategy adaptations are often linked to insights, considered to be a unique aspect of human cognition tied to complex processes such as creativity or meta-cognitive reasoning. Here, we take a learning perspective and ask whether insight-like behaviour can occur in simple artificial neural networks, even when the models only learn to form input-output associations through gradual gradient descent. We compared learning dynamics in humans and regularised neural networks in a perceptual decision task that included a hidden regularity to solve the task more efficiently. Our results show that only some humans discover this regularity, whose behaviour was marked by a sudden and abrupt strategy switch that reflects an aha-moment. Notably, we find that simple neural networks with a gradual learning rule and a constant learning rate closely mimicked behavioural characteristics of human insight-like switches, exhibiting delay of insight, suddenness and selective occurrence in only some networks. Analyses of network architectures and learning dynamics revealed that insight-like behaviour crucially depended on a regularised gating mechanism and noise added to gradient updates, which allowed the networks to accumulate "silent knowledge" that is initially suppressed by regularised gating. This suggests that insight-like behaviour can arise from gradual learning in simple neural networks, where it reflects the combined influences of noise, gating and regularisation. These results have potential implications for more complex systems, such as the brain, and guide the way for future insight research. Author summary: Insights, or aha-moments, are a remarkable phenomenon in human cognition that is unique in a number of ways: they are accompanied by a powerful subjective experience, occur abruptly after an unpredictable period of having been stuck on a problem, and for some arise never. But are insights harbingers of a unique mode of learning that only appears in the highest of animals? We show that insight- like behaviours can occur even in the simplest neural networks trained with regular gradient descent techniques. Human and machine behaviour was compared on the same decision making task which included a hidden regularity. Neural networks with L1-regularised gate modulation closely mimic the key behavioural characteristics that we identified in humans. An analysis of the insight networks showed that noise and regularisation played an important part in bringing about insight-like behaviour, besides being preceded by "silent knowledge" that is initially suppressed by gating. Our results shed new light on the computational origins of insights and suggest that they can arise from gradual learning mechanisms. [ABSTRACT FROM AUTHOR]

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles. Author summary: Since the founding of psychology as a scientific discipline, scientists have recognized that much of human and animal behavior falls into two separate categories: slow, effortful deliberation (e.g., solving math problems) and fast, automatic habitual action selection (e.g., signing your name). Such "dual process" theories of cognition have since been developed to explain a number of phenomena from across psychology and neuroscience. Despite all of these experimental results, understanding why this pattern in behavior exists has proven elusive. In this work, we develop a model of dual process cognition which can be derived independently from experimental observations about the brain. Our theory proposes that the division between effortful and automatic behavior emerges as a natural result of the need to balance mastery of the tasks we have to complete every day and flexibility to adapt to new circumstances. In a large number of computer simulations, our model captures many behavioral phenomena associated with "dual process" cognition across multiple subfields of cognitive science. [ABSTRACT FROM AUTHOR]

How individuals navigate perspectives and attitudes that diverge from their own affects an array of interpersonal outcomes from the health of marriages to the unfolding of international conflicts. The finesse with which people negotiate these differing perceptions depends critically upon their tacit assumptions—e.g., in the bias of naïve realism people assume that their subjective construal of a situation represents objective truth. The present study adds an important assumption to this list of biases: the illusion of information adequacy. Specifically, because individuals rarely pause to consider what information they may be missing, they assume that the cross-section of relevant information to which they are privy is sufficient to adequately understand the situation. Participants in our preregistered study (N = 1261) responded to a hypothetical scenario in which control participants received full information and treatment participants received approximately half of that same information. We found that treatment participants assumed that they possessed comparably adequate information and presumed that they were just as competent to make thoughtful decisions based on that information. Participants' decisions were heavily influenced by which cross-section of information they received. Finally, participants believed that most other people would make a similar decision to the one they made. We discuss the implications in the context of naïve realism and other biases that implicate how people navigate differences of perspective. [ABSTRACT FROM AUTHOR]

Embodied cognition asserts a symbiotic relationship between cognitive processes and the physical body, raising an intriguing question: could personality traits be intertwined with the biomechanical performance of the lower limb? This study aimed to explore this connection by examining how personality traits, assessed using the Myers-Briggs Type Indicator (MBTI), relate to lower limb rebound power (RP) measured through the five-repetition rebound jump test. Eighty participants completed two sessions: a biomechanical analysis of hopping using an Optojump® system to measure contact time, flight time, and RP, and a personality traits assessment categorizing traits across four MBTI axes: extraversion-introversion (favorite world); sensing-intuition (information processing preference); thinking-feeling (decision making); and judging-perceiving (structure). Participant characteristics did not significantly differ across MBTI axes (p≥0.07), minimizing potential confounding factors. Notably, individuals classified as intuitive showed significantly longer flight times (p = 0.02) and larger RP (p = 0.007) compared to sensing individuals, suggesting a greater reliance on the fast stretch-shortening cycle and showcasing superior use of their lower limb structures as springs. This suggests potential implications for sports performance, with intuition individuals possibly excelling in plyometric sports. However, no significant associations were found between biomechanical performance and the other three MBTI axes (p≥0.12), challenging the initial hypothesis. This research provides initial insights into the nuanced relationship between personality traits and movement patterns, indicating the potential for tailored physical interventions to enhance adherence and optimize responses in training programs. [ABSTRACT FROM AUTHOR]

Decision-making in the real world involves multiple abilities. The main goal of the current study was to examine the abilities underlying the Preschool Gambling task (PGT), a preschool variant of the Iowa Gambling task (IGT), in the context of an integrative decision-making framework. Preschoolers (n = 144) were given the PGT along with four novel decision-making tasks assessing either decision-making under ambiguity or decision-making under risk. Results indicated that the ability to learn from feedback, to maintain a stable preference, and to integrate losses and gains contributed to the variance in decision-making on the PGT. Furthermore, children's awareness level on the PGT contributed additional variance, suggesting both implicit and explicit processes are involved. The results partially support the integrative decision-making framework and suggest that multiple abilities contribute to individual differences in decision-making on the PGT. [ABSTRACT FROM AUTHOR]

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality. [ABSTRACT FROM AUTHOR]

Implicit statistical learning, whereby predictable relationships between stimuli are detected without conscious awareness, is important for language acquisition. However, while this process is putatively implicit, it is often assessed using measures that require explicit reflection and conscious decision making. Here, we conducted three experiments combining an artificial grammar learning paradigm with a serial reaction time (SRT-AGL) task, to measure statistical learning of adjacent and nonadjacent dependencies implicitly, without conscious decision making. Participants viewed an array of six visual stimuli and were presented with a sequence of three auditory (nonsense words, Expt. 1; names of familiar objects, Expt. 2) or visual (abstract shapes, Expt. 3) cues and were asked to click on the corresponding visual stimulus as quickly as possible. In each experiment, the final stimulus in the sequence was predictable based on items earlier in the sequence. Faster responses to this predictable final stimulus compared to unpredictable stimuli would provide evidence of implicit statistical learning, without requiring explicit decision making or conscious reflection. Despite previous positive results (Christiansen et al. 2009 and Misyak et al. 2010) we saw little evidence of implicit statistical learning in any of the experiments, suggesting that in this case, these SRT-AGL tasks were not an effective measure implicit statistical learning. [ABSTRACT FROM AUTHOR]

Author summary: Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives—the user and the developer. [ABSTRACT FROM AUTHOR]

Attending to the behaviors of eyewitnesses at police lineups could help to determine whether an eyewitness identification is accurate or mistaken. Eyewitness identification decision processes were explored using augmented reality holograms. Children (n = 143; Mage = 10.79, SD = 1.12 years) and adults (n = 152; Mage = 22.12, SD = 7.47 years) viewed staged crime videos and made identification decisions from sequential lineups. The lineups were presented in augmented reality. Children were less accurate than adults on the lineup task. For adults, fast response times and high post-identification confidence ratings were both reflective of identification accuracy. Fast response times were also reflective of accuracy for children; however, children's confidence ratings did not reflect the likely accuracy of their identifications. A new additional measure, the witness' proximity to the augmented reality lineup, revealed that children who made mistaken identifications moved closer to the lineup than children who correctly identified the person from the crime video. Adults who moved any distance towards the lineup were less accurate than adults who did not move at all, but beyond that, adults' proximity to the lineup was not reflective of accuracy. The findings give further evidence that behavioral indicators of deliberation and information-seeking by eyewitnesses are signals of low lineup identification reliability. The findings also suggest that when assessing the reliability of children's lineup identifications, behavioral measures are more useful than metacognitive reports. [ABSTRACT FROM AUTHOR]

Recipient financial need is a crucial factor in donation decisions. This study proposes a novel model for determining financial donations, incorporating consumption levels of both donor and recipient within a societal context. Solving our model's utility maximization problem reveals how consumption, donation, and savings are interlinked. Empirical evidence reinforces these findings, aligning with prior research and showing that larger consumption gaps between donors and recipients lead to increased donations. Our findings point towards an inherent altruistic motivation in donation, where elevating the recipient's well-being ultimately enhances the donor's own utility. This reinforces the notion that consideration of the recipient's financial hardship, as reflected by their consumption patterns, is crucial when making donation decisions. Shifting beyond traditional models, this study introduces a groundbreaking approach to financial donations. Our novel model factors in consumption levels of both the donor and recipient, along with the broader societal context, using utility maximization to unravel the intertwined decisions of consumption, donation, and savings. Real-world data validates our model, confirming known donation factors and revealing a key finding: larger disparities in consumption lead to increased giving, suggesting an altruistic drive where helping others boosts personal satisfaction. [ABSTRACT FROM AUTHOR]

This study examines the potential negative effects of city-county mergers on firm-level total factor productivity (TFP). A theoretical framework is established to explain how city-county mergers could lead to higher institutional and non-institutional costs, thus influencing government decision-making and resource allocation. As a result, firms operating in the economy will absorb these costs and adjust their behavior accordingly. By employing Chinese firm-level data in a difference-in-differences approach, this study finds a statistically significant negative effect of transitioning county-level cities into municipal districts (TCD) on firm-level productivity. Additionally, this study identifies the financial situation of reformed governments and the internal environment of firms as two mechanisms through which the TCD reform influences firm-level productivity. [ABSTRACT FROM AUTHOR]

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak. Moreover, the issue of the winner's curse could emerge when utilizing the same dataset for both IV selection and causal effect estimation, leading to biased estimates of causal effects and high false positives. Focusing on one-sample MR analysis, this paper introduces a novel method termed Mendelian Randomization with adaptive Sample-sPLitting with cross-fitting InstrumenTs (MR-SPLIT), designed to address bias issues due to IV selection and weak IVs, under the 2SLS IV regression framework. We show that the MR-SPLIT estimator is more efficient than its counterpart cross-fitting MR (CFMR) estimator. Additionally, we introduce a multiple sample-splitting technique to enhance the robustness of the method. We conduct extensive simulation studies to compare the performance of our method with its counterparts. The results underscored its superiority in bias reduction, effective type I error control, and increased power. We further demonstrate its utility through the application of a real-world dataset. Our study underscores the importance of addressing bias issues due to IV selection and weak IVs in one-sample MR analyses and provides a robust solution to the challenge. Author summary: Mendelian randomization (MR) is a method used in genetic epidemiology to determine whether a specific exposure has a causal effect on a health outcome. Ensuring the accuracy of this method is crucial for its reliability and for making informed decisions that can enhance public health and medical practices. Typically, researchers employ the two-stage least squares (2SLS) method which involves selecting a set of valid instrumental variables (IVs) to estimate and infer the causal effect. However, 2SLS can produce biased results when the effects of the IVs are weak, known as weak instrument bias. Additionally, the "winner's curse" problem may occur when using the same dataset for both IV selection and causal effect estimation, introducing additional bias. Here we introduce a novel approach called MR-SPLIT, which addresses these two bias issues by randomly splitting the data into two parts: one for IV selection and the other for IV construction and causal effect estimation. Through effective integration, this strategy enhances the power, reduces bias, and provides more precise estimates. Our approach is validated through extensive simulation studies, and its effectiveness is demonstrated by an application to a real-world dataset. [ABSTRACT FROM AUTHOR]

Surveillance systems that monitor pathogen genome sequences are critical for rapidly detecting the introduction and emergence of pathogen variants. To evaluate how interactions between surveillance capacity, variant properties, and the epidemiological context influence the timeliness of pathogen variant detection, we developed a geographically explicit stochastic compartmental model to simulate the transmission of a novel SARS-CoV-2 variant in New York City. We measured the impact of (1) testing and sequencing volume, (2) geographic targeting of testing, (3) the timing and location of variant emergence, and (4) the relative variant transmissibility on detection speed and on the undetected disease burden. Improvements in detection times and reduction of undetected infections were driven primarily by increases in the number of sequenced samples. The relative transmissibility of the new variant and the epidemic context of variant emergence also influenced detection times, showing that individual surveillance strategies can result in a wide range of detection outcomes, depending on the underlying dynamics of the circulating variants. These findings help contextualize the design, interpretation, and trade-offs of genomic surveillance strategies of pandemic respiratory pathogens. Author summary: To prevent the spread of infections that are more transmissible, evade immunity, or cause more serious illness, public health agencies must quickly detect changes in pathogens such as the virus responsible for COVID-19, which is done by testing the population to identify infections and then sequencing the positive cases to determine which virus variants caused the infections. However, it is unclear how different factors, such as the volume of testing and sequencing, the timing in the outbreak, or the transmissibility of the new variants affect our ability to quickly detect new variants of concern. In our study, we used mathematical simulations of disease spread in New York City to better understand how these factors influence the time it takes to detect a new variant and how many people have been infected by the time it is detected. In our simulations, the greatest improvement in detection speed was achieved by increasing the number of positive cases that are sampled for sequencing. However, factors beyond policymakers' control also influenced the time it took to detect the new variant, meaning that a wide range of detection outcomes was possible even under an ideal public health strategy. These findings help guide decision making for future outbreaks. [ABSTRACT FROM AUTHOR]

Anthropometric prevalence indicators such as stunting, wasting, and underweight are widely-used population-level tools used to track trends in childhood nutrition. Threats to the validity of these data can lead to erroneous decision making and improper allocation of finite resources intended to support some of the world's most vulnerable populations. It has been demonstrated previously that aggregated prevalence rates for these indicators can be highly sensitive to biases in the presence of non-directional measurement errors, but the quantitative relationship between the contributing factors and the scale of this bias has not been fully described. In this work, a Monte Carlo simulation exercise was performed to generate high-statistics z-score distributions with a wide range of mean and standard deviation parameters relevant to the populations in low- and middle-income countries (LMIC). With the important assumption that the distribution's standard deviation should be close to 1.0 in the absence of non-directional measurement errors, the shift in prevalence rate due to this common challenge is calculated and explored. Assuming access to a given z-score distribution's mean and standard deviation values, this relationship can be used to evaluate the potential scale of prevalence bias for both historical and modern anthropometric indicator results. As a demonstration of the efficacy of this exercise, the bias scale for a set of 21 child anthropometry datasets collected in LMIC contexts is presented. [ABSTRACT FROM AUTHOR]

Background: If communicated adequately, numerical decision-relevant information can support informed and shared decision making. Visual formats are recommended, but which format supports patients depending on their health literacy (HL) levels for specific decisions is unclear. Study aim: The aim of this study is to investigate: 1) the effect of survival rates and side-effects presentation formats on comprehension and 'feeling informed'; 2) differential effects among women with higher/lower HL, with adjuvant systemic breast cancer therapy as case example. Methods: Two online experiments among women from the Dutch population without a history of breast cancer were conducted. Experiment 1 had a 3 (survival rate format: text block–bar graph–icon array) x 2 (HL: low–high) between-subjects design. Experiment 2 had a 5 (side-effects format: no probability information–probability information in numbers with or without a visualisation–probability information in numbers with or without a visualisation accompanied by a description of the side-effects) x 2 (HL: low–high) design. Primary outcomes were comprehension and feeling informed (Experiment 2 only). Formats were previously designed in co-creation with patients. Results: In Experiment 1, presentation format did not affect gist or verbatim comprehension. Higher HL was associated with higher gist comprehension. Experiment 2 showed an interaction between presentation format and HL on 'feeling informed'. When provided with visualised probability information without a description of the side-effects, women with lower HL felt better informed than women with higher HL. Conclusion: Visual formats did not enhance comprehension of survival rate information beyond a well-designed text block format. However, none of the formats could overcome HL differences. When designing decision-relevant information, visualisations might not necessarily provide an advantage over structured numerical information for both patients with lower and higher HL. However, a deeper understanding of presenting side-effect information is warranted. [ABSTRACT FROM AUTHOR]

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA). [ABSTRACT FROM AUTHOR]

In this research, we developed an epidemic model with a combination of Atangana-Baleanu Caputo derivative and classical operators for the hybrid operator's memory effects, allowing us to observe the dynamics and treatment effects at different time phases of syphilis infection caused by sex. The developed model properties, which take into account linear growth and Lipschitz requirements relating the rate of effects within its many sub-compartments according to the equilibrium points, include positivity, unique solution, exitance, and boundedness in the feasible domain. After conducting sensitivity analysis with various parameters influencing the model for the piecewise fractional operator, the reproductive number R0 for the biological viability of the model is determined. Generalized Ulam-Hyers stability results are employed to preserve global stability. The investigated model thus has a unique solution in the specified subinterval in light of the Banach conclusion, and contraction as a consequence holds for the Atangana-Baleanu Caputo derivative with classical operators. The piecewise model that has been suggested has a maximum of one solution. For numerical solutions, piecewise fractional hybrid operators at various fractional order values are solved using the Newton polynomial interpolation method. A comparison is also made between Caputo operator and the piecewise derivative proposed operator. This work improves our knowledge of the dynamics of syphilis and offers a solid framework for assessing the effectiveness of interventions for planning and making decisions to manage the illness. [ABSTRACT FROM AUTHOR]

We make a novel investigation of welfare costs associated with various suboptimal decisions made by retirees, both analytically and numerically. We utilize a unique framework that incorporates recursive utility with housing, and also encompasses expected utility and recursive utility without housing as special cases. Our findings indicate that under-investment in stocks incurs lower welfare costs compared to an equivalent over-investment. Suboptimal allocations in bond holdings result in higher costs than similar misallocations in stocks. Choosing not to participate in the stock market is less detrimental than avoiding the bond market. Should retirees opt to simplify their decision-making by investing solely in one type of asset, it is less costly for them to invest exclusively in bonds. Overconsumption of housing is less costly than an equivalent underconsumption. Suboptimal consumption imposes the highest welfare cost. Decisions regarding consumption, housing, and savings are found to be more crucial than the choice of how to distribute liquid savings between stocks and bonds. Additionally, recursive utility model better captures retirees' preference for bonds over stocks than expected utility model. Our results, which are consistent across various parameter settings, provide valuable insights for academics and policymakers aiming to enhance retiree welfare. [ABSTRACT FROM AUTHOR]

Purpose: A cross-sectional study was conducted to investigate the feasibility of implementing routine collection of the Euro-Qol 5 dimensions (EQ-5D) questionnaire, to inform drug and health technology reimbursement decision making. Methods: Women with breast cancer were recruited during scheduled clinic visits to an academic cancer centre. EQ-5D-5L was self-administered using electronic tablets. Diagnostic and treatment data were abstracted from patient charts. Feasibility was assessed primarily by the proportion of patients who fully completed EQ-5D-5L and by their willingness to complete the instrument at each clinic visit. Results: 588 women were approached for study participation, 341 were enrolled. Fully completed EQ-5D-5L questionnaires were obtained in 323 participants (95% of participants, 95% CI 92–97%). Median time for EQ-5D-5L completion was 1.5 minutes (range:0.35 to 14.7). Mean age of participants was 58 years old. Most women who completed EQ-5D were White, born outside Canada and presented a high education level; one-quarter had metastatic disease. Most participants reported "No problems" in all EQ-5D-5L dimensions. Mean EQ-5D-5L index and mean EQ-5D-5L VAS values for all participants were respectively 0.83 (SD 0.13) and 75.7 (SD 17.45), with patients with metastatic disease scoring the lowest values. Seventy-eight percent of participants were willing to complete EQ-5D-5L at each clinic visit; lower Charlson comorbidity index and higher education level were predictors of willingness to continue to answer EQ-5D-5L. Conclusions: Tablet-based collection of EQ-5D-5L in the context of routine clinical practice proved to be feasible. However, many patients declined study participation or reported being in full health, raising concerns about whether this method of collecting EQ-5D adequately represents the health status of all breast cancer patients. [ABSTRACT FROM AUTHOR]

Big data pertains to extensive and intricate compilations of information that necessitate the implementation of proficient and cost-effective evaluation and analysis tools to derive insights and support decision making. The Fermatean fuzzy set theory possesses remarkable capability in capturing imprecision due to its capacity to accommodate complex and ambiguous problem descriptions. This paper presents the study of the concepts of dynamic ordered weighted aggregation operators in the context of Fermatean fuzzy environment. In numerous practical decision making scenarios, the term "dynamic" frequently denotes the capability of obtaining decision-relevant data at various time intervals. In this study, we introduce two novel aggregation operators: Fermatean fuzzy dynamic ordered weighted averaging and geometric operators. We investigate the attributes of these operators in detail, offering a comprehensive description of their salient features. We present a step-by-step mathematical algorithm for decision making scenarios in the context of proposed methodologies. In addition, we highlight the significance of these approaches by presenting the solution to the decision making problem and determining the most effective big data analytics platform for YouTube data analysis. Finally, we perform a thorough comparative analysis to assess the effectiveness of the suggested approaches in comparison to a variety of existing techniques. [ABSTRACT FROM AUTHOR]

Background: Pulmonary Rehabilitation (PR) services typically offer programmes to support individuals living with COPD make rehabilitation choices that best meet their needs, however, uptake remains low. Shared Decision-Making (SDM; e.g., Patient Decision Aids (PtDA)) interventions increase informed and values-based decision-making between individuals and healthcare professionals (HCPs). We aimed to develop an intervention to facilitate PR SDM which was acceptable to individuals living with COPD and PR HCPs. Methods: An iterative development process involving qualitative methods was adopted. Broad overarching frameworks included: complex intervention development framework, the multiple stakeholder decision making support model, and the Ottawa Decision Support Framework. Development included: assembling a steering group, outlining the scope for the PtDA, collating data to inform the PtDA design, prototype development, alpha testing with individuals with COPD (n = 4) and PR HCPs (n = 8), PtDA finalisation, and design and development of supporting components. This took nine months. Results: The PtDA was revised six times before providing an acceptable, comprehensible, and usable format for all stakeholders. Supporting components (decision coaching training and a consultation prompt) were necessary to upskill PR HCPs in SDM and implement the intervention into the PR pathway. Conclusions: We have developed a three-component SDM intervention (a PtDA, decision coaching training for PR healthcare professionals, and a consultation prompt) to support individuals living with COPD make informed and values-based decision about PR together with their PR healthcare professional. Clear implementation strategies are outlined which should support its integration into the PR pathway. [ABSTRACT FROM AUTHOR]

The Ultimatum Game is an effective tool for understanding how social decision-making is influenced by emotions in both research and clinical settings. Previous findings have shown that the Ultimatum Game can evoke negative emotions, especially anger and aggression. In a sample of non-clinical adults (N = 143) we evaluated the sensitivity of an anger-infused version of the Ultimatum Game to individual differences in anger and irritability. Findings showed significant relationships between anger and aggressive behaviors in the Ultimatum game, but no association between irritability and aggressive behavior were observed. This indicates that the anger-infused Ultimatum Game is a promising method for studying individual differences in trait anger and anger expression. However, the relationship between decision-making in the anger-infused Ultimatum Game and irritability is less straight forward and needs further investigation. Therefore, when studying the behavioral responses of irritability, it would be beneficial to capture other behaviors beyond aggressive responses. [ABSTRACT FROM AUTHOR]

In order to assess the quality of senior tourism services in vacation destinations, we examine complex interval valued intuitionistic Fuzzy Dombi Hamy Mean (CIVIFDHM) operators. These operators successfully manage imprecision and uncertainty in the preferences of senior tourists. However, the Hamy mean (HM) operator can identify the connections between various input data sets and produce excellent outcomes when combining and evaluating information. We illustrate their usefulness and efficacy through a case study, providing a strong instrument for improving service quality for senior citizens and promoting an inclusive and fulfilling travel experience. In this work, we develop the HM operator and Dombi operations with Complex interval valued intuitionistic fuzzy numbers (CIVIFNs). We recommend the CIVIFDHM operator, complex interval valued intuitionistic fuzzy weighted Dombi Hamy mean (CIVIFWDHM), complex interval valued intuitionistic fuzzy dual Dombi Hamy mean (CIVIFDDHM), and complex interval valued intuitionistic fuzzy weighted dual Dombi Hamy mean (CIVIFWDD) operators. Next, multiple attribute decision making (MADM) models are constructed with the help of CIVIFWDHM and CIVIFWDDHM operators. We provided an evaluation of an older tourism operator in a tourist area as an example to show the suggested models. [ABSTRACT FROM AUTHOR]

The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental in harnessing ocean waves for electricity generation. To benchmark WEC technologies, we employed an integrated approach combining the MEthod based on the Removal Effects of Criteria (MEREC) and the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo). MEREC provided a systematic way to determine the importance of various benchmarking criteria, while SF-CoCoSo facilitated the synthesis of complex decision-making data into a coherent evaluation score for each technology. The results of the study offer a definitive ranking of WEC technologies, with findings emphasizing the importance of grid connectivity and adaptability to various wave conditions as pivotal to the technologies' success. While the study makes significant strides in the evaluation of WECs, it also recognizes limitations, including the potential for evolving market dynamics to influence criteria weightings and the assumption that the MCDM methods capture all decision-making complexities. Future work should expand the evaluative criteria and explore additional MCDM methods to validate and refine the benchmarking process further. [ABSTRACT FROM AUTHOR]

Background: Shared decision-making (SDM) has yet to be successfully adopted into routine use in psychiatric settings amongst people living with severe mental illnesses. Suboptimal rates of SDM are particularly prominent amongst patients with psychotic illnesses during antipsychotic treatment choices. Many interventions have been assessed for their efficacy in improving SDM within this context, although results have been variable and inconsistent. Aims: To generate an in-depth understanding of how, why, for whom, and to what extent interventions facilitating the application of SDM during antipsychotic treatment choices work and the impact of contextual factors on intervention effectiveness. Methods: This review will use realist review methodology to provide a causal understanding of how and why interventions work when implementing SDM during antipsychotic treatment choices. The cohort of interest will be those experiencing psychosis where ongoing treatment with an antipsychotic is clinically indicated. The review will take place over five stages; (1) Locating existing theories, (2) Searching for evidence, (3) Selecting articles, (4) Extracting and organising data and (5) Synthesizing evidence and drawing conclusions. An understanding of how and why interventions work will be achieved by developing realist programme theories on intervention effectiveness through iterative literature reviews and engaging with various stakeholder groups, including patient, clinician and carer representatives. Discussion: This is the first realist review aiming to identify generative mechanisms explaining how and why successful interventions aimed at improving SDM within the parameters outlined work and in which contexts desired outcomes are most likely to be achieved. Review findings will include suggestions for clinicians, policy and decision-makers about the most promising interventions to pursue and their ideal attributes. [ABSTRACT FROM AUTHOR]

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter β). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases. Author summary: Mathematical and computational work has shown that in order to optimize decision making, humans and other adaptive agents must compute confidence in their perception and actions. Currently, it remains unknown how this confidence is computed. We demonstrate how humans can approximate confidence in a tractable manner. Our computational model makes novel predictions about when confidence will be biased (e.g., over- or underconfidence due to selective environmental feedback). We empirically tested these predictions in a novel experimental paradigm, by providing continuous model-based feedback. We observed that different feedback manipulations elicited distinct patterns of confidence judgments, in ways predicted by the model. Overall, we offer a framework to both interpret optimal confidence and resolve confidence biases that characterize several psychiatric disorders. [ABSTRACT FROM AUTHOR]

Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation—all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively. [ABSTRACT FROM AUTHOR]

Intolerance of Uncertainty (IU) is thought to lead to maladaptive behaviours and dysfunctional decision making, both in the clinical and healthy population. The seminal study reported by Luhmann and collaborators in 2011 showed that IU was negatively associated with choosing a delayed, but more certain and valuable, reward over choosing an immediate, but less certain and valuable, reward. These findings have been widely disseminated across the field of personality and individual differences because of their relevance to understand the role of IU in maladaptive behaviours in anxiety-related disorders. We conducted a study to replicate and extend Luhmann et al.'s results with a sample of 313 participants, which exceeded the size necessary (N = 266) to largely improve the statistical power of the original study by using the small telescopes approach. The results of our well powered study strongly suggest that the relationship between IU and the tendency to prefer an immediate, but less certain and less valuable reward is virtually negligible. Consequently, although this relationship cannot be definitely discarded, we conclude that it cannot be detected with Luhmann et al.'s (2011) decision-making task. [ABSTRACT FROM AUTHOR]

This article delves into the current popular phenomenon of live streaming e-commerce, with a specific focus on issues related to product quality and after-sales service. It constructs an evolutionary game model that encompasses three key stakeholders: e-commerce platforms, consumers, and streamers. The study conducts a thorough analysis of the interactions and strategic choices among these entities, investigating the stability of equilibrium strategy combinations within the game system and the influence of various factors on decision-making behaviors. Furthermore, the validity of the analytical conclusion is corroborated through the application of simulation analysis methods. The study finds that for the consumer, strategies such as reducing losses encountered due to quality issues under strict demands, enhancing compensation in these scenarios, and increasing benefits for maintaining stringent requirements during live streaming sessions can motivate them to adopt more stringent strategies. For the streamer, essential factors in promoting the selection of high-quality products include increasing the benefits associated with such choices and reducing the probability of quality issues, or alternatively, decreasing the gains from lower-quality selections and increasing the likelihood of encountering quality problems with these products. For the e-commerce platform, strategically adjusting the profit-sharing ratio to maintain collaborative momentum and influence the enthusiasm of both consumers and streamers is a critical strategy to avert market scenarios akin to prisoner's dilemmas and tragic outcomes. Overall, this research offers profound insights into the complex strategic evolution within the live commerce market, providing valuable guidance for interaction strategies among e-commerce platforms, consumers, and streamers. Its implications for practical decision-making optimization and strategic formulation are of significant importance. [ABSTRACT FROM AUTHOR]

In everyday life, we encounter situations that require tradeoffs between potential rewards and associated costs, such as time and (physical) effort. The literature indicates a prominent role for dopamine in discounting of both delay and effort, with mixed findings for delay discounting in humans. Moreover, the reciprocal antagonistic interaction between dopaminergic and cholinergic transmission in the striatum suggests a potential opponent role of acetylcholine in these processes. We found opposing effects of dopamine D2 (haloperidol) and acetylcholine M1 receptor (biperiden) antagonism on specific components of effort-based decision-making in healthy humans: haloperidol decreased, whereas biperiden increased the willingness to exert physical effort. In contrast, delay discounting was reduced under haloperidol, but not affected by biperiden. Together, our data suggest that dopamine, acting at D2 receptors, modulates both effort and delay discounting, while acetylcholine, acting at M1 receptors, appears to exert a more specific influence on effort discounting only. Tradeoffs between potential rewards and associated costs involves the dopaminergic system, which antagonistically interacts with the cholinergic system. This study describes that dopamine and acetylcholine have opposing effects on the modulation of specific components of cost-benefit decision making in humans. [ABSTRACT FROM AUTHOR]

How do people with amyotrophic lateral sclerosis (PALS) deal with their diagnosis and engage in end-of-life decision-making? What informational or supportive needs do they have for counselling about life-sustaining treatment and end-of-life care? Which correlating conditions and influences relate to these needs and how do they connect to the wish to die or wish to live? We conducted a qualitative interview study with 13 people with ALS in Germany from March 2019 to April 2021. Data collection and analysis followed a grounded theory-based approach and revealed close relationships between coping, informational needs and the preparedness for decision-making. We identified the coping strategies 'avoid thinking about end-of-life' and its counterpart, 'planning ahead to be well-prepared,' and differentiated the latter into the patterns 'withdrawing from life and taking precautions against life-prolongation' and 'searching for a new meaning in life and preparing for life-sustaining treatment'. The approaches are based on individual perceptions, attitudes and motives and can be positively/negatively reinforced by healthcare professionals (HCP), family and other interpersonal networks, but also by disease progression and in reaction to health care services. Type and degree of needs concerning information and counselling differed according to coping strategies. These strategies may vary over time, resulting in different support needs. Our findings signify that deep insight is needed into PALS' coping processes to understand their decision-making about life-sustaining treatment. Healthcare professionals should be sensitive to illness experiences beyond medical aspects and foster coping as a biographical process to better support people with ALS. [ABSTRACT FROM AUTHOR]

Background: The birth plan is a document expressing a pregnant woman's childbirth preferences, enabling communication of expectations and facilitating discussions among women, their partners, and healthcare providers for key birthing decisions. There has been limited research on the role of birth plans in shared decision-making (SDM). Our study aims to explore how the use of birth plans can contribute to SDM from women's, partners, and healthcare providers' perspectives. Methods: We conducted in-depth interviews with women, their partners, and their healthcare providers. We used a thematic analysis to identify themes and subthemes. Furthermore, we created a grounded theory about the role of birth plans as a tool in SDM. Results: Three main themes were created: "Creating a birth plan", "Getting all on board" and "Birth plans in the daily practice of decision-making". Most women, partners, and healthcare providers agreed that birth plans can facilitate communication and SDM. Women and their partners viewed the birth plan as a tool to prepare for birth. Most healthcare providers mentioned the birth plan as a tool to get to know the women, their partners, and their preferences. Barriers are the attitude of healthcare providers toward birth plans, such as their evident resistance to the birth plan itself or to certain preferences. Another barrier is the assumption women and their partners may have that these plans can accurately predict the childbirth experience, enhancing the chance of a disappointing, negative experience. Some healthcare providers view birth plans as barriers to SDM. Conclusion: The use of a birth plan seems to promote women's, partners', and healthcare providers' involvement in the birth process, and seems suitable to facilitate SDM. Further research is required to explore strategies for overcoming barriers, including healthcare providers' attitudes toward birth plans and the expectations of women and their partners regarding their role. [ABSTRACT FROM AUTHOR]

The use of multi-criteria decision analysis (MCDA) for disease prioritization at the sub-national level in sub-Sahara Africa (SSA) is rare. In this research, we contextualized MCDA for parallel prioritization of endemic zoonoses and animal diseases in The Adamawa and North regions of Cameroon. MCDA was associated to categorical principal component analysis (CATPCA), and two-step cluster analysis. Six and seven domains made of 17 and 19 criteria (out of 70) respectively were selected by CATPCA for the prioritization of zoonoses and animal diseases, respectively. The most influencing domains were "public health" for zoonoses and "control and prevention" for animal diseases. Twenty-seven zoonoses and 40 animal diseases were ranked and grouped in three clusters. Sensitivity analysis resulted in high correlation between complete models and reduced models showing the robustness of the simplification processes. The tool used in this study can be applied to prioritize endemic zoonoses and transboundary animal diseases in SSA at the sub-national level and upscaled at the national and regional levels. The relevance of MCDA is high because of its contextualization process and participatory nature enabling better operationalization of disease prioritization outcomes in the context of African countries or other low and middle-income countries. [ABSTRACT FROM AUTHOR]

Drug-induced QT prolongation (diLQTS), and subsequent risk of torsade de pointes, is a major concern with use of many medications, including for non-cardiac conditions. The possibility that genetic risk, in the form of polygenic risk scores (PGS), could be integrated into prediction of risk of diLQTS has great potential, although it is unknown how genetic risk is related to clinical risk factors as might be applied in clinical decision-making. In this study, we examined the PGS for QT interval in 2500 subjects exposed to a known QT-prolonging drug on prolongation of the QT interval over 500ms on subsequent ECG using electronic health record data. We found that the normalized QT PGS was higher in cases than controls (0.212±0.954 vs. -0.0270±1.003, P = 0.0002), with an unadjusted odds ratio of 1.34 (95%CI 1.17–1.53, P<0.001) for association with diLQTS. When included with age and clinical predictors of QT prolongation, we found that the PGS for QT interval provided independent risk prediction for diLQTS, in which the interaction for high-risk diagnosis or with certain high-risk medications (amiodarone, sotalol, and dofetilide) was not significant, indicating that genetic risk did not modify the effect of other risk factors on risk of diLQTS. We found that a high-risk cutoff (QT PGS ≥ 2 standard deviations above mean), but not a low-risk cutoff, was associated with risk of diLQTS after adjustment for clinical factors, and provided one method of integration based on the decision-tree framework. In conclusion, we found that PGS for QT interval is an independent predictor of diLQTS, but that in contrast to existing theories about repolarization reserve as a mechanism of increasing risk, the effect is independent of other clinical risk factors. More work is needed for external validation in clinical decision-making, as well as defining the mechanism through which genes that increase QT interval are associated with risk of diLQTS. [ABSTRACT FROM AUTHOR]

Family and cultural contexts can constrain the effectiveness of evidence-based interventions designed to improve the health and wellbeing of women and their children. Unequal power relationships within the household may underlie the failure of many programs targeting women to achieve their intended impact. To reduce these unequal power dynamics within the households, many programs or interventions aim to both assess and improve the gender dynamics between husbands and wives within the household. Decision-making is one important facet of these dynamics and has been linked to health outcomes for women and children. However, household decision-making is rarely observed and often difficult to capture. This study aimed to use qualitative research to further understand one aspect of decision-making, namely on how to spend money. In two regions of Tanzania, we used surveys and interviews to explore different perspectives on spending and allocation of resources among 58 couples in rural farming households. While many men and women initially reported that they made decisions jointly, most women stated they would often concede if there was a disagreement or argument around spending. These results highlight the different perceptions of joint decision-making between men and women. We compared these results to survey responses on decision-making and found differences within and between couples across interview and survey responses. Based on the differences in qualitative and survey responses within couples and how they reported dealing with disagreement, our study found households were on a spectrum from no cooperation in decision-making to full cooperation. Our results highlight challenges for assessing decision-making on spending and ultimately improving these decision-making dynamics within the household. These challenges are especially important for maternal and child behavioral change and provide insights on why many interventions aimed at improving women's decision- making power on money may not reach their full potential. [ABSTRACT FROM AUTHOR]

What is the role of working memory over the course of non-native speech category learning? Prior work has predominantly focused on how working memory might influence learning assessed at a single timepoint. Here, we substantially extend this prior work by examining the role of working memory on speech learning performance over time (i.e., over several months) and leverage a multifaceted approach that provides key insights into how working memory influences learning accuracy, maintenance of knowledge over time, generalization ability, and decision processes. We found that the role of working memory in non-native speech learning depends on the timepoint of learning and whether individuals learned the categories at all. Among learners, across all stages of learning, working memory was associated with higher accuracy as well as faster and slightly more cautious decision making. Further, while learners and non-learners did not have substantially different working memory performance, learners had faster evidence accumulation and more cautious decision thresholds throughout all sessions. Working memory may enhance learning by facilitating rapid category acquisition in initial stages and enabling faster and slightly more careful decision-making strategies that may reduce the overall effort needed to learn. Our results have important implications for developing interventions to improve learning in naturalistic language contexts. [ABSTRACT FROM AUTHOR]

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making. [ABSTRACT FROM AUTHOR]

Cognitive rehabilitation, STEM (science, technology, engineering, and math) skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. Additionally, treating humans as noisy optimal agents, we employ maximum entropy inverse reinforcement learning to analyze the effect of feedback on the implicit human reward structure that guides their decision making. Lastly, we explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes. Our findings underscore that humans perceive evaluative feedback as indicative of their long-term strategic success, thus aiding in skill acquisition and transfer in sequential decision-making tasks. Moreover, we demonstrate that evaluative feedback fosters a more structured and organized learning experience compared to learning without feedback. Furthermore, our results indicate that providing intermediate goals alone does not significantly enhance human learning outcomes. [ABSTRACT FROM AUTHOR]

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369–3.504), BUN (OR = 1.378, 95%CI:1.026–1.959), NLR (OR = 1.370, 95%CI:1.016–1.906), D-D (OR = 1.500, 95%CI:1.112–2.110), and Ascites (OR = 5.517, 95%CI:1.217–2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians. [ABSTRACT FROM AUTHOR]

The paper presents the results of research on the influence of risk factors on the implementation of railway investments in Poland (build stage) and deals with a detailed diagnosis of relation between factors. The application of DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method for the analyses allowed to develop a cause-and-effect model of key factors and analyse the importance of the factors. Eleven factors identified in eariel studies as the most important risk factors were examined. It was found that the factors: errors in the preparation of tender documents (10.38%), errors in project documentation (10.02%), improperly estimated time of completion of the investment by the Employer (9.82%), internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts (9.51%) have the highest degree of importance. Factors: too many external institutions involved in the investment process and internal regulations of PKP Polskie Koleje Państwowe S.A. (Polish State Railways) not coordinated with the provisions of contracts, have the greatest net impact on the other factors. The relations between the factors and factors importance are valuable knowledge for engineers, enabling the project to be implemented according to the planned schedule and investment cost. [ABSTRACT FROM AUTHOR]

Introduction: People make oral healthcare decisions regardless of having partial information, misinformation, sources that deliberately mislead, or information that is culturally influenced. This is particularly true in the Appalachian culture where oral healthcare decision-making practices are not well understood by researchers and dental professionals. Despite efforts to improve dental care utilization, the Appalachia region remains low in oral healthcare utilization. There is a need for a theory to identify concepts in decision-making when seeking oral healthcare. The theory could be useful in creating oral health interventions. The study objective is to develop a theory to identify concepts that influence oral healthcare decision-making in Appalachia (OHDA). Methods: The researchers used a grounded theory qualitative study design to explain data for a theory of OHDA. Participants from Appalachia, in 20-minute interviews, provided insights into concepts that influence OHDA from August 22, 2017 to May 26, 2022. Notes/memos were written during and after the interviews and coding was conducted after the interviews. Open coding categories emerged through constant comparison of responses. Results: Five overarching concepts that embody OHDA were discovered: Affect (Level of Pain/Emotion/Stress involvement), Awareness, Trust/belief, Resources, and Risk Perception. All participants discussed the impact of social media toward these concepts. Conclusion: To influence a person's OHDA, public health officials and researchers need to address the person's affect, level of awareness, trust/belief, available resources, and risk perception. Social media is very important in awareness concerning oral health information. These factors are important to consider for similar research in oral healthcare utilization at the population level. [ABSTRACT FROM AUTHOR]

We have previously shown that polygenic risk scores (PRS) can improve risk stratification of peripheral artery disease (PAD) in a large, retrospective cohort. Here, we evaluate the potential of PRS in improving the detection of PAD and prediction of major adverse cardiovascular and cerebrovascular events (MACCE) and adverse events (AE) in an institutional patient cohort. We created a cohort of 278 patients (52 cases and 226 controls) and fit a PAD-specific PRS based on the weighted sum of risk alleles. We built traditional clinical risk models and machine learning (ML) models using clinical and genetic variables to detect PAD, MACCE, and AE. The models' performances were measured using the area under the curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), and Brier score. We also evaluated the clinical utility of our PAD model using decision curve analysis (DCA). We found a modest, but not statistically significant improvement in the PAD detection model's performance with the inclusion of PRS from 0.902 (95% CI: 0.846–0.957) (clinical variables only) to 0.909 (95% CI: 0.856–0.961) (clinical variables with PRS). The PRS inclusion significantly improved risk re-classification of PAD with an NRI of 0.07 (95% CI: 0.002–0.137), p = 0.04. For our ML model predicting MACCE, the addition of PRS did not significantly improve the AUC, however, NRI analysis demonstrated significant improvement in risk re-classification (p = 2e-05). Decision curve analysis showed higher net benefit of our combined PRS-clinical model across all thresholds of PAD detection. Including PRS to a clinical PAD-risk model was associated with improvement in risk stratification and clinical utility, although we did not see a significant change in AUC. This result underscores the potential clinical utility of incorporating PRS data into clinical risk models for prevalent PAD and the need for use of evaluation metrics that can discern the clinical impact of using new biomarkers in smaller populations. [ABSTRACT FROM AUTHOR]

Background: The practice of female genital mutilation is associated with harmful social norms promoting violence against girls and women. Various studies have been conducted to examine the prevalence of female genital mutilation and its associated factors. However, there has been limited studies conducted to assess the association between female genital mutilation and markers of women's autonomy, such as their ability to negotiate for safer sex. In this study, we examined the association between female genital mutilation and women's ability to negotiate for safer sex in sub-Saharan Africa (SSA). Methods: We pooled data from the most recent Demographic and Health Surveys (DHS) conducted from 2010 to 2020. Data from a sample of 50,337 currently married and cohabiting women from eleven sub-Saharan African countries were included in the study. A multilevel binary logistic regression analysis was used to examine the association between female genital mutilation and women's ability to refuse sex and ask their partners to use condom. Adjusted odds ratios (aORs) with a 95% confidence interval (CI) were used to present the findings of the logistic regression analysis. Statistical significance was set at p<0.05. Results: Female genital mutilation was performed on 56.1% of women included in our study. The highest and lowest prevalence of female genital mutilation were found among women from Guinea (96.3%) and Togo (6.9%), respectively. We found that women who had undergone female genital mutilation were less likely to refuse sex from their partners (aOR = 0.91, 95% CI = 0.86, 0.96) and ask their partners to use condoms (aOR = 0.82, 95% CI = 0.78, 0.86) compared to those who had not undergone female genital mutilation. Conclusion: Female genital mutilation hinders women's ability to negotiate for safer sex. It is necessary to implement health education and promotion interventions (e.g., decision making skills) that assist women who have experienced female genital mutilation to negotiate for safer sex. These interventions are crucial to enhance sexual health outcomes for these women. Further, strict enforcement of policies and laws aimed at eradicating the practice of female genital mutilation are encouraged to help contribute to the improvement of women's reproductive health. [ABSTRACT FROM AUTHOR]

Acute behavioural disturbance (ABD), sometimes called 'excited delirium', is a medical emergency. In the UK, some patients presenting with ABD are managed by advanced paramedics (APs), however little is known about how APs make restraint decisions. The aim of this research is to explore the decisions made by APs when managing restraint in the context of ABD, in the UK pre-hospital ambulance setting. Seven semi-structured interviews were undertaken with APs. All participants were experienced APs with post-registration, post-graduate advanced practice education and qualifications. The resulting data were analysed using reflexive thematic analysis, informed by critical realism. We identified four interconnected themes from the interview data. Firstly, managing complexity and ambiguity in relation to identifying ABD patients and determining appropriate treatment plans. Secondly, feeling vulnerable to professional consequences from patients deteriorating whilst in the care of APs. Thirdly, negotiating with other professionals who have different roles and priorities. Finally, establishing primacy of care in relation to incidents which involve police officers and other professionals. A key influence was the need to characterise incidents as medical, as an enabler to establishing clinical leadership and decision-making control. APs focused on de-escalation techniques and sought to reduce physical restraint, intervening with pharmacological interventions if necessary to achieve this. The social relationships and interactions with patients and other professionals at the scene were key to success. Decisions are a source of anxiety, with fears of professional detriment accompanying poor patient outcomes. Our results indicate that APs would benefit from education and development specifically in relation to making ABD decisions, acknowledging the context of inter-professional relationships and the potential for competing and conflicting priorities. A focus on joint, high-fidelity training with the police may be a helpful intervention. [ABSTRACT FROM AUTHOR]

Changes in risk preference have been reported when making a series of independent risky choices or non-foraging economic decisions. Behavioral economics has put forward various explanations for specific effects on risk preference in non-foraging tasks, but a consensus regarding the general principle underlying these effects has not been reached. In contrast, recent studies have investigated human economic risky choices using tasks adapted from foraging theory, which require consideration of past choices and future opportunities to make optimal decisions. In these foraging tasks, human economic risky choices are explained by the ethological principle of fitness maximization, which naturally leads to dynamic risk preference. Here, we conducted two online experiments to investigate whether the principle of fitness maximization can explain risk preference dynamics in a non-foraging task. Participants were asked to make a series of independent risky economic decisions while the environmental richness changed. We found that participants' risk preferences were influenced by the current and past environments, making them more risk-averse during and after the rich environment compared to the poor environment. These changes in risk preference align with fitness maximization. Our findings suggest that the ethological principle of fitness maximization might serve as a generalizable principle for explaining dynamic preferences, including risk preference, in human economic decision-making. Author summary: Decision-making involving probabilistic outcomes (i.e., under risk) is an integral part of our daily lives. Empirical studies have shown that risky behavior often deviates from standard economic theory, and peoples' risk preferences change depending on their psychological and physiological states, and the context of the decision. While previous studies have developed empirical mathematical models to provide mechanistic explanations of these effects, there is no unifying principle that explains why human risky decision-making is tuned this way. Here, we document one such context effect and suggest that this effect can be explained by the ethological principle of fitness maximization. In our study, participants made sequential independent risky decisions in different environments. We found that environments in which it was easy to get large rewards (rich environment) increased participants' risk-aversiveness both during and after experiencing them, and experiencing poor environments increased risk taking behavior. The foregoing modulations of risk-aversiveness are predicted if participants make decisions to satisfy some internal threshold for minimum reward gain, akin to reaching a minimum threshold for survival, rather than to maximize reward gain. Our results suggest that a better understanding of human economic behavior may be achieved under the principle of fitness maximization. [ABSTRACT FROM AUTHOR]