Judgment Model Research Articles

Values, decision-making and empirical bioethics: a conceptual model for empirically identifying and analyzing value judgements

It can be assumed that value judgements, which are needed to judge what is ‘good’ or ‘better’ and what is ‘bad’ or ‘worse’, are involved in every decision-making process. The theoretical understanding and analysis of value judgements is, therefore, important in the context of bioethics, for example, to be able to ethically assess real decision-making processes in biomedical practice and make recommendations for improvements. However, real decision-making processes and the value judgements inherent in them must first be investigated empirically (‘empirical bioethics’). For this to succeed, what exactly a ‘value judgement’ is and of what components it might consist must initially be theoretically clarified. A corresponding conceptual model can then support or even enable empirical data collection and analysis and, above all, subsequent ethical analysis and evaluation. This paper, therefore, presents a value judgement model with its theoretical derivation. It also illustrates its application in an interview study of decision-making between animal experimentation and alternative methods in the context of biomedical research. Though the model itself can be theoretically deepened and extended, the application of the model works in general and helps to uncover what value judgements can enter into decision-making. However, the empirical methods, for example, qualitative interviews, can also be better oriented towards eliciting value judgements (as understood according to the model). Further applications of the model to other topics or by means of other empirical methods are conceivable.

The impact of tutoring on nursing students' clinical judgment: A quasi-experimental study.

Nurses' lack of clinical judgment often leads to adverse patient outcomes due to failure to recognize clinical deterioration, intervene, and manage complications. Teaching clinical judgment through a nursing process can help nursing students provide safe and competent patient care with improved health outcomes and to pass the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The aim of this study was to examine the effect of tutoring on clinical judgment of undergraduate nursing students utilizing Lasater's Clinical Judgment Rubric (LCJR). This study also compared the clinical judgment of male and female nursing students and students from different semester levels. This quasi-experimental study utilized a single group pretest, posttest design. A convenience sample of n = 40 undergraduate nursing students from the Los Angeles County College of Nursing and Allied Health participated in the study. The participants underwent a pretest simulation, four sessions of the Clinical Judgment Model (CJM)-based tutoring, and a posttest simulation. The posttest clinical judgment scores (35.70 ± 3.6) were significantly different from the pretest scores (25.78 ± 5.20). The tutoring had a significant effect on the clinical judgment of nursing students t(39) = -11.64, n = 40, p < .001, at 95% CI of the mean difference. Enhancing nursing students' clinical judgment is crucial to provide high-quality, safe patient care with improved health outcomes. The CJM-based tutoring is an effective strategy for developing clinical judgment in nursing students. This new teaching approach can train students to critically think, develop clinical judgment, and prepare for the complex healthcare environment. Therefore, nurse educators should focus on integrating clinical judgment into the prelicensure nursing program curriculum as a priority.

Retracted: Evaluation and Application of College English Mixed Flipping Classroom Teaching Quality Based on the Fuzzy Judgment Model

Retracted: Evaluation and Application of College English Mixed Flipping Classroom Teaching Quality Based on the Fuzzy Judgment Model

The implementation of Tanner's Clinical Judgment Model and the Indonesian version of the Lasater Clinical Judgment Rubric in the clinical setting

A critical component for nurses to provide safe and quality care with strong patient outcomes is clinical judgment. This study aimed to identify the difference in the clinical judgment score in newly graduated nurses by implementing Tanner's Clinical Judgment Model. This study followed an experimental design with a pretest-posttest and involved thirty-two newly graduated nurses from three private hospitals in Indonesia. The intervention group had a scheduled post-conference with their preceptors using Tanner's Clinical Judgment Model's guidelines with high-level, open-ended questions for two consecutive weeks. Independent samples t-test was used to compare pre-and post-test scores utilizing the valid and reliable Indonesia version of the Lasater Clinical Judgment Rubric. There was no significant difference between the pre-test results of the intervention and control groups, with a significance value of 0.647. The results indicated a significant difference in the mean clinical judgment scores between the intervention and control groups after two weeks of intervention, where the intervention group's scores were higher than the control group's with a significance value of <0.001 with a mean difference of 6.75 and CI of 95 % (4.18–9.31). Tanner's Clinical Judgment Model showed a significant impact in increasing clinical judgment scores. The Indonesia version of the Lasater Clinical Judgment Rubric is a valuable tool for assessing and evaluating the development of clinical judgment and provides detailed targeted areas needing improvement. Preceptors and nursing leaders in the clinical setting should continue to use this model and tools to assist newly graduated nurses in developing clinical judgment skills during their transition period.

Mental jenga: A counterfactual simulation model of causal judgments about physical support.

From building towers to picking an orange from a stack of fruit, assessing support is critical for successfully interacting with the physical world. But how do people determine whether one object supports another? In this paper, we develop a counterfactual simulation model (CSM) of causal judgments about physical support. The CSM predicts that people judge physical support by mentally simulating what would happen to a scene if the object of interest was removed. Three experiments test the model by asking one group of participants to judge what would happen to a tower if one of the blocks were removed, and another group of participants how responsible that block was for the tower's stability. The CSM accurately captures participants' predictions by running noisy simulations that incorporate different sources of uncertainty. Participants' responsibility judgments are closely related to counterfactual predictions: a block is more responsible when many other blocks would fall if it were removed. By construing physical support as preventing from falling, the CSM provides a unified account of how causal judgments in dynamic and static physical scenes arise from the process of counterfactual simulation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Aircraft Accessibility Based on Generalized Regression Neural Network

According to the requirements of aircraft accessibility judgment, based on the existing research, this paper proposes two accessibility judgment methods based on the “initial position velocity threshold range” and “expected landing site velocity threshold range”. Firstly, the motion model of the aircraft is established, and the accessibility judgment sample library is established by using the trajectory planning method. Then, the initial and landing velocities are selected as the prediction targets, and the neural network is established to achieve accessibility prediction and judgment. Finally, the simulation analysis of the unpowered aircraft diving stage scene is carried out. The results show that the accessibility judgment model designed in this paper has high prediction accuracy and preliminary accessibility judgment ability. This paper realizes the optimization of the existing scheme and provides a method for the research field of accessibility judgment.

Learning Clinical Judgments With a Lens of Inclusivity.

To prepare practice-ready graduates and promote NCLEX® success, many schools of nursing have adopted a clinical judgment model (CJM) to provide a framework for their curriculum and teaching strategies. Missing from most CJMs are clear principles of justice, equity, diversity, and inclusion (JEDI), imperative to prepare a nursing workforce to care holistically for diverse populations. This article describes the curriculum integration of an adapted model with added JEDI principles. By intentionally integrating the language and understanding of a CJM with JEDI principles, nurse educators can promote the clinical judgment development necessary to prepare graduates to care for diverse populations.

Preparing for the Next Generation NCLEX Using a Virtual Simulation.

Virtual clinical simulation is a digital innovation that augments clinical reasoning and clinical judgment, narrowing the theory-practice gap, preparing new graduates for the Next Generation NCLEX (National Council Licensure Examination), and mitigating the demand for clinical placement and clinical faculty. The purpose of this article is to describe an educational innovation employing a virtual clinical simulation using the tenets of Tanner's Clinical Judgment Model and a National League for Nursing Advancing Care Excellence for Seniors unfolding case. A virtual clinical simulation successfully served to replace direct care clinical for 86 undergraduate nursing students enrolled in a foundation nursing course, augmenting the students' ability to transfer and apply theoretical knowledge to clinical practice. This virtual clinical simulation served as a viable platform for teaching clinical reasoning and clinical judgment, mitigating the education-practice gap for new graduate registered nurses. [J Nurs Educ. 2024;63(7):485-489.].

Fairness in Assessment: Identifying a Complex Adaptive System.

Assessment design in health professions education is continuously evolving. There is an increasing desire to better embrace human judgement in assessment. Thus, it is essential to understand what makes this judgement fair. This study builds upon existing literature by studying how assessment leaders conceptualise the characteristics of fair judgement. Sixteen assessment leaders from 15 medical schools in Australia and New Zealand participated in online focus groups. Data collection and analysis occurred concurrently and iteratively. We used the constant comparison method to identify themes and build on an existing conceptual model of fair judgement in assessment. Fairness is a multi-dimensional construct with components at environment, system and individual levels. Components influencing fairness include articulated and agreed learning outcomes relating to the needs of society, a culture which allows for learner support, stakeholder agency and learning (environmental level), collection, interpretation and combination of evidence, procedural strategies (system level) and appropriate individual assessments and assessor expertise and agility (individual level). We observed that within the data at fractal, that is an infinite pattern repeating at different scales, could be seen suggesting fair judgement should be considered a complex adaptive system. Within complex adaptive systems, it is primarily the interaction between the entities which influences the outcome it produces, not simply the components themselves. Viewing fairness in assessment through a lens of complexity rather than as a linear, causal model has significant implications for how we design assessment programs and seek to utilise human judgement in assessment.

A Novel Multi-Domain Adaptation-Based Method for Blast Furnace Anomaly Detection

In the steelmaking process, ensuring stable and reliable furnace plays a vital role for guaranteeing production quality of steel products. Traditional methods for detecting furnace anomalies in blast furnaces rely on operator judgment models built upon expert knowledge that can be limited by human experience. Moreover, data generated in blast furnace ironmaking process can be multidimensional, non-Gaussian distributed, and periodical, which can be easily affected by environmental and human factors and thus resulting in low accuracy of anomaly detection. Therefore, an online intelligent framework for detecting furnace anomalies is in high need. In this paper, the authors propose a novel anomaly detection method based on a furnace condition parameter-characterization model, a mining model of periodic patterns in the ironmaking process, and a multi-domain adaptive anomaly detection algorithm. They conduct extensive numerical analysis based on real-world production datasets as well to evaluate the effectiveness and accuracy of the method.

Asset Portfolio Investment Strategies Based on Econometric Models

In order to provide traders with a daily trading strategy for gold and bitcoin, a price forecasting model was first developed. After performing ADF stationarity tests and LM tests on returns, the ARMA (3,7) and ARMA (4,5) models are estimated using THE PERTUR (3,7) and ALM (5) models with perturbation terms GARCH (1,1) and vt following a t-distribution. The second step is to build a risk judgement model that considers the impact of the special bull and bear market quotes, sets the bull score and determines its weight as 0.333. The third step is to build a decision model that introduces a purchase score and builds a planning equation based on the fact that gold is only traded on trading days to obtain the final decision. With this model, a principal of $1,000 could end up with an asset of $164,080. MCM/ICM: Procedures and techniques for a great experience. Proving that the model used is the optimal strategy is done in two parts: an accuracy test and an analysis of investment behavior. The accuracy test introduces the regression evaluation metrics MSE and RMSE and the results show that the portfolio investment model is the most accurate. The analysis of investment behavior demonstrates the failure of one-way investments by comparing complete bitcoin investments or gold investments with the information reviewed.

Purely subjective revealed ambiguity

The objective of this paper is to show how ambiguity, and an individual’s response to it, can be modeled formally in the context of a likelihood relation over events. When certain structural conditions are met, we introduce the rational core of a likelihood relation and show that it can be represented by a set of priors, as suggested by Ghirardato et al. (2004). We also prove that a general likelihood relation can be represented in the spirit of Hurwicz’s “optimism index” model (Hurwicz, 1951). We then obtain a fully subjective model of likelihood judgments under ambiguity for ambiguity averse individuals.

Contrasting two models of utilitarian reasoning

One influential framework for examining human moral cognition has been a dual process model, in which utilitarian judgment (e.g., infliction of harm for the greater good) is associated with cognitive control processes, while non-utilitarian judgment (e.g., avoiding such harms) is associated with emotional, automatic processes. Another framework of moral cognition, the two-dimensional model of utilitarian psychology, posits that utilitarian choices may reflect either instrumental harm, i.e., inflicting harm on an individual for the greater good; or impartial beneficence, i.e., impartially and altruistically acting for the benefit of the overall welfare. We evaluated preregistered hypotheses (https://osf.io/m425d) derived from these models of moral cognition in a sample of 275 neurologically healthy older adults. Our results suggest that both the dual process and two-dimensional models provided insights regarding utilitarian reasoning, including three cardinal domains of conflict between utilitarianism and common-sense morality: agent-centered permissions, special obligations, and personal rights. One prediction of the dual process-based model was supported by our findings, with higher emotionality associated with decreased endorsement of utilitarian judgments (b = - 0.12, p < .001). We also found partial support for the two-dimensional model, as utilitarian judgments about dilemmas involving agent-centered permissions and personal rights were dissociated; however, both sets of judgments were associated with utilitarian judgments involving special obligations (p < .001 and p = .008, respectively). We propose that our findings, with support for some elements of the dual process and two-dimensional models, can be integrated into a revised two-dimensional model of utilitarian judgment as including impartial beneficence and acceptance of attributable harms.

Criminal Action Graph: A semantic representation model of judgement documents for legal charge prediction

Semantic information in judgement documents has been an important source in Artificial Intelligence and Law. Sequential representation is the traditional structure for analyzing judgement documents and supporting the legal charge prediction task. The main problem is that it is not effective to represent the criminal semantic information. In this paper, to represent and verify the criminal semantic information such as multi-linked legal features, we propose a novel criminal semantic representation model, which constructs the Criminal Action Graph (CAG) by extracting criminal actions linked in two temporal relationships. Based on the CAG, a Graph Convolutional Network is also adopted as the predictor for legal charge prediction. We evaluate the validity of CAG on the confusing charges which composed of 32,000 judgement documents on five confusing charge sets. The CAG reaches about 88% accuracy averagely, more than 3% over the compared model. The experimental standard deviation also show the stability of our model, which is about 0.0032 on average, nearly 0. The results show the effectiveness of our model for representing and using the semantic information in judgement documents.

Modeling of Clinical Judgment Using Lasso Regression and Reduction of Assessment Items

In the medical field, accidents involving the fall of inpatients around beds have become an increasing problem. To prevent such accidents, nurses record the condition of inpatients using a check sheet referred to as the Fall Assessment Score Sheet (Fall AS). These sheets are utilized for each inpatient and provide useful information for daily clinical judgment. However, compiling these sheets adds to the excessive workload of nurses, as the Fall AS contains numerous items to be checked and assessments frequently necessitate revision. We sought to model the clinical judgment of nurses using Lasso regression, with the aim of reducing the number items of the Fall AS. Three nurses were assigned a questionnaire that included 200 patterns of Fall AS and seven patterns of patient posture, and were requested to fill in the fall risk (0% to 100%) for all 1400 pattern combinations. The clinical judgment model was constructed based Lasso regression, adopting each fall risk and Fall AS as objective and explanatory variables, respectively. The proposed model succeeded in reducing the number of Fall AS items from 50 to 25. Moreover, the mean error of estimated values for the clinical judgment increased by only 3.44% compared with the general least-squares method.

Revising Reflection Assignments to Align With Clinical Judgment Measurement Model Language: Maximizing Critical Thinking, Feedback, and Measurement in Simulation.

Student reflection stimulates self-awareness, deep learning, insight into patient care challenges, and professional role development. As such, reflection assignments are an ideal target for revision using language associated with the steps of the clinical judgment measurement model (CJMM) to maximize critical thinking. Tanner's clinical judgment model aligns with the CJMM and underpins the curriculum in many nursing programs. Faculty used the model to update a simulation reflection assignment template, incorporating CJMM language and facilitating improved measurement of clinical judgment. Substantially improved student responses resulted during an initial quality improvement pilot study.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.

The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Psychoactive Comfort Products or Snacks: How Chinese Young Adults Perceive the Potentially Addictive Nature of E-Cigarettes.

The potential health value and pitfalls of e-cigarettes are currently under dispute in the scientific community. Exploring young adult e-cigarette users' perceptions would assist in adding a public dimension of understanding to the literature and in scientific public health decision making. Therefore, in this study, we collected and analyzed data from interviews with young adult (n = 14) e-cigarette users and found that many referred to e-cigarettes as "snacks," indicating that they considered that both their frequency of use and addiction were manageable and that they could stop using e-cigarettes at any time. To further understand the behavior of Chinese young adults in relation to their perception of e-cigarettes as a "snack", the study developed a social context framework (crossroads model) and psychological judgment model to explain how youth e-cigarette users' perception of "controlled addiction and ready cessation" arises. These models can be used to assess the effectiveness of e-cigarette policy.

Cost supervision mining from EMR based on artificial intelligence technology.

To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring a complete set of inpatient cost supervision methods. To lay the foundation for applying artificial intelligence (AI) technology in medical insurance cost control supervision and provide feasible paths and available tools for medical insurance cost control managers. By way of collecting and cleaning electronic medical record (EMR) data from 2016 to 2018 of a city in East China, focusing on basic patient information and cost information, and using a combination of machine learning modeling and information system construction, the study tries to form a feasible inpatient cost supervision method and operation path. The set of the regulatory method, applied in nursing homes of a city in East China, is compelling. The accuracy rates of rationality judgement in different main diseases are stable up to 80%, the false positive rate is steady within 10%, and rehabilitation fee days of hospitalization, and the number of complications are important factors affecting the rationality of the inpatient cost. The model construction and optimization method combining machine learning and information system can make practical cost rationality judgement on medical institution's inpatient cost data, which can directly reflect the key influencing factors of relevant inpatient costs, and achieve the effect of guiding medical behavior and improving the efficiency of medical insurance fund use.

A Study on the Development of Deep Learning Algorithm for Determining External Quality of Welded Parts Using Transfer Learning

Recently, deep learning has been applied to various welding techniques, such as laser welding, gas metal arc welding (GMAW), and resistance spot welding, and research on automation and quality prediction is being conducted. Even for GMAW, many researchers have attempted to predict quality through X-ray, current, and voltage measurements. If judgment in real time is not necessary, it is most effective to judge the quality of a welded part using an exterior image. Therefore, in this study, a welded appearance quality judgment model was analyzed using image deep learning. Welding defects were classified into pores, overlaps, craters, melting of the base material, cracks, and undercuts, and were divided into 7 categories including normal ones. In constructing the deep-learning model, transfer learning was performed using existing networks, such as ResNet and AlexNet. To improve the accuracy of deep learning, tests were performed while the optimization technique, maximum number of epochs, and minibatch size were changed. It was confirmed that the accuracy of weld defect prediction improved as the minibatch size increased, and the stochastic gradient descent model had the highest accuracy. Increasing the number of data for learning should make the technique of using images to judge the quality of GMAW welds using the proposed model more widely applicable.