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A major problem in employee selection coalesces around convincing decision‐makers (e.g., hiring managers) to use analytically derived models. Existing recommendations in the literature largely focus on convincing executives to adopt analytical models and then exert their top‐down influence on lower‐level hiring decisions. In contrast to these solutions, we explore end user modifiability (i.e., allowing decision‐makers to modify a statistical model before use) as a bottom‐up approach for increasing hiring managers' implementation of analytical recommendations. From a utility standpoint, we consider how incorporating end user modifiability into hiring decisions will result in a less statistically valid, but potentially more valuable, organizational selection process. We explore these ideas in two studies. In Study 1, we experimentally test whether model modification increases decision‐maker reliance on a statistical model, as well as how much decision‐makers need to modify a model in order to use it. In Study 2, we examine the extent that modifiability introduces implicit biases that might adversely affect marginalized groups. Results suggest that modifiability can increase decision‐makers' perceived usefulness of a model and, importantly, that only a small amount of modifiability is needed to elicit this effect. Further, end user modifications were statistically insignificant predictors of hiring rates across race‐based subgroups, though supplementary analyses suggest important cautionary nuance. Given that analytical models are rarely perfectly or wholly implemented, end user modifiability may offer a viable solution for organizations seeking to increase the implementation of algorithmic guidance in selection decisions, even if it deviates modestly from a statistical optimality. [ABSTRACT FROM AUTHOR]

Because Null Hypothesis Significance Tests (NHST) can be misleading particularly when sample sizes are large, reporting effect sizes is recommended. Effect sizes such as Cohen's d, however, are abstract statistics and not relevant to decision-making. This article introduced an alternative formulation of Cohen's d based on a "minimal important percent difference" (MIPD) between groups that has an intuitive, practical meaning. Cohen's d was calculated based on detecting MIPDs of 5% and 10% differences in means. Power analyses were used to determine the number of responses needed to detect differences in means. Power-probability sample size requirements were examined for an 80% and a 50% chance of accepting a difference of MIPD at p <.05. We proposed a four-step process to show how both effect size and NHST jointly contribute to decision-making. To demonstrate the process, data were obtained for 6 years of annual waterfowl surveys in Illinois (N = 17,265, average response rate = 41%). [ABSTRACT FROM AUTHOR]

Data literacy, which is considered among 21st century skills, is becoming increasingly important. In particular, incentives for data-driven decision-making draw attention to data literacy. Data literacy is also critical for teachers who need to use data in educational settings. Therefore, there is a need for data on whether teachers are equipped with data literacy competencies before their service. In this study, pre-service teachers' data literacy competencies were examined. The case study approach was used in the study, which was conducted as qualitative research. The study group consisted of 61 pre-service teachers studying in three different undergraduate programs. Data were collected through the "What do the data say?" activity instrument. Rubrics were used to analyze the data. The findings of the study showed that while there was no difference in terms of using data and data communication, there was a statistically significant difference between the programs in terms of data recognition, comparing data and establishing relationships between data competencies, as well as total data literacy. In addition, it was found that the majority of pre-service teachers were partially inadequate or inadequate in terms of using data, data communication and total data literacy. Nearly half of them were partially inadequate or inadequate in comparing data and establishing relationships between data. The results indicated that preservice teachers have certain deficiencies regarding data literacy. [ABSTRACT FROM AUTHOR]

Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. [ABSTRACT FROM AUTHOR]

The probability of test object compliance with the specified requirements and non-binary decision is a measure of how likely the test object is to meet the criteria for acceptance or rejection based on multiple factors. It is necessary to calculate by using a valid mathematical model that takes into account the test object's characteristics, the test conditions, the test criteria, and the uncertainty of the measurements. The significance of the probability of test object compliance with the specified requirements and non-binary decision can help to evaluate the quality, reliability, and performance of the test object, as well as to support decision making in complex situations. [ABSTRACT FROM AUTHOR]

Patient-physician communication has the potential to improve outcomes and satisfaction through the shared decision-making process (SDM). This study aims to assess the relationship between perception of SDM and demographic, clinical, and patient-reported outcomes in patients undergoing Hallux Valgus (HV) correction. A prospective analysis of 306 patients scheduled for HV surgery was completed. The CollaboRATE score was used to measure SDM. Multivariable linear regression model was used to assess whether SDM scores were associated with preoperative characteristics or postoperative outcome scores. The mean CollaboRATE score was 2.9 (SD 0.9) and did not differ by age, socioeconomic status, or sex. Lower CollaboRATE scores were associated with more symptoms of depression, lower socioeconomic status, and lower general health scores (p-value < 0.05). There was no association between SDM scores and postoperative outcome scores. In this study, patients with depressive symptoms and lower socioeconomic status had worse perceptions of SDM. There was no difference in postoperative outcomes among participants based on SDM scores. Level of Evidence: Level III, prospective observational study [ABSTRACT FROM AUTHOR]

The use of Monte‐Carlo (MC) p$$ p $$‐values when testing the significance of a large number of hypotheses is now commonplace. In large‐scale hypothesis testing, we will typically encounter at least somep$$ p $$‐values near the threshold of significance, which require a larger number of MC replicates than p$$ p $$‐values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 106$$ 1{0}^6 $$) of MC replicates may not be enough to guarantee conclusions reached using MC p$$ p $$‐values. Gandy and Hahn (GH)6‐8 have developed the only method that directly addresses this problem. They defined a Monte‐Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p$$ p $$‐values are different from decisions based on idealp$$ p $$‐values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large‐scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p$$ p $$‐values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study. [ABSTRACT FROM AUTHOR]

Background: To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods: We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results: Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions: The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size. [ABSTRACT FROM AUTHOR]

Aim: To translate and culturally adapt the FRAIL scale into Spanish and perform a preliminary test of diagnostic accuracy in patients admitted to intensive care units. Design: Cross‐sectional diagnostic study. Methods: Five intensive care units (ICU) in Spain were participated. Stage 1: Three native Spanish‐speaking bilingual translators familiar with the field of critical care translated the scale from English into Spanish. Stage 2: Three native English‐speaking bilingual translators familiar with critical care medicine. Stage 3: Authors of the original scale compared the English original and back‐translated versions of the scale. Stage 4: Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the comprehension and relevance of each of the items of the Spanish version in 30 patients of 3 different age ranges (<50, 50–65 and >65 years). Results: The FRAIL scale was translated and adapted cross‐culturally for patients admitted to intensive care units in Spain. The process consisted of four stages: translation, back translation, comparison and pilot test. There was good correspondence between the original scale and the Spanish version in 100% of the items. The participating patients assessed the relevance (content validity) and comprehensibility (face validity) of each of the items of the first Spanish version. The relevance of some of the items scored low when the scale was used in patients younger than 65 years. Conclusions: We have cross‐culturally adapted the FRAIL scale, originally in English, to Spanish for its use in the critical care medical setting in Spanish‐speaking countries. Implications for Professionals: Physicians and nurses can apply the new scale to all patients admitted to the intensive care units. Nursing care can be adapted according to frailty, trying to reduce the side effects of admission to these units for the most fragile patients. Reporting Method: The manuscript's authors have adhered to the EQUATOR guidelines, using the COSMIN reporting guideline for studies on the measurement properties of patient‐reported outcome measures. Patient or Public Contribution: In a pilot clinical study, we applied the first version of the FRAIL‐Spain scale to intensive care unit (ICU) patients. Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the relevance (content validity) and comprehensibility (face validity) of the five items of the first Spanish version. Relevance was assessed using a 4‐point Likert scale ranging from 1 (no relevance) to 4 (high relevance), and comprehensibility was assessed as poor, acceptable or good. Each health professional applied the scale to three patients (total number of patients = 30) of three different age ranges (<50, 50–65 and >65 years) and recorded the time of application of the scale to each patient. Although the frailty scales were initially created by geriatricians to be applied to the elders, there is little experience with their application in critically ill patients of any age. Therefore, more information is needed to determine the relevance of using this scale in critical care patients. In this pilot study, we considered that nurses and critical care physicians should evaluate frailty using this adapted scale in adult patients admitted to the Intensive Care Units. [ABSTRACT FROM AUTHOR]

Since capital may not be abundant for banks, banks have to assess the efficiency of small and medium enterprises (SMEs) to efficiently and profitably allocate available funds. This study aims to generate criteria priority series for the decision process of commercial credit applications (DPCCAs) to conduct statistical hypothesis testing and produce information for researchers, decision-makers/managers. Having lots of criteria in assessing SME performance, analytic hierarchy process (AHP) — a multi-criteria decision-making method — is applied according to experts' views. By determining SMEs' indicator-criteria priorities, this study's priorities give information on SME growth potential, managerial competence and institutionalization which are rarely used in SMEs' credit evaluation in academic studies. This study also evaluates tolerated consistency boundary approach (TCBA) defined in Pekkaya's studies and generates priority-series that allows for statistical testing. The findings reveal that "profitability and business volume" with its sub-criteria of "operating profitability" have the highest priority, and then "stability and intelligence" with "repayment practices of debts" and "total income and stability in profitability" have quite high priorities among seven main- and 26 sub-criteria. This study contains some criteria which are not previously observed in the related literature, incorporates considerably more expert views, and shows to conduct statistical hypothesis tests by using the generated priority series via AHP for each criterion. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether professionally applied topical fluoride‐hesitant caregivers are also hesitant about other forms of fluoride for their child. Methods: This was a mixed methods study of 56 caregivers hesitant about professionally applied topical fluoride for their child recruited from the University of Washington Center for Paediatric Dentistry and Seattle Children's Hospital's Odessa Brown Children's Dental Clinic. A 32‐item semi‐structured interview script was piloted and finalized. One‐time interviews with caregivers were conducted by phone in 2019. Associations between hesitancy of topical fluoride, fluoridated water and toothpaste were assessed quantitatively via two‐tailed chi‐squared tests. Qualitative data were coded using an inductive approach and content analytic methods to investigate reasons for hesitancy. Results: There were significant associations between hesitancy in all three pairwise comparisons of fluoride form (p <.01). Similar proportions of caregivers strongly or somewhat opposed fluoridated water compared with toothpaste (75% and 65%, respectively), but four times as many caregivers strongly or somewhat favoured fluoridated toothpaste compared to water for their child (25% and 7%, respectively). Concerns about harm were the most common reason caregivers opposed both fluoridated water and toothpaste. However, fluoride‐hesitant caregivers reported being more comfortable with fluoridated toothpaste because amount and frequency can be controlled, and ingestion can be prevented. Conclusions: Professionally applied topical fluoride hesitancy is significantly associated with fluoridated water and toothpaste hesitancy, but caregivers who were hesitant about topical fluoride was more comfortable with fluoridated toothpaste than fluoridated water for their child. [ABSTRACT FROM AUTHOR]

Retrospective patterns are commonly investigated to validate fish stock assessment models. A widely applied measure for retrospective bias is Mohn's ρ and corresponding retrospective plots. However, retrospective patterns can be interpreted differently by experts. To make decisions regarding significant retrospective patterns less subjective, we proposed a post-sample Mohn's ρ significance test. As case studies, we applied the state space assessment model SAM with data on Northeast Arctic cod and Norwegian coastal cod north of 67°N. We showed that the acceptance regions of Mohn's ρ depends on both the data available and the assessment model complexity. We also assessed the test power under a range of assumption violations and conclude that Mohn's ρ is useful for detecting violations associated with bias, but not for violations associated with variances and correlations. [ABSTRACT FROM AUTHOR]

Background: Family intensive care unit (ICU) syndrome, a comorbid response to another person's stay in the ICU, is characterized by emotional distress, poor sleep health, and decision fatigue. Objectives: This pilot study examined associations among symptoms of emotional distress (anxiety and depression), poor sleep health (sleep disturbance), and decision fatigue in a sample of family members of patients in the ICU. Methods: The study used a repeated-measures, correlational design. Participants were 32 surrogate decision makers of cognitively impaired adults who had at least 72 consecutive hours of mechanical ventilation within the neurological, cardiothoracic, and medical ICUs at an academic medical center in northeast Ohio. Surrogate decision makers with a diagnosis of hypersomnia, insomnia, central sleep apnea, obstructive sleep apnea, or narcolepsy were excluded. Severity of symptoms of family ICU syndrome was measured at 3 time points in 1 week. Zero-order Spearman correlations of the study variables were interpreted at baseline and partial Spearman correlations of study variables were interpreted 3 days and 7 days after baseline. Results: The study variables showed moderate to large associations at baseline. Baseline anxiety and depression were associated with each other and with decision fatigue at day 3. Baseline sleep disturbance was associated with anxiety, depression, and decision fatigue at day 7. Conclusions: Understanding the temporal dynamics and mechanisms of the symptoms of family ICU syndrome can inform clinical, research, and policy initiatives that enhance the provision of family-centered critical care. [ABSTRACT FROM AUTHOR]

Technological orientation is one of the competitive functions in the capital market, which can be considered a basis for evaluating the advantages of companies in achieving strategic goals. This study aims to investigate the effect of geographical location on technology capabilities. It uses a resource-based view (RBV) to develop the research hypothesis. The research hypothesis was first tested using the data envelopment analysis (DEA) method to measure the technology capacities of companies and then by considering that the research dependent variable was measured as 0 and 1, based on logistic regression. The result of testing the research hypothesis was that the significance level of the coefficient of variable geographical distance (DIS) negatively and significantly affects the technical efficiency vector (TEV). Based on the test of the resource reliance theory, this result indicates that a geographical distance from the center can affect companies' access to competitive resources and, in this situation, reduce the capabilities of their core technology compared to competitors. This study is one of the few studies that examine the effect of technology capability on the geographical distance of petrochemical companies. Its results can help strengthen companies' strategic decisions. Therefore, this study can be considered innovative because it helps develop theoretical literature and strategic decision-making implications. [ABSTRACT FROM AUTHOR]

How should political decisions be made to ensure a high level of legitimacy in the eyes of ordinary citizens? In order to answer this question, we conducted six focus groups (N = 29) with adults (20-78 years old). We analyzed data using a thematic analysis, within the essentialist/realist framework and focused on the explicit meanings of the data. Two specific issues were explored: the adoption of the Euro and acceptance of Syrian orphan refugees. The bottom-up analysis revealed that participants considered two strategies of political decision-making (direct vote and representation based) and discussed the pros and cons of each process in detail. The results point out the importance of public deliberation, transparency, and the source of decision-making in evaluating the overall legitimacy of decisions-making process. Further, unlike popular belief that citizens are thirsty for direct democracy our results suggest that people are rather hesitant about placing big decisions into the hands of ordinary citizens, nor do they want to be burdened with making decisions about issues that might not affect them directly. Rather, people described representation-based decisions as legitimate if condition of transparency, deliberation, and trust in politicians is met. [ABSTRACT FROM AUTHOR]

Background: Concussion public policies have been developed to address the burden of concussions. The aim of the present study was to examine implementation compliance, barriers, and facilitators of Canada's first concussion public policy, Ontario's Policy/Program Memorandum 158: School Board Policies on Concussion (PPM158). Methods: An electronic survey was sent to 515 randomly selected elementary and high school principals across specific geographic, language, and publicly funded school types in Ontario. Data were analyzed using both qualitative and quantitative methods. Results: One hundred and thirty‐five principals responded to the survey (26%). Concussion education was provided to teachers in 81% of schools, to students in 83%, and coaches in 79%. Additionally, 89% reported having a return‐to‐learn protocol in place and 90% reported having a return‐to‐play protocol. Implementation barriers included difficulties in providing concussion education to parents (42%), obtaining notes from physicians, and maintaining the volume of documentation. Eighty‐seven percent of respondents believed that PPM158 improves student well‐being. Conclusions: Identified implementation barriers and facilitators can inform concussion policy practices to improve student well‐being. We recommend: (1) an appointed concussion policy lead at each school, (2) electronic documentation, (3) determining the optimal education format to improve parent/guardian education, (4) fostering relationships between schools and health care professionals, and (5) student concussion education in every grade in Ontario schools. [ABSTRACT FROM AUTHOR]

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Purpose: To examine the impact of in situ simulation (ISS) with scripting on nursing staff's knowledge and confidence to initiate rapid response teams (RRTs) immediately after identifying patient condition deterioration. Background/Significance: Failure to rescue (FTR) related to delays in activation of RRT is on the rise, leading to poor patient outcomes. Lack of confidence, knowledge, and empowerment are associated with delayed activation of RRTs. As such, the nursing staff's confidence is integral in activating RRTs and FTR prevention. In situ simulation may help nurses increase their confidence, thus empowering timely RRT initiation. Methods: This quantitative pretest-posttest study used a convenience sample of nurses and nursing assistants. First, participants completed the Rapid Response Team Survey (RRTS) pretest. Then, they participated in the ISS scenario. Lastly, they completed debriefing and the RRTS posttest. Results: Pearson's correlation results showed no significant relationships between the variables. Dependent t-test results showed statistically significant increases between the pretest and posttest means (Part l, t = -5.51, p < .001, MD = 1.32; Part 2, t = -1.04, p < .01, MD = 3.1). These results suggest that ISS with scripting increased participants' knowledge and confidence in early activation of RRTs. Additionally, staff reported feeling more confident and empowered regarding future RRT decision-making and communication with other healthcare colleagues. Discussion: Early activation of RRTs prevents FTR. The results of this study suggest ISS with scripting increases staff's knowledge, confidence, and empowerment to activate RRTs. We recommend that hospital organizations adopt ISS with scripting to empower nurses to activate RRTs to prevent FTR. [ABSTRACT FROM AUTHOR]

Australians fail to meet the daily recommended two and five serves of fruit and vegetable respectively, which increases mortality risk for non‐communicable diseases such as cardiovascular disease and type 2 diabetes. This study aimed to evaluate the effectiveness of an online intervention delivered through social media on food literacy and fruit and vegetable consumption in Australian adults. In a pre‐post single group experimental study, 29 participants completed the "online MedDiet challenge", a four‐week intervention delivered via Facebook. Infographics, recipes and informational videos aligned with food literacy concepts related to the Mediterranean Diet were shared with participants. Outcome measures included a validated food literacy questionnaire with two questions from the National Nutrition Survey to record fruit and vegetable consumption. The mean age of participants was 52 years (range: 25–67 years). Post intervention, food literacy improved between 21%–45% across each survey component. Participants also reported an increase in fruit and vegetable consumption by 0.6 and 1.3 serves per day (p < 0.05) respectively. Social media holds potential for increasing fruit and vegetable consumption in adults through food literacy. Future research should focus on longer studies and larger cohorts to confirm that food literacy plays a key component to maintain sustainability of such interventions. [ABSTRACT FROM AUTHOR]

We investigate what fraction of all significant results in website A/B testing is actually null effects (i.e., the false discovery rate (FDR)). Our data consist of 4,964 effects from 2,766 experiments conducted on a commercial A/B testing platform. Using three different methods, we find that the FDR ranges between 28% and 37% for tests conducted at 10% significance and between 18% and 25% for tests at 5% significance (two sided). These high FDRs stem mostly from the high fraction of true null effects, about 70%, rather than from low power. Using our estimates, we also assess the potential of various A/B test designs to reduce the FDR. The two main implications are that decision makers should expect one in five interventions achieving significance at 5% confidence to be ineffective when deployed in the field and that analysts should consider using two-stage designs with multiple variations rather than basic A/B tests. This paper was accepted by Eric Anderson, marketing. [ABSTRACT FROM AUTHOR]

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman's rank correlation test assesses the statistical significance of two rankings sets. [ABSTRACT FROM AUTHOR]

In this paper, Kruskal-Wallis test is extended to deal with neutrosophic data in single valued form using score, accuracy and certainty functions to calculate ranks of SVNNs, also Mann-Whitney test is extended to deal with same data type which makes it possible to do a post-hoc test after rejecting null hypothesis using Neutrosophic Statistics Kruskal-Wallis test. Numerical examples were successfully solved showing the power of this new idea to deal with SVNNs and make statistical decisions on them. [ABSTRACT FROM AUTHOR]

An editorial is presented on expecting manuscripts for using estimation methods instead of null hypothesis statistical tests. Topics include statistical inference being the process of making inferences about populations using data from samples; and availability of alternative methods of statistical inference and promotion of the methods in statistical, medical, and physiotherapy journals.

PURPOSE Men with metastatic castration-resistant prostate cancer increasingly encounter complex treatment decisions. Consultation audio recordings and summaries promote patient informed decision making but are underutilized. Mobile recording software applications may increase access. Little is known regarding the feasibility of implementation in clinical encounters. METHODS We conducted a mixed-methods pilot study in men with progressive metastatic castration-resistant prostate cancer. We instructed patients to use a mobile software application to record an oncology visit. Patients could share the recording with our patient scribing program to receive a written summary. We assessed feasibility and acceptability with postvisit surveys. We measured patient-reported helpfulness of the intervention in decision making and change in Decisional Conflict Scale-informed subscale. We conducted semistructured interviews to explore implementation and analyzed transcripts using thematic analysis. RESULTS Across 20 patients, 18 (90%) recorded their visits. Thirteen of 18 (72%) listened to the recording, and 14 of 18 (78%) received a summary. Eighteen of 20 (90%) visits were telehealth. Fourteen patients (70% of all 20; 78% of 18 question respondents) found the application easy to use. Nine patients (50% of 18 recording patients; 90% of 10 question respondents) reported that the recording helped treatment decision making. Decisional conflict decreased from baseline to 1-week postvisit (47.4-28.5, P, .001). Interviews revealed benefits, facilitators, contextual factors, and technology and patient-related barriers to recordings and summaries. CONCLUSION In this single-institution academic setting, a mobile application for patients to record consultations was a feasible, acceptable, and potentially valued intervention that improved decision making in the telehealth setting. Studies in larger, diverse populations are needed. [ABSTRACT FROM AUTHOR]

Aim: To examine the diagnostic reasoning skills of two nurse practitioner student cohorts. Introduction: Nurse practitioners continue to play a pivotal role in health care provision. Diagnostic reasoning is a core skill of advanced practice. A comparative diagnostic reasoning study between two student cohorts was undertaken between 2018 and 2019. Methods: A validated diagnostic reasoning scale was completed by nurse practitioner students in the United Kingdom and the United States of America at the beginning and end of their clinical placements. The study utilized descriptive quantitative statistics from the data submitted by 22 survey respondents from an online survey. Results: Analysis of the diagnostic reasoning skills suggested there was a difference in the structural memory scores when compared with the beginning of their clinical placements and the final semester of their clinical placements. Conclusion: Diagnostic reasoning skills are a core skill of advanced practice. As students progress through their training, these skills improve. Despite the statistical difference in diagnostic reasoning scores, caution should be made in determining any larger implications due to the small number of participants in this study. Further study is needed in this area. Summary statement: What is already known about this topic? Diagnostic reasoning is a core skill for nurse practitioners.There is minimal research in this area.Diagnostic accuracy is vital for ensuring patient safety. What this paper adds? Structural memory scores improved at the end of nurse practitioner clinical placements. What are the implications of this paper for policy/practice/research/education? Further global research in the field of diagnostic reasoning for nurse practitioners and nurse practitioner students is needed to validate these findings.It is important to ensure nurse practitioners have competency in diagnostic reasoning to reduce diagnostic error.Curricula could be designed to incorporate the use of the DTI to evaluate a student's progress throughout the clinical portion of the nurse practitioner programme. [ABSTRACT FROM AUTHOR]

Introduction: Simon's design has been widely used in oncology to conduct single arm phase II trials and to make Go/No-Go development decision. Other authors have proposed designs with decision-making frameworks that include a third, "Consider" outcome. For results in the Consider zone, a final Go/No-Go development decision must still be made; however it is typically a subjective decision based on the totality of data and the development landscape. Under this framework, the probability of continuing development when the candidate therapy is truly ineffective or the probability of stopping development when the candidate therapy is truly effective is undefined. Methods: We use a motivating example to compare end of trial decision-making between Simon's two-stage approach and a Multilevel outcome approach. We present the minimum and maximum development decision error probabilities by varying whether candidates that end in the Consider zone would ultimately continue with development or not. Results: The Multilevel approach typically requires fewer patients, but the risk of making an incorrect drug development decision is inflated above the statistically defined Type I and Type II error rates. Compared to a Type I error rate of 20%, the Multilevel trial's maximum probability of moving forward with an ineffective therapy is 22%, 27%, and 36% for Consider zone sizes of 10%, 20%, and 30%, respectively. Conclusion: The Multilevel approach provides flexibility in interpreting moderate efficacy results. However, the flexibility is accomplished with a lower sample size and corresponding uncertainty in the trial outcome that increases the risk of incorrect drug development decisions. [ABSTRACT FROM AUTHOR]

Three experiments were conducted to test whether an enhanced degree of fair behavior could be obtained by making justice a goal, whether consciously set, primed, or both. Each experiment assessed fairness in a competitive negotiation context. All participants, across the three experiments, were asked to attain a base-level performance goal. The first experiment examined how a negotiation is affected by a consciously set goal for fairness as well as a primed fairness goal. The results revealed that both the conscious and the primed goal enhanced a participant's fairness. The second and third experiments examined the underlying mediating mechanisms of the effects found in the first experiment. Overall, the results of three experiments indicate that both conscious and primed goals, individually or in combination, can increase fair behavior by enhancing justice saliency. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this article, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomized, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under consideration. We propose using distributional discrepancy to characterize the commensurability between subtrials for appropriate borrowing of information through a spike-and-slab prior, which is placed on the prior precision factor. When the basket trial has at least three subtrials, commensurate priors for point-to-point borrowing are combined into a marginal predictive prior, according to the weights transformed from the pairwise discrepancy measures. In this way, only information from subtrial(s) with the most commensurate treatment effect is leveraged. The marginal predictive prior is updated to a robust posterior by the contemporary subtrial data to inform decision making. Operating characteristics of the proposed methodology are evaluated through simulations motivated by a real basket trial in chronic diseases. The proposed methodology has advantages compared to other selected Bayesian analysis models, for (i) identifying the most commensurate source of information and (ii) gauging the degree of borrowing from specific subtrials. Numerical results also suggest that our methodology can improve the precision of estimates and, potentially, the statistical power for hypothesis testing. [ABSTRACT FROM AUTHOR]

Urban congestion issue is considered as part of major issues that affects the urban development progress and also the living qualities. One of the impacts is reducing the properties prices due to demolishing in property values. This paper provides an insight onto the possibility to simulate the changes of property prices due to variation in different traffic congestion conditions. Firstly, a number of discussions are conducted onto lack of proper analysis tool in assessing the relationship between the extent of urban traffic congestion condition and the fluctuations of property prices in urban areas. Secondly, the aim of this paper is to simulate and generate a modelling framework in order to fill up the niche areas in order to justify the validation of this tool in analyzing the implication of urban traffic congestions onto the changes of property prices. In order to ensure the research work can be implemented, thus a combination of traffic congestion data and property price data which are further integrated using Overlay Mapping Method by referring to i-PLAN, and outcomes are further evaluated through correlation analysis and hypothesis testing. Based on these research inputs, it is thus estimated that generated outputs in index format is able to provide an important guideline for the further developer in making their decisions to acquire property for development. [ABSTRACT FROM AUTHOR]

Introduction: In our research, we investigated the components, executive functions, and cognitive abilities underlying decision-making in sports performance using a computer-based test system. To define the athlete experience, we classified athletes according to an exact, unambiguous definition following international terminology, based on their performance, achievement, playing age, and sport's popularity. We hypothesize that team athlete with significant athletic experience will perform better on the executive function test. Methods: A cross-sectional study was conducted on team athletes N=52. The VTS-DT test was used to measure executive function. The classification of athlete experience was based on an internationally accepted taxonomy defined with scientific precision (SWANNN, 2015). Results: When classified by athlete experience, the athletes we studied fell into amateur and elite categories. The DT/S2 scores of the two groups were compared based on each factor. We obtained trend-like correlations for the factors 'number of reactions', 'number of stimulations,' and 'number of good responses. Correlation analysis was performed between the factors on a group-by-group basis. When examining the relationship between reaction time, we found that there is a strong relationship with the number of stimulations (amateurs: r = -0.80; elite athletes: r = -0.87) since the faster someone is (i.e., the less reaction time), the more times they can respond to stimulations. Looking at the results for elite athletes, we also observe a moderately strong relationship between reaction time and the number of good responses (r = -0.68). We may suggest that experienced athletes could produce better responses faster than less professional athletes. Conclusions: Our hypotheses were partially confirmed, as we found a trend-like correlation that elite athletes scored higher on the DT test, which examined athletes' executive functions in a complex, adaptive way. Our research demonstrates that, on the one hand, it is worthwhile for coaches to build on experienced athletes when assembling a team, in addition to the momentum of young athletes, and that the development of executive functions and cognitive skills can improve the performance of athletes. [ABSTRACT FROM AUTHOR]

Introduction/Objectives: Due to the risks associated with sedative hypnotic medications in older adults, our study evaluated the impact of a multidisciplinary approach to deprescribing in geriatric primary care clinics. Methods: The study was a retrospective review of older adults at 2 academic, geriatric primary care clinics who were prescribed sedativehypnotic medications. Patients were mailed an education packet of information that included working with the clinical pharmacy team, behavioral health team, or both in order to deprescribe their sedative-hypnotic medication. The study assessed the rate of discontinuation of sedative-hypnotic medications between the different intervention groups. Results: The study included 93 older adults with a mean age of 81.3 years and 39.8% discontinuation rate of their medication. The number of falls decreased in patients who discontinued use compared to when previously using a sedative hypnotic medication. Conclusion: Patients are more likely to discontinue their sedative hypnotic medication with a multidisciplinary approach, specifically with primary care provider support. [ABSTRACT FROM AUTHOR]

Aim: To describe the impact of the COVID‐19 pandemic on stress, resilience and depression in health professionals from a public hospital in Barcelona, Spain after the first peak of pandemic. Background: The COVID‐19 pandemic in Spain has pushed boundaries in health systems and, especially, for health professionals. Analysis of resilience as an individual resource and it is essential to understand the mechanisms that make staff react unfavourably to stressors caused by the pandemic. Design: A descriptive cross‐sectional study was designed. Participants: Health professionals supervised by the nursing department, including registered nurses, health care assistants, health technicians, final year nurse student nurses, foreign nurses, and other nurse‐related health workers. Methods: The study complies with the STROBE checklist for cross‐sectional studies. An online survey was administered to all health professionals supervised by the nursing department between 6 and 27 May 2020. The survey included the ER‐14 Resilience Scale, the widely‐used PHQ‐9 depression scale, the Spanish version of the Nursing Stress Scale, and an ad‐hoc questionnaire to obtain sociodemographic and occupational variables. Results: A total of 686 participants answered the survey. Resilience was high or very high in health professionals, with an inverse correlation with stress and depression scores. Personal on fixed shifts showed better resilience. The most stressed health professionals were full‐time registered nurses, followed by health care assistants. Up to 25% of nursing professionals had depression. Conclusion: Our study showed a high degree of resilience among nurse professionals despite the overwhelming nature of the COVID‐19 pandemic. Relevant signs of depression and stress were detected among participants. Occupational factors heavily influenced nurses' resilience, stress and depression. Implications for Nursing & Health Policy: Government policy shifts are needed in Spain to improve nurses' workforce conditions, enhance the ratio of nurses to patient numbers, and avoid workforce losses. Maintaining the resilience of health professionals would assist in improving their health and their capacity to possible future emergency situations. [ABSTRACT FROM AUTHOR]

Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision-making. In the largely extended null hypothesis significance testing for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS. [ABSTRACT FROM AUTHOR]

In 2015, South Carolina passed the Domestic Violence Reform Act giving officers increased arrest discretion and rescinding mandatory arrest laws for intimate partner violence (IPV). Analyses using incident reports (2016/2017) from South Carolina show that officers primarily rely on the presence of five variables to determine arrest: children, injury, property destruction, offender at scene, and weapons. Cases with Black female victims are more likely to result in arrest, suggesting that Black males are being criminalized. We conclude that officers are reluctant to use individual discretion and rely on a form of structured discretion provided by administrators that shapes local culture and decision-making. [ABSTRACT FROM AUTHOR]

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If artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal 'Goldilocks' zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI's influence, raising important implications and new directions for research on human–AI interaction. [ABSTRACT FROM AUTHOR]

The discussion on the use and misuse of p-values in 2016 by the American Statistician Association was a timely assertion that statistical concept should be properly used in science. Some researchers, especially the economists, who adopt significance testing and p-values to report their results, may felt confused by the statement, leading to misinterpretations of the statement. In this study, we aim to re-examine the accuracy of the p-value and introduce an alternative way for testing the hypothesis. We conduct a simulation study to investigate the reliability of the p-value. Apart from investigating the performance of p-value, we also introduce some existing approaches, Minimum Bayes Factors and Belief functions, for replacing p-value. Results from the simulation study confirm unreliable p-value in some cases and that our proposed approaches seem to be useful as the substituted tool in the statistical inference. Moreover, our results show that the plausibility approach is more accurate for making decisions about the null hypothesis than the traditionally used p-values when the null hypothesis is true. However, the MBFs of Edwards et al. [Bayesian statistical inference for psychological research. Psychol. Rev. 70(3) (1963), pp. 193–242]; Vovk [A logic of probability, with application to the foundations of statistics. J. Royal Statistical Soc. Series B (Methodological) 55 (1993), pp. 317–351] and Sellke et al. [Calibration of p values for testing precise null hypotheses. Am. Stat. 55(1) (2001), pp. 62–71] provide more reliable results compared to all other methods when the null hypothesis is false. [ABSTRACT FROM AUTHOR]

Evidence-based medicine frequently uses statistical hypothesis testing. In this paradigm, data can only disconfirm a research hypothesis' competitors: One tests the negation of a statistical hypothesis that is supposed to correspond to the research hypothesis. In practice, these hypotheses are often misaligned. For instance, directional research hypotheses are often paired with non-directional statistical hypotheses. Prima facie, one cannot gain proper evidence for one's research hypothesis employing a misaligned statistical hypothesis. This paper sheds lights on the nature of and the reasons for such misalignments and it provides a thorough analysis of whether they pose a threat to evidence-based medicine. The upshots are that the misalignments are often hidden for clinicians and that although some cases of misalignments can be partially counterbalanced, the overall threat is non-negligible. The counterbalances either lead to methodological inadequacy (in addition to the misalignment), loss of statistical power, or involve a (potential) lack of information that could be crucial for decision making. This result casts doubt on various findings of medical studies in addition to issues associated with under-powered studies or the replication crisis. [ABSTRACT FROM AUTHOR]

Typical problems of the theory of statistical hypothesis testing are considered. All these problems belong to the same object area and are formulated in a single system of axioms and assumptions using a common linguistic thesaurus. However, different approaches are used to solve each of these problems and a unique solution method is developed. In this regard, the work proposes a unified methodological approach for formulating and solving these problems. The mathematical basis of the approach is the theory of continuous linear programming (CLP), which generalizes the known mathematical apparatus of linear programming for the continuous case. The mathematical apparatus of CLP allows passing from a twopoint description of the solution of the problem in the form {0; 1} to a continuous one on the segment [0; 1]. Theorems justifying the solution of problems in terms of CLP are proved. The problems of testing a simple hypothesis against several equivalent or unequal alternatives are considered. To solve all these problems, a continuous function is introduced that specifies a randomized decision rule leading to continuous linear programming models. As a result, it becomes possible to expand the range of analytically solved problems of the theory of statistical hypothesis testing. In particular, the problem of making a decision based on the maximum power criterion with a fixed type I error probability, with a constraint on the average risk, the problem of testing a simple hypothesis against several alternatives for given type II error probabilities. The method for solving problems of statistical hypothesis testing for the case when more than one observed controlled parameter is used to identify the state is proposed. [ABSTRACT FROM AUTHOR]

Background: Musculoskeletal disorders (MSDs) are the leading cause of pain and disability among nurses and are frequently accompanied by depression. However, the association between the comorbidity of MSDs and depression and working conditions has not been studied, which was the aim of this study. Methods : In 2015, all nurses (n = 1,102) employed at a community hospital in the Northeast United States were invited to participate in this cross-sectional study. A survey was distributed in which participants were asked to report on MSDs, depressive symptoms, as well as subjective working conditions assessed including physical demands, psychological demands, decision authority, social support, and work–family conflict. Findings : 397 nurses responded (36%), and the prevalence of the comorbidity of MSDs and depression was 14.5%. Poisson regression (PR) models suggested that work–family conflict was associated with increased risk of the comorbidity (PR = 2.18; 95% confidence interval [CI] = 1.33–3.58), as was 8-hour night shift (PR = 2.77; 95% CI = [1.22, 6.31]) or 12-hour day shift (PR = 2.20; 95% CI = [1.07, 4.50]). Other working conditions were not directly associated with the comorbidity. Conclusions/Application to Practice : The comorbidity of MSDs and depression is prevalent among hospital nurses, and work–family conflict and working night shift or longer shifts were significantly associated with this. Effective workplace programs are needed to address nurses' working conditions to reduce their work–family conflict, thereby improving their musculoskeletal and mental health. [ABSTRACT FROM AUTHOR]

• A new smooth distance-based class-wise statistic is introduced to measure the coherence of each class. • A variant of Extended Nearest Neighbor algorithm with metric learning is developed for classification. • The gradient of the loss function is derived and an iterative learning algorithm is developed. • Extensive experimental results with significance tests on real-life datasets demonstrates the effectiveness of the proposed approach. Recent studies have shown that extended nearest neighbor (ENN) method is able to improve the classification performance over traditional k -nearest neighbor (KNN) methods, due to its novel "two-way communication" decision making process. The ENN method classifies a test data sample by maximizing the intra-class coherence gain over the whole data set. However, the original ENN method, like KNN, is a type of instance-based learning algorithm without a training stage. This paper presents a variant of ENN method, called M aximum intra-class C oherence E xtended N earest N eighbor (MCENN), which incorporates distances between individual data sample and its nearest neighbors into the decision making process and introduces a novel distance metric learning algorithm as a training process to learn an optimal linear transformation that maximizes the overall intra-class coherence of training data. We demonstrate that the proposed MCENN approach is able to improve the discriminative performance for pattern recognition. Experimental results on real-life data sets demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Although choosing between several alternatives is the basic and most common process of decision-making, it has been well established that when having to decide, people are prone to various biases and seldom elect an alternative rationally. One of the most common and well-researched biases is the 'choice-bias' and many researches demonstrated its effect on decision-makers (both high-level and daily decisions) of the previous generations. This research examined the hypothesis that since the psychological development of 'generation Y' differs significantly from the researched generations, youth today are more resistant to this specific bias. A lifetime of constant exposure to the overabundance of suggestions and opportunities may have influenced decision-making capabilities. A study conducted on two separate groups of students (256 participants) revealed that no choice bias was observed, unlike similar studies conducted with previous generation populations. [ABSTRACT FROM AUTHOR]

The evaluation of geoscience data is a far-reaching topic which cannot be systematically covered. The purpose of inferential statistics is to harness useful information from data for making decisions. This paper conducts in-depth statistical study for the Bursa-Wolf and Molodensky Badekas models of the three-dimensional transformation parameters. We also considered the combined and observation equations scenarios of these methods for the comparative study. Four key indicators are conducted to evaluate the performance of the two transformation models according to the residual results. These include root mean square error (RMSE), paired t-test, Wilcoxon signed-rank test and the Cohen's d effect size measure. RMSE evaluation is based on the mean difference between model estimates and observed values. The correlations in the model results is investigated based on paired t-test. Wilcoxon signed-rank test assesses the statistical significance of the model's paired differences. To estimate the effect size of the performance differences, Cohen's d measures are computed. Further, the residuals of the estimated parameters are plotted according to their respective control points. The inference results of these tests generally show that Badekas transformation approach is more precise than Bursa-Wolf. Specifically, Badekas combined case is the most precise, followed by its observation case, then Bursa-Wolf combined and finally its observation case is the least performing model. The application of various data analysis and statistical verifications make the task of data interpretation and best model selection easier. [ABSTRACT FROM AUTHOR]

Many studies have confirmed the positive effects of comprehensive sexuality education on the development of children's sexuality, such as the acquisition of sexual health knowledge. However, little is known about the impact of comprehensive sexuality education on children's social development, although several core aspects of the approach stress social concepts such as fairness, respect and equality. This study examined whether comprehensive sexuality education could weaken the effect of in-group bias on social decision-making towards friends and strangers. Compared to the students in a control group, who never received comprehensive sexuality education, we found that students who received six years (72 lessons) of comprehensive sexuality education had a less in-group bias towards strangers regarding trust and fairness as reflected in a trust game, an ultimatum game and a dictator game. These results suggest that comprehensive sexuality education weakened the effect of in-group bias in the experimental group and encouraged egalitarianism in the experimental group's interpersonal cooperation and social decision-making. These findings suggest that comprehensive sexuality education could positively impact children's interpersonal attitudes and contribute to their social development. [ABSTRACT FROM AUTHOR]

A risk‐based decision‐making mechanism capable of accounting for uncertainty regarding local conditions is crucial to water resources management, regulation, and policy making. Despite the great potential of hydrogeological models in supporting water resources decisions, challenges remain due to the many sources of uncertainty as well as making and communicating decisions mindful of this uncertainty. This paper presents a framework that utilizes statistical hypothesis testing and an integrated approach to the planning of site characterization, modeling prediction, and decision making. Benefits of this framework include aggregated uncertainty quantification and risk evaluation, simplified communication of risk between stakeholders, and improved defensibility of decisions. The framework acknowledges that obtaining absolute certainty in decision making is impossible; rather, the framework provides a systematic way to make decisions in light of uncertainty and determine the amount of information required. In this manner, quantitative evaluation of a field campaign design is possible before data are collected, beginning from any knowledge state, which can be updated as more information becomes available. We discuss the limitations of this approach by the types of uncertainty that can be recognized and make suggestions for addressing the rest. This paper presents the framework in general and then demonstrates its application in a synthetic case study. Results indicate that the effectiveness of field campaigns depends not only on the environmental performance metric being predicted but also on the threshold value in decision‐making processes. The findings also demonstrate that improved parameter estimation does not necessarily lead to better decision making, thus reemphasizing the need for goal‐oriented characterization. Key Points: We present the risk‐based data acquisition design evaluation (RDADE) framework to integrate stochastic analyses with defensible decisionsWe find that improved parameter estimation does not always guarantee lower decision riskWhen dealing with extreme events with high consequences, it is advantageous to improve system resiliency in addition to modeling accuracy [ABSTRACT FROM AUTHOR]

Background/purpose: Research from other disciplines demonstrates that ethical position, idealism, or relativism predicts ethical decision-making. Individuals from diverse cultures ascribe to various religious beliefs and studies have found that religiosity and culture affect ethical decision-making. Moreover, little literature exists regarding undergraduate nursing students' ethical position; no studies have been conducted in the United States on students' ethical position, their self-identified culture, and intrinsic religiosity despite an increase in the diversity of nursing students across the United States. Participants and Research Context Objectives: The study's two aims were to determine the relationship of self-identified culture, religiosity, and ethics position of undergraduate nursing student and whether students' level of education and past ethics courses taken related to idealism. Two hundred and twelve volunteer undergraduate students participated. Research design: A descriptive cross-sectional study was designed for participants who completed the Ethical Position Questionnaire, The intrinsic subscale of the Religious Orientation Scale, and a Demographic, Cultural, Ethnicity Form. To test the five hypotheses, analyses included t-tests, correlations, and ANOVA. Ethical Considerations: The study was approved by the Institutional Review Board at Adelphi University. Results: Idealism and intrinsic religiosity were significantly related. Differences were observed for intrinsic religiosity and idealism for cultural identity and cultural dimensions such as parents' place of birth, and if participants were US born. Students' level of education or participation in past courses on ethics did not influence idealism. Conclusions: The study's findings were similar to most of the research from other disciplines on culture, ethics position, and religiosity. Generic courses on ethics taken prior to clinical work may not assist nursing students in integrating principles into complex ethical dilemmas. Self-identified culture, religion, and intrinsic religiosity related to ethics position; completing ethics courses and level of education, juniors compared with seniors, did not influence idealism. Faculty should consider integrating students' culture, religious orientation, and ethics position into teaching ethics for all levels of nursing education. [ABSTRACT FROM AUTHOR]