Distribution Of Preferences Research Articles

Collaboration between clinicians and vision-language models in radiology report generation.

Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial intelligence (AI)-generated reports. We build a state-of-the-art report generation system for chest radiographs, called Flamingo-CXR, and perform an expert evaluation of AI-generated reports by engaging a panel of board-certified radiologists. We observe a wide distribution of preferences across the panel and across clinical settings, with 56.1% of Flamingo-CXR intensive care reports evaluated to be preferable or equivalent to clinician reports, by half or more of the panel, rising to 77.7% for in/outpatient X-rays overall and to 94% for the subset of cases with no pertinent abnormal findings. Errors were observed in human-written reports and Flamingo-CXR reports, with 24.8% of in/outpatient cases containing clinically significant errors in both report types, 22.8% in Flamingo-CXR reports only and 14.0% in human reports only. For reports that contain errors we develop an assistive setting, a demonstration of clinician-AI collaboration for radiology report composition, indicating new possibilities for potential clinical utility.

A cross-sectional time series of cardiometabolic health education format preferences across sociodemographic groups.

Health education is integral to cardiometabolic disease (CMD) management. This study aimed to assess whether and how education preferences have changed over time, and whether trends differ by sociodemographic characteristics (education status, age, ethnicity, and sex). A cross-sectional questionnaire was deployed across five counties in the East Midlands, UK between 2017 and 2022 to adults with CMD (type 2 diabetes, cardiovascular disease or cerebrovascular disease). Respondent demographic data were collected alongside health education preferences. Statistical analyses ascertained whether demographic characteristics influenced preferences. The distribution of preferences over time was charted to identify trends. A total of 4301 eligible responses were collected. Face-to-face one-to-one education was preferred (first choice for 75.1% of participants) but popularity waned over the five-year period. Trends were similar amongst demographic groups. Online education showed a U-shaped trend: In 2017, 44% of respondents ranked it as acceptable, peaking at 53% in 2019, but declining again, to below base line, 43%, by 2022. This modality was more popular with participants aged younger than 65 years, but popularity in people older than 65 years increased over the study period. The popularity of printed information also declined over time across all demographic groups except those of South Asian ethnicity, for whom it remained static. The overwhelming preference for face-to-face one-to-one health education from a doctor or nurse highlights the importance of preserving access to this modality, even in the face of current NHS pressures and trends towards digitalisation. Trends are changing, and should continue to be monitored, including between different sociodemographic groups.

Integrating heterogeneous information for modeling non-stationarity of extreme precipitation in the Yangtze River Basin

Extreme precipitation exhibits non-stationary characteristics due to climate change and land use/cover change in recent years. In the literature, probability distributions with varying parameters associated with covariates have been applied to describe the non-stationarity of extreme precipitation. However, there is no consensus on the optimal distributions and the number and type of covariates for constructing non-stationary models. This study comprehensively investigates and determines the optimal distribution models, the type of covariates, and the number of covariates to best characterize the non-stationarity of extreme precipitation, using the Yangtze River Basin as the study area. The time series of four indices representing extreme precipitation of different durations and intensities are fitted by stationary and non-stationary Generalized Additive Models for Location, Scale, and Shape. The models are constructed using seven distributions and four types (time, global climate, local climate, and land cover) of covariates. Each model incorporates one to three covariates simultaneously. The results show that the most preferred distribution is the Generalized Extreme Value distribution for very wet-day precipitation, while the Log-Normal distribution is more suitable for other extreme precipitation indices. For the type of covariates, when using a single covariate, the proportion of stations using a global climate covariate as the best covariate (22.3 %–33.2 % of 193 stations) is close to that using a local climate covariate as the best covariate (22.3 %–27.5 %). The proportion of stations using land cover as the best covariate is much lower (7.3 %–10.9 %), while that using time as the best covariate is the lowest (1.6 %–4.1 %). When using multiple covariates, the models with both global and local climate covariates are mostly selected (8.3 % to 17.6 %) for all extreme precipitation indices. For the number of covariates, one or two covariates are enough to describe the non-stationary behaviors of all extreme precipitation indices. This study emphasizes the necessity to use non-stationary models for extreme precipitation frequency analysis with careful consideration and selection of distributions and covariates.

Mineral effects on methane hydrate formation and distribution in sand sediments

Methane hydrate is a promising energy resource widely occurring in the world, but the formation and distribution of methane hydrate in marine sediments with complex minerals remains unclear. Due to the small pore space and fine-grained size of clay minerals, the contact relationship between methane hydrate and clay mineral surface under excess water has not been characterized fully, which is required to investigate the mineral effect on methane hydrate formation. In this study, cryo-SEM was applied to observe the distribution of methane hydrate synthesized in pure quartz sands and the quartz sands mixed with montmorillonite or illite separately. According to the results of the research, methane hydrate dispersedly forms in the pore space away from and on the surface of quartz under a local excess water condition. In addition, methane hydrate occurs away from montmorillonite in pores with excess water and contacts the edge of montmorillonite under a local excess-gas condition. Moreover, methane hydrate was only observed to form away from illite aggregates in the pore with residual water. Montmorillonite and illite influence the preferable distribution of methane hydrate to occur away from mineral surface in excess water condition. In the sediments composed of different minerals, methane hydrate forms stochastically in the pore space and grows up individually. As a result, the findings of this study significantly expand the understanding of the formation and distribution of methane hydrate in marine sediments and the prediction of physical properties of hydrate-bearing sediments, and provide insights into natural gas hydrate exploration and production.

Distribution of Eu and Er in Ca9RE(PO4)7 phosphors analyzed by anomalous X-ray scattering

The structures of Ca9Eu(PO4)7 and Ca9Er(PO4)7 phosphors were analyzed using single-crystal X-ray diffraction. Most Eu ions reside at the medium-sized M1 and M2 sites, with a small amount occupying the M5 site. In contrast, the smaller Er ions favor the smallest M5 site. This study also demonstrates the use of anomalous X-ray scattering measurements at the Er LIII and Er LIII-edges. The energy dependencies observed in the scattering factors for Ca9Eu(PO4)7 and Ca9Er(PO4)7 confirmed the preferred distribution of Eu and Er. Because this anomalous X-ray scattering method leverages the unique anomalous dispersion terms of each element, this advanced technique, coupled with a synchrotron radiation source, is suggested to be a powerful method for distinguishing near-neighbor rare-earth elements in solid-state phosphors.

Living in a Diverse Community: Effects of Geographical Variations in Diversity on Partisan Communication

This study examines the effects of geographical variations in diversity on political communication within the context of partisan media and audiences. Partisan heterogeneity, as an indicator of political diversity, was conceptualized as a geographical context of the distribution of party preferences within a county. Using a set of nationwide panel survey data with county-level statistics, we conducted multilevel modeling to test its effects on partisan news use, political discussion, knowledge, and affective polarization. The results indicate that county-level partisan heterogeneity reduced the average levels of like-minded news consumption. Moreover, it attenuated the positive relationship between like-minded news use and political talk with strong ties. Additionally, we found that the context of partisan heterogeneity significantly moderated the mediation effects of partisan news use on political knowledge and affective polarization through political discussion. Implications of these results on partisan communication were discussed.

A Sociological Take on Language Generative AI Tools

Language represents the medium in which human experiences are uniquely encoded by how an individual processes and produces language. The development of generative artificial intelligence tools such as ChatGPT created a challenge in the context of education due to how humans, being humans, often employ tools in unconventional ways as hallmarks of our creativity and critical thinking. Efforts to resist the incursion of AI into academic work have evidently failed, as observable from how the Russell Group changed its stance from prior bans on AI to the current acceptance of AI to a large degree (4 July 2023). This acceptance created tremors for the longstanding traditions of knowledge acquisition and production, which many academics are paying increasing attention to. In a bid to better understand the impact of such acceptance, the study was carried out to investigate the impact of AI use for sociological work. Employing a pilot study of a pragmatic approach with 20 graduates, it was found that while there was an even distribution of preference between personal writing and AI writing, academic submission preferences scaled towards AI writing. Preference for AI writing for submission was noted to be twice that of personal writing, despite an even distribution of preference. Findings also noted the qualitative differences between personal writing (268 words with 84 unique words) and AI writing (250 with 10 unique words) in word range and unique words. The analysis notes significant differences in word range between personal writing (± 35.93) and AI writing (± 4.28), reflecting a convergence of writing rhetoric that proves to be largely detrimental to sociological developments. The discussion presents considerations in three dimensions: the challenge on education, the challenge on language education, and the challenge on the sociological lens.

Dynamic preference inference network: Improving sample efficiency for multi-objective reinforcement learning by preference estimation

Multi-objective reinforcement learning (MORL) addresses the challenge of optimizing policies in environments with multiple conflicting objectives. Traditional approaches often rely on scalar utility functions, which require predefined preference weights, limiting their adaptability and efficiency. To overcome this, we propose the Dynamic Preference Inference Network (DPIN), a novel method designed to enhance sample efficiency by dynamically estimating the trajectory decision preference of the agent. DPIN leverages a neural network to predict the most favorable preference distribution for each trajectory, enabling more effective policy updates and improving overall performance in complex MORL tasks. Extensive experiments in various benchmark environments demonstrate that DPIN significantly outperforms existing state-of-the-art methods, achieving higher scalarized returns and hypervolume. Our findings highlight DPIN’s ability to adapt to varying preferences, reduce sample complexity, and provide robust solutions in multi-objective settings.

The economic roots of cross‐national similarity in voter preferences

AbstractWe argue that economic and political integration leads voters' political preferences toward cross‐national convergence. Analyzing data on voter preferences across 30 European democracies from 1976 to 2022, we measure the similarity of preference distributions across state dyads over time, documenting an average increase in similarity over this period. We then model these associations statistically and find that greater similarity and complementarity in economic production and co‐participation in the European Union and the Eurozone are associated with increasingly similar voter preferences. The argument and analyses broaden our understanding of the political implications of globalization and also provide a theoretical and empirical foundation for two growing literatures: one on the cross‐national diffusion of parties' strategies and one on the political implications of macroeconomic stimuli such as trade shocks or banking crises.

Effects of visual diet on colour discrimination and preference.

To what extent is perception shaped by low-level statistical regularities of our visual environments and on what time scales? We characterized the chromatic 'visual diets' of people living in remote rainforest and urban environments, using calibrated head-mounted cameras worn by participants as they went about their daily lives. All environments had chromatic distributions with the most variance along a blue-yellow axis, but the extent of this bias differed across locations. If colour perception is calibrated to the visual environments in which participants are immersed, variation in the extent of the bias in scene statistics should have a corresponding impact on perceptual judgements. To test this, we measured colour discrimination and preferences for distributions of colour for people living in different environments. Group differences in the extent of blue-yellow bias in colour discrimination were consistent with perceptual learning in local environments. Preferences for colour distributions aligned with scene statistics, but not specifically to local environments, and one group preferred distributions along an unnatural colour axis orthogonal to that dominant in natural scenes. Our study shows the benefits of conducting psychophysics with people at remote locations for understanding the commonalities and diversity in human perception.

Analisis Tingkat Pengetahuan Keselamatan dan Kesehatan Kerja (K3) Mengenai Lockout/Tagout (LOTO) terhadap Tindakan Tidak Aman (Unsafe Action) dalam Lockout/Tagout (LOTO) Pekerja Divisi Manufactur dan Divisi Enggenering pada PT. X Kabupaten Banyuwangi

PT.X is a business located in Banyuwangi district, East Java. PT.X is engaged in manufacturing which produces mineral water. PT.X Banyuwangi's work procedures have implemented K3 in its work processes. The Lockout/Tagout (LOTO) locking and marking system has been implemented, but unsafe behavior is still found by workers when carrying out work processes using the Lockout/Tagout (LOTO) system, such as not using PPE during machine repair/maintenance processes, and not installing tags. . . during repairs. This research aims to analyze the level of K3 knowledge about Lockout/Tagout regarding unsafe actions in implementing Lockout/Tagout among workers at PT. In general, this research is a type of quantitative research. The aim of using the crosstab method is to identify data patterns, such as the distribution of variable preferences based on the level of knowledge in carrying out actions. The results of the analysis were that 3 respondents in the good knowledge category carried out unsafe actions in the high category representing 37.5% of the total respondents, 5 respondents in the good knowledge category carried out unsafe actions in the low category representing 62.5% of the total respondents. total respondents. Then for the poor knowledge level category, there were 13 respondents who carried out unsafe actions in the high category, representing 56.5% of the total respondents, and 10 respondents who had poor knowledge levels carried out unsafe actions in the low category. representing 43.5% of the total respondents.

Shared Decision-Making in Colorectal Cancer Screening for Older Adults

Decisions about whether to stop colorectal cancer (CRC) screening tests in older adults can be difficult and may benefit from shared decision-making (SDM). To evaluate the effect of physician training in SDM and electronic previsit reminders (intervention) vs reminders only (comparator) on receipt of the patient-preferred approach to CRC screening and on overall CRC screening rates of older adults at 12 months. This was a secondary analysis of the Promoting Informed Decisions About Colorectal Cancer Screening in Older Adults (PRIMED) cluster randomized clinical trial. In the PRIMED trial, primary care physicians (PCPs) from 36 primary care practices in Massachusetts and Maine were enrolled between May 1 and August 30, 2019, and were randomized to the intervention group or the comparator group. Patients aged 76 to 85 years who were overdue for CRC screening and did not have a prior diagnosis of CRC enrolled between October 21, 2019, and April 8, 2021. Data analysis was performed between May 24, 2022, and May 10, 2023. Primary care physicians in the intervention group completed an SDM training course and received previsit reminders of patients eligible for CRC testing discussion, whereas PCPs in the comparator group received reminders only. The primary outcome was concordance, or the percentage of patients who received their preferred screening approach. Postvisit surveys were administered to assess patient preference for testing, and electronic health record review was used to assess CRC testing at 12 months. Heterogeneity of treatment effect analyses examined interaction between study groups and different factors on concordance rates. This study included 59 physicians and 466 older adults. Physicians had a mean (SD) age of 52.7 (9.4) years and a mean (SD) of 21.6 (10.2) years in practice; 30 (50.8%) were women and 16 (27.1%) reported prior training in SDM. Patients had a mean (SD) age of 80.3 (2.8) years; 249 (53.4%) were women and 238 (51.1%) reported excellent or very good overall health. Patients preferred stool-based tests (161 [34.5%]), followed by colonoscopy (116 [24.8%]) or no further screening (97 [20.8%]); 75 (16.1%) were not sure. The distribution of patient preferences was similar across groups (P = .36). At 12 months, test uptake was also similar for both the intervention group (29 [12.3%] for colonoscopy, 62 [26.3%] for stool-based tests, and 145 [61.4%] for no testing) and the comparator group (32 [13.9%] for colonoscopy, 35 [15.2%] for stool-based tests, and 163 [70.9%] for no testing; P = .08). Approximately half of patients in the intervention group received their preferred approach vs the comparator group (115 of 226 [50.9%] vs 103 of 223 [46.2%]; P = .47). Heterogeneity of treatment effect analyses found significantly higher rates with the intervention vs the comparator for patients with a strong intention to follow through with the preferred approach (adjusted odds ratio [AOR], 1.79 [95% CI, 1.11-2.89]; P = .02, P = .05 for interaction) and for patients who reported more than 5 minutes (AOR, 3.27 [95% CI, 1.25-8.59]; P = .02, P = .05 for interaction) of discussion with their PCP regarding screening. Higher rates were also observed among patients who reported 2 to 5 minutes of discussion with their PCP, although this finding was not significant (AOR, 1.89 [95% CI, 0.93-3.84]; P = .08, P = .05 for interaction). In this secondary analysis of a cluster randomized clinical trial, approximately half of older patients received their preferred approach to CRC screening. Physician training in SDM did not result in higher concordance rates overall but may have benefitted some subgroups. Future work to refine and evaluate clinical decision support (in the form of an electronic advisory or reminder) as well as focused SDM skills training for PCPs may promote high-quality, preference-concordant decisions about CRC testing for older adults. ClinicalTrials.gov Identifier: NCT03959696.

Are Contraceptive Method Preferences Stable? Measuring Change in the Preferred Method among Kenyan Women.

Contraceptive preferences are important for reproductive outcomes, such as contraceptive continuation and pregnancy. Current approaches to measuring reproductive preferences in population surveys are limited to exploring only fertility preferences and implicitly assume that contracepting people are using a method they want. We know that people change their fertility preferences over the life course as a response to life events, but there is no information about changes in contraceptive preferences, given the limited evidence about the measurement and distribution of contraceptive preferences. In this study, we examined the extent of change in women's contraceptive preferences over one year and identified characteristics associated with this change in Kenya using three rounds of nationally representative longitudinal data. Over one year, 18 percent of contraceptive users and 46 percent of contraceptive nonusers reported changes in their preferred contraceptive. Experiencing a pregnancy or birth and changes in marital status were associated with changes in contraceptive preferences for users and nonusers. We found that contraceptive preferences are dynamic, suggesting that family planning programs should ensure people's access to various methods to respond to women's changing circumstances and preferences.

Research on decomposition-based multi-objective evolutionary algorithm with dynamic weight vector

In recent years, multi-objective evolutionary algorithm based on decomposition has gradually attracted people's interest. However, this algorithm has some problems. For example, the diversity of the algorithm is poor, and the convergence and diversity of the algorithm are unbalanced. In addition, users don't always care about the entire Pareto front. Sometimes they may only be interested in specific areas of entire Pareto front. Based on the above problems, this paper proposes a decomposition-based multi-objective evolutionary algorithm with dynamic weight vector (MOEA/D-DWV). Firstly, a weight vector generation model with uniform distribution or preference distribution is proposed. Users can decide which type of weight vector to generate according to their own wishes. Then, two combination evolution operators are proposed to better balance the convergence and diversity of the algorithm. Finally, a dynamic adjustment strategy of weight vector is proposed. This strategy can adjust the distribution of weight vector adaptively according to the distribution of solutions in the objective space, so that the population can be uniformly distributed in the objective space as much as possible. MOEA/D-DWV algorithm is compared with 9 advanced multi-objective evolutionary algorithms. The comparison results show that MOEA/D-DWV algorithm is more competitive. Data availabilityData will be made available on request.

Oh, the Places You'll Go? Exploring the Geographic Program Distribution and Use of Geographic Preferences in the Current Radiation Oncology Residency Application Cycle

Oh, the Places You'll Go? Exploring the Geographic Program Distribution and Use of Geographic Preferences in the Current Radiation Oncology Residency Application Cycle

Consumers’ perception of distribution channel for the Malaysian microtakaful scheme: an exploratory study

Purpose This paper aims to investigate consumers’ preferences regarding the distribution channels for subscription, contribution payment and compensation claims of microtakaful scheme in Malaysia. Design/methodology/approach Consumers’ preferences were explored through questionnaires and focus group discussions (FGD) conducted among the bottom 40% income classification households (B40) in five zones: northern, central, eastern, southern and Sabah and Sarawak. Findings Empirical findings from cross-tabulation analysis revealed that takaful company is the preferred distribution channel for purchasing protection plans and making compensation claims. However, the online platform is the favoured channel to make contribution payments. Further investigation through FGD suggested that the selection of a channel for subscription, contribution payment and compensation claim is influenced by consumer trust, cost-effectiveness and simplicity of procedure. Research limitations/implications Limitation is pertaining to only cross-tabulation analysis used in explaining the choice of distribution channel for microtakaful among B40 group. Thus, advanced analysis is required to strengthen the findings. Practical implications Findings of this study would help marketers and practitioners to formulate strategies to promote their microtakaful protection to enhance subscription among the low-income population. Originality/value Empirical findings offer academic contributions to the existing body of knowledge on microtakaful area as the primary data collected will eventually allow future researchers to explicate the contribution of the current study to understand the important of distribution channel for microtakaful from the perspective of subscribers and potential subscribers.

A Comparison of Admissions Data Between Virtual and In-person Dental School Interviews

Objectives: The COVID-19 pandemic forced many dental schools to conduct some or all of their interviews virtually. The purpose of the study was to evaluate whether an association existed between interview setting and applicant characteristics, specifically focusing on the difference in the interview score between applicants who chose virtual interviews and those who chose in-person interviews.Methods: Demographic and admission data were obtained from the 101 interviewed candidates from the XXXX 2021-2022 application cycle. Distribution of demographics and interview preference were assessed. Simple and multiple generalized linear models (GLM) were run to examine the difference in the applicants’ interview score between in-person and virtual interviews.Results: Of the 29 in-person interviews, 16 (55.17%) were offered acceptances, while 31 (43.06%) of the 72 virtual interviews were accepted, a difference that was not statistically significant (p-value = 0.380). Preference for the interview format was not associated with any of the applicants’ factors including gender, current residence (the four bureau-designated regions), overall grade point average (GPA), science GPA, Dental Admissions Test (DAT) Academic Average score, and perceptual ability test (PAT) score. In addition, there was no statistical difference in the interview score between the two interview formats.Conclusions: The effect of virtual interviewing on interview scores and applicants’ admission rate was found to be statistically insignificant. If further study with larger sample sizes confirms this finding, virtual interviewing could be considered as a viable alternative to in-person interviewing in the dental school admissions process.

Designing information to improve welfare in matching markets

In matching markets, objects are allocated to agents without monetary transfers, based on agents’ preferences. However, agents may not have enough information to determine their preferences over the objects precisely. How should a benevolent planner optimally reveal information to maximize social welfare in this context? I show that when agents are symmetric and there are just two options, letting each agent know his rank in the realized distribution of preferences – but not his actual preferences – always improves social welfare over providing no information. When there are more objects, this rank-based information policy generalizes to the Object Recommendation (OR) Signal, which consists of simply recommending each agent to pick his socially-optimal choice. Under a mild regularity condition, I show that, when agents’ a priori relative preferences over the objects are “not too strong”, the OR Signal, used together with any standard ordinal mechanism, not only maximizes welfare, but achieves the unconstrained social optimum — formalizing the intuition that when people do not have strong opinions over several options, it is easy to sway them.

Global Perspectives on Fiscal Policy and Labor Income-Leisure Choices: Theoretical and Practical Insights

In macroeconomic literature, fiscal policy is considered a powerful tool to achieve sustainable development, full employment, and social well-being in developed and developing societies. For this, public authority uses expansionary and contractionary tax policies to achieve stable growth and employment environment for the stable labor market. This study theoretically and empirically investigates the problem of inference on income-leisure labor preference. Also, it considers the impact of tax and expenditure structure on labor choices regarding working hours under the assumption of neoclassical theory. We use quantile regression analysis to investigate the data set for worldwide, high, and low-income countries from 2000 to 2022, for the macro-level analysis based on the empirical investigation of 123 countries cross-section panel. The outcomes show that when the fiscal authorities impose a regressive form of taxes, it may hurt the labor wages and distribution of income-leisure preferences or the welfare of labor. Similarly, non-labor income hurts labor utility through a large volume of non-development expenditure. However, when the progressive form of taxes is imposed, it may improve the labor utility. At the same time, on the other side, when fiscal authority disburses the development expenditure, it may support the non-labor income through the provision of public goods and services. For practice, the empirical results of quantile regression show that government expenditure has a positive while tax hurts labor supply. Fiscal policy in low-income countries has provided an alternative outcome

Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a cross-section of repeated choices. We assume that either the latent choice sets are sparse or that the number of repeated choices is sufficiently large. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer programming to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to incorrect estimates of preferences.