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Background: Lolland-Falster Health Study (LOFUS) was a health examination survey that included self-administered questionnaires, clinical examinations, and the collection of biological samples, undertaken in 2016–2020 in a rural, socioeconomically deprived area with the lowest life expectancy in Denmark. The aim of this study was to examine the determinants of non-participation in LOFUS to evaluate the extent to which LOFUS data reflected the general population of the area. Methods: LOFUS invited randomly selected subjects together with their entire household. As determinants of non-participation, we analyzed age, sex, municipality of residency, citizenship, residency status, socioeconomic status, invitation type, and year of invitation. Relative risk regression was used to estimate the association between determinants and non-participation rate, mutually adjusted for other determinants. Results: In total, 53,313 subjects were invited of whom 18,949 (36%) participated. In the multivariable analysis, men had a 3% higher non-participation rate than women; subjects with citizenship other than Danish had a 3% higher non-participation rate than Danes. In-migrants had 6% higher non-participation than long-term residents. Compared with self-supported subjects aged 30–64, both publicly supported subjects of this age and younger and older subjects had higher non-participation rates: 16%, 16%, and 13%, respectively. Compared with self-supported, long-term residents, publicly supported in-migrants had 23% higher non-participation. Conclusions: Only about one third of subjects invited to LOFUS participated. Yet, this is a relatively high participation rate compared with other recent health examination surveys in Denmark. Furthermore, there was a relatively flat social gradient in the non-participation rate across the studied determinants. [ABSTRACT FROM AUTHOR]

The significant simplification achieved by Main Path Analysis (MPA) to a network of documents raises concerns about whether the derived main paths (MPs) accurately capture and reflect the overall knowledge development within the network. This study addresses this MP representativeness issue from a network-structural perspective, considering that MPs can only represent the parts of the network that are structure-related to them, and these parts of the network are referred to as constituting the MPs' coverage. The share of documents falling within the MPs' coverage can serve as a quantitative measure to complement the qualitative assessment of MP representativeness. This study introduces a so-called key-node path search to uncover the coverage of MPs. In cases where a significant portion of the network falls outside the coverage of MPs, this study proposes a method to discover auxiliary MPs from the out-of-coverage parts of the network. These auxiliary MPs provide additional insights into the representativeness of the primary MPs, the structural characteristics of the network, and additional knowledge development trajectories. To demonstrate the practical application of these concepts and methodologies, a case study using U.S. utility patents related to chimeric antigen receptor (CAR) T-cell therapy is conducted. [ABSTRACT FROM AUTHOR]

Environmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products. [ABSTRACT FROM AUTHOR]

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed–based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream–downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures. [ABSTRACT FROM AUTHOR]

Diagnostic expectations constitute a realistic behavioural model of inference. This paper shows that this approach to expectation formation can be productively integrated into the New Keynesian framework. Diagnostic expectations generate endogenous extrapolation in general equilibrium. We show that diagnostic expectations generate extra amplification in the presence of nominal frictions; a fall in aggregate supply generates a Keynesian recession; fiscal policy is more effective at stimulating the economy. We perform Bayesian estimation of a rich medium-scale model that incorporates consensus forecast data. Our estimate of the diagnosticity parameter is in line with previous studies. Moreover, we find empirical evidence in favour of the diagnostic model. Diagnostic expectations offer new propagation mechanisms to explain fluctuations. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This study aimed to investigate the reach of a large-scale health assessment delivered by the occupational health service in Sweden for almost 30 years. METHODS: A total of 418 286 individuals who participated in a health assessment (Health Profile Assessment, HPA) between 1995–2021 were included. A comparative sample was obtained from Statistics Sweden, comprising the entire working population for each year (4 962 127–6 011 829 unique individuals per time period). Sociodemographic and work organization characteristics were compared between the HPA and comparative population for six different periods. Under- and overrepresented groups in the private and public sectors were identified using the most recent data (2015–2021). RESULTS: With negative per cent indicating underrepresentation, the most notable changes over time in representation in the HPA population compared to the comparative were observed for women (-1.2% to -12.8%), private sector employees (-9.4% to 14.9%), individuals with ≥3 years of employment (14.5% to 0.9%), in personal care (0.8% to -8.8%) and manufacturing (0.7% to 6.4%) occupations. Consistently overrepresented groups (median representation across periods) included individuals who had a single income source (6.3%) and were middle-aged (10.8%), born in Sweden (5.9%), associate professionals (8.7%), and employed in companies with high operating profit (17.9%) and low staff turnover (14.3%). Conversely, individuals with low income (-34.0%) and employed in small companies/organizations (-10.9%) were consistently underrepresented. Middle-aged women in education occupations were most underrepresented in the public sector, while in the private sector, it was young women in service and shop sales occupations. CONCLUSIONS: This health assessment has reached many professionals, including hard-to-reach groups, but did not fully represents the Swedish workforce throughout the years.

For decades, the exercise psychology research literature echoed the conclusion that exercise makes most people feel better, with no clear evidence that this "feel-better effect" is moderated by intensity. An overhaul of the methodological approach subsequently showed that high-intensity exercise is experienced as unpleasant, and the "feel-better effect," although possible, is conditional and therefore not as robust or prevalent as initially thought. Recently, several studies investigating high-intensity interval training (HIIT) have concluded that HIIT is pleasant and enjoyable, despite the high intensity. Considering that HIIT is emerging as an option in physical activity recommendations and exercise prescription guidelines, in part due to these claims, a methodological checklist is presented to aid researchers, peer reviewers, editors, and other readers in critically appraising studies examining the effects of HIIT on affect and enjoyment. This first part addresses the characteristics and number of participants, as well as the selection of measures of affect and enjoyment. [ABSTRACT FROM AUTHOR]

The information paradigms defining contemporary society call for the character of plurivalence through their omnipresence in multiple areas of activity. Their ubiquitous character provides the basis of the accounting-computer science interdependence, the undeniable reality being marked by the computer indispensability in the professional accountant’s life. In this way, specialized literature becomes allencompassing by the approaches marked by complexity and density regarding the issues of modernizing the accounting by its massive computerization. The cloud accounting phenomenon is a component part of the innovative changes that accounting has undergone lately. The research paper aims to outline the bibliometric universe of the cloud accounting concept based on the methods, tools and techniques subscribed to bibliometrics in view of reflecting its representativeness through the lens of specialized literature. The results thus obtained consolidate the emblematic character of the cloud accounting issue in light of the massive interest manifested for its thoroughness and exploration equally by researchers, theoreticians, and practitioners. The study also stands out for its originality, being currently the only one dealing with issues of cloud accounting concept bibliometrics. The attribute of uniqueness of this paper is defined by shaping the research universe assigned to the phenomenon based on specialized literature, identifying the manner to reshape the profession and the field by changing the digital paradigm and illustrating the representativeness of technology in the academic community through the perspective of the bibliometrics tool. Likewise, an element of novelty is given by highlighting the definition of cloud accounting from the author’s perspective based on the image provided by the literature review.

Background: The representativeness of cohort studies compared to nationwide data is a major concern. This study evaluated the similarity and seasonality of causative respiratory viruses for chronic obstructive pulmonary disease (COPD) and asthma exacerbations between retrospective multicenter cohort study and nationwide data. Methods: We compared data from the retrospective multicenter cohort study with Korean Influenza and Respiratory Surveillance System data between 2015 and 2018. Correlation, dynamic time warping (DTW), and seasonal autoregressive integrated moving average (SARIMA) analyses were performed. Results: Spearman correlation coefficients [ρ] indicated very strong (respiratory syncytial virus [RSV] [ρ = 0.8458] and influenza virus [IFV] [ρ = 0.8272]), strong (human metapneumovirus [HMPV] [ρ = 0.7177] and parainfluenza virus [PIV] [ρ = 0.6742]), and moderate (rhinovirus [RV] [ρ = 0.5850] and human coronavirus [HCoV] [ρ = 0.5158]) correlations. DTW analyses showed moderate (PIV) and high (IFV, RSV, and HMPV) synchronicity between the two datasets, while RV and HCoV showed low synchronicity. SARIMA analyses revealed 12-month seasonality for IFV, RSV, PIV, and HMPV. The peak season was winter for RSV and IFV, spring to summer for PIV, and spring for HMPV. Conclusions: This was the first study to report the synchronicity between a retrospective multicenter cohort study of viruses that can cause COPD or asthma exacerbations and nationwide surveillance system data. [ABSTRACT FROM AUTHOR]

Melothria pendula L., a wild relative of cucurbit crops, is also used for food and as a medicinal plant in Mexico. The objective of this study was to ecogeographically characterize the known populations of M. pendula in Mexico, determining its adaptive range and possible sites for in situ and ex situ conservation. To achieve this goal, we compiled a dataset of 1270 occurrences of M. pendula from herbarium and botanical databases and individual observations. Adaptive scenarios were generated through the development of an ecogeographic land characterization (ELC) map, preceded by the identification of abiotic variables influencing the species' distribution. Eleven bioclimatic, edaphic, and geophysical variables were found to be important for the species' distribution. The ELC map obtained contained 21 ecogeographic categories, with 14 exhibiting the presence of M. pendula. By analyzing ecogeographic representativeness, 111 sites of high interest were selected for the efficient collection of M. pendula in Mexico. Eight high-priority hotspots for future in situ conservation of M. pendula were also identified based on their high ecogeographic diversity, with only three of these hotspots located within protected natural areas. In this study, ecogeographic approaches show their potential utility in conservation prioritization when genetic data are scarce, a very common condition in crop wild relatives. [ABSTRACT FROM AUTHOR]

This article analyzes the evolution of governance models within public administrations as they respond to complex socio-political challenges. It emphasizes the need to enhance the legitimacy and representativeness of decision-making processes in the face of persistent issues, social fragmentation, increasing inequalities, and political polarization. The study discusses two primary models: citizen participation, which promotes a more engaged form of democracy, and representative bureaucracy, which seeks to ensure that public administration reflects socio-demographic diversity. However, these frameworks have flaws, particularly in achieving representativeness and maintaining administrative efficiency. To address these issues, the concept of "Externally Active Representative Bureaucracy" (EARB) is proposed, which involves incorporating citizens directly into administrative structures to address specific challenges while improving the legitimacy and representativeness of decisions. The article reviews 155 academic articles to explore the various dimensions and effects of representative bureaucracy and citizen participation. The objective is to illustrate that EARB provides an innovative approach to public administration that bridges bureaucratic efficiency with citizen inclusion, inviting further research into this hybrid model to enhance our understanding of the operation of modern public administrations. [ABSTRACT FROM AUTHOR]

Anticipation is key to performance in many sports. By definition, anticipation as a perceptual-cognitive process is meant to inform action and help athletes reduce potential motor costs under spatiotemporal pressure. Anticipation research has repeatedly been criticized for neglecting action and raised the need for predominant testing under conditions of perception-action coupling (PAC). To the best of our knowledge, however, there is a lack of explicit criteria to characterize and define PAC conditions. This can lead to blurred terminology and may complicate interpretation and comparability of PAC conditions and results across studies. Here, we make a first proposal for a 7-level classification of PAC conditions with the defining dimensions of stimulus presentation and response mode. We hope this classification may constitute a helpful orientation for study planning and reporting in research on anticipation. Further, we illustrate the potential utilization of the PAC classification as a template for experimental protocol analysis in a review on anticipation in racket sports. Analysis of N = 115 studies reported in N = 91 articles confirms an underrepresentation of representative PAC conditions and reveals little change in PAC approaches over more than 40years of research in that domain. We discuss potential reasons for these findings, the benefits of adopting the proposed PAC classification and reiterate the call for more action in anticipation research. [ABSTRACT FROM AUTHOR]

A four-step dividing algorithm of sampling points for artificial neural networks is presented to select a representative training subset for modelling the spatial distribution. The chromium and manganese contents in the topsoil in Tarko-Sale and Noyabrsk cities (Russian subarctic zone) were used as raw data. The spatial distributions of the content of elements in the topsoil layer were performed using a multilayer perceptron (MLP) with sigmoid and hyperbolic tangential activation functions. The root means squared error (RMSE) was calculated for each element content and area. The MLP with hyperbolic tangential activation function showed better accuracy for both subarctic cities and model areas. For Noyabrsk, the model with a hyperbolic tangent was about 10% better. For Tarko-Sale, the improvement of RMSE was around 200%. We have identified three classes of points: «elite», «middle», and «useless». Given this information when dividing the raw set, the accuracy of the models will increase. [ABSTRACT FROM AUTHOR]

Adaptive survey design refers to using targeted procedures to recruit different sampled cases. This technique strives to reduce bias and variance of survey estimates by trying to recruit a larger and more balanced set of respondents. However, it is not well understood how adaptive design can improve data and survey estimates beyond the well-established post-survey adjustment. This paper reports the results of an experiment that evaluated the additional effect of adaptive design to post-survey adjustments. The experiment was conducted in the Detroit Metro Area Communities Study in 2021. We evaluated the adaptive design in five outcomes: 1) response rates, 2) demographic composition of respondents, 3) bias and variance of key survey estimates, 4) changes in significant results of regression models, and 5) costs. The most significant benefit of the adaptive design was its ability to generate more efficient survey estimates with smaller variances and smaller design effects. [ABSTRACT FROM AUTHOR]

Background: Distinctive differences in multiple sclerosis (MS) have been observed by race and ethnicity. We aim to (1) assess how often race and ethnicity were reported in clinical trials registered on ClinicalTrials.gov, (2) evaluate whether the population was diverse enough, and (3) compare with publications. Methods: We included phase 3 clinical trials registered with results on ClinicalTrials.gov between 2007 and 2023. When race and/or ethnicity were reported, we searched for the corresponding publications. Results: Out of the 99 included studies, 56% reported race and/or ethnicity, of which only 26% of those primarily completed before 2017. Studies reporting race or ethnicity contributed to a total of 33,891 participants, mainly enrolled in Eastern Europe. Most were White (93%), and the median percentage of White participants was 93% (interquartile range (IQR) = 86%–98%), compared to 3% for Black (IQR = 1%–12%) and 0.2% for Asian (IQR = 0%–1%). Four trials omitted race and ethnicity in publications and even when information was reported, some discrepancies in terminology were identified and categories with fewer participants were often collapsed. Conclusion: More efforts should be done to improve transparency, accuracy, and representativeness, in publications and at a design phase, by addressing social determinants of health that historically limit the enrollment of underrepresented population. [ABSTRACT FROM AUTHOR]

AbstractThis article reviews the deterioration of government stability in Israel since the late 1990s and in the last three years in particular and examines the causes and consequences of this reality. The findings indicate several factors that have been contributing to government instability in Israel beginning from the late 1990s. One is the considerable heterogeneity typical of Israeli society, which encourages the establishment of many sectoral parties based on faith, country of origin, or some common interest. The second is related to cultural changes that derive from embracing a utilitarian worldview which has begun to spread throughout Israeli society and to leave its mark on the citizens’ voting patterns, and the third is related to the many possible options for dissolving the Knesset. These factors, together and separately, have led to an inability to form a stable government that has sufficient electoral power to lead long-term policy processes. Instead, government stability is eroding increasingly, politicians are developing a reasoning that is based on narrow interests and neglecting the values and ideologies for which they were chosen. All this in order to preserve their political survival, which has become their top goal, instead of promoting wide public interests.

In 2021, controversy erupted in the literary world over the choice of translation for the Dutch edition of « The Hill We Climb », a poem that became famous after Amanda Gorman recited it at Joe Biden's inauguration. In the Netherlands, publisher J.M. Meulenhoff chose Marieke Lucas Rijneveld, winner of the previous year's Man Booker International Prize, for the job. Following criticism from journalist Janice Deul, Rijneveld withdrew from the project, sparking a worldwide debate. This article uses that controversy to reflect on the relationship between literary production and translation, the violence of translation practice, and related issues of visibility and representativeness. To this end, the article draws on the reflections of Tiphaine Samoyault, Lawrence Venuti, Paulo Henriques Britto, and Walter Benjamin.

OBJECTIVE: Pragmatic trials often implement an intervention across a population of patients but require information unavailable at the population level that must be reported by a subset of patients. In this pragmatic clinical trial, we compared characteristics of seriously ill patients with those who completed a survey evaluating advance care planning across 3 academic health systems. METHODS: A deliberate process including health system and external stakeholders and patients was used to design materials for and the approach to seriously ill patients. We developed a survey and conducted a multistep process to identify seriously ill primary care patients. We evaluated the relationships of age, gender, race and ethnicity, and vulnerability using the social vulnerability index in this population, and explored the representativeness of survey respondents compared with the underlying seriously ill population in terms of age, race and ethnicity, and vulnerability measured. RESULTS: About 5% (8707 patients) of the primary care population was classified as seriously ill, 5351 were mailed a survey and 1100 provided survey responses. Hispanic and Black patients were younger than White patients, and Black and Hispanic patients were more vulnerable than White and Asian patients and patients of other races. Representativeness was high across age and race and ethnicity, although White and Hispanic patients were more likely to respond than Black and Asian patients and patients of other races. Vulnerability in the surveyed sample was nearly identical to the population. CONCLUSIONS: A tailored survey and recruitment strategy yielded a representative sample of seriously ill, largely older, primary care respondents in the context of a pragmatic clinical trial.

Abstract Background Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. Methods Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall ‘envisioned population’ survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. Results Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. Conclusions Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

Background: Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP. Methods: Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall 'envisioned population' survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census. Results: Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings. Conclusions: Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings. [ABSTRACT FROM AUTHOR]

Institutional transformations in recent decades have limited the weight and power of collective actors. Notwithstanding this, literature on employer associations (EAs) has argued a resilient performance in terms of affiliation. In this paper, we use comparative institutional analysis to evaluate the actual scope of these associations in terms of membership and the share of workforce employed by their members. Moreover, by utilising a logistic regression approach we delve into the drivers of the choice to join these organisations. Our results show a general decline of EAs, and point to large manufacturing companies as the great reservoirs of affiliation. However, institutional variations across Europe cause significant differences between countries. We thus demonstrate how membership is strongly influenced by the institutional framework, the relevance of the variables related to the provision of collective goods as a counterbalance to trade union activity and adherence to collective bargaining schemes. [ABSTRACT FROM AUTHOR]

The U.S. Centers for Disease Control and Prevention (CDC) received surveillance data on how many people tested positive for SARS-CoV-2, but there was little information about what individuals did to mitigate transmission. To fill the information gap, we conducted an online, probability-based survey among a nationally representative panel of adults living in the United States to better understand the behaviors of individuals following a positive SARS-CoV-2 test result. Given the low response rates commonly associated with panel surveys, we assessed how well the survey data aligned with CDC surveillance data from March, 2020 to March, 2022. We used CDC surveillance data to calculate monthly aggregated COVID-19 case counts and compared these to monthly COVID-19 case counts captured by our survey during the same period. We found high correlation between our overall survey data estimates and monthly case counts reported to the CDC during the analytic period (r: +0.94; p<0.05). When stratified according to demographic characteristics, correlations remained high. These correlations strengthened our confidence that the panel survey participants were reflective of the cases reported to CDC and demonstrated the potential value of panel surveys to inform decision making. [ABSTRACT FROM AUTHOR]

Real‐world (rw‐) statistical regularities, or expectations about the visual world learned over a lifetime, have been found to be associated with scene perception efficiency. For example, good (i.e., highly representative) exemplars of basic scene categories, one example of an rw‐statistical regularity, are detected more readily than bad exemplars of the category. Similarly, good exemplars achieve higher multivariate pattern analysis (MVPA) classification accuracy than bad exemplars in scene‐responsive regions of interest, particularly in the parahippocampal place area (PPA). However, it is unclear whether the good exemplar advantages observed depend on or are even confounded by selective attention. Here, we ask whether the observed neural advantage of the good scene exemplars requires full attention. We used a dual‐task paradigm to manipulate attention and exemplar representativeness while recording neural responses with functional magnetic resonance imaging (fMRI). Both univariate analysis and MVPA were adopted to examine the effect of representativeness. In the attend‐to‐scenes condition, our results replicated an earlier study showing that good exemplars evoke less activity but a clearer category representation than bad exemplars. Importantly, similar advantages of the good exemplars were also observed when participants were distracted by a serial visual search task demanding a high attention load. In addition, cross‐decoding between attended and distracted representations revealed that attention resulted in a quantitative (increased activation) rather than qualitative (altered activity patterns) improvement of the category representation, particularly for good exemplars. We, therefore, conclude that the effect of category representativeness on neural representations does not require full attention. [ABSTRACT FROM AUTHOR]

The UK Biobank study contains several sources of diagnostic data, including hospital inpatient data and data on self-reported conditions for approximately 500,000 participants and primary-care data for approximately 177,000 participants (35%). Epidemiologic investigations require a primary disease definition, but whether to combine data sources to maximize statistical power or focus on only 1 source to ensure a consistent outcome is not clear. The consistency of disease definitions was investigated for venous thromboembolism (VTE) by evaluating overlap when defining cases from 3 sources: hospital inpatient data, primary-care reports, and self-reported questionnaires. VTE cases showed little overlap between data sources, with only 6% of reported events for persons with primary-care data being identified by all 3 sources (hospital, primary-care, and self-reports), while 71% appeared in only 1 source. Deep vein thrombosis–only events represented 68% of self-reported VTE cases and 36% of hospital-reported VTE cases, while pulmonary embolism–only events represented 20% of self-reported VTE cases and 50% of hospital-reported VTE cases. Additionally, different distributions of sociodemographic characteristics were observed; for example, patients in 46% of hospital-reported VTE cases were female, compared with 58% of self-reported VTE cases. These results illustrate how seemingly neutral decisions taken to improve data quality can affect the representativeness of a data set. [ABSTRACT FROM AUTHOR]

The study aims to examine the impact of various behavioral biases such are overconfidence, representativeness, availability, anchoring and herding, and their effects on individual investment decisions in the case-developed country context of China. To achieve this, data was meticulously gathered from 362 participants active in the Shanghai stock market. Employing advanced analytical tools, particularly the Smart PLS 3.3.2 software and structural equation modelling (SEM), this study rigorously scrutinized the intricate relationships between behavioral biases and investment decisions. The findings of this study notably reveal that all examined behavioral biases exert a significant positive impact on investment decisions within the Anxin, Haitong, Shanxi, and China Galaxy stock markets. Remarkably, no substantial disparities in the effects of these biases on stock market trading were observed among these markets. Importantly, these findings bear exceptional significance within the context of a developed country like China. The implications extend to a wide spectrum of stakeholders, including government entities, regulatory bodies, practitioners, the academic community, industry professionals, and researchers. Regulatory authorities can leverage these insights to refine their strategies, practitioners can fine-tune their investment advisory approaches, and academia and researchers can build upon these findings to deepen the understanding of behavioral finance in the realm of stock market investments. [ABSTRACT FROM AUTHOR]

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Anticipation is key to performance in many sports. By definition, anticipation as a perceptual-cognitive process is meant to inform action and help athletes reduce potential motor costs under spatiotemporal pressure. Anticipation research has repeatedly been criticized for neglecting action and raised the need for predominant testing under conditions of perception-action coupling (PAC). To the best of our knowledge, however, there is a lack of explicit criteria to characterize and define PAC conditions. This can lead to blurred terminology and may complicate interpretation and comparability of PAC conditions and results across studies. Here, we make a first proposal for a 7-level classification of PAC conditions with the defining dimensions of stimulus presentation and response mode. We hope this classification may constitute a helpful orientation for study planning and reporting in research on anticipation. Further, we illustrate the potential utilization of the PAC classification as a template for experimental protocol analysis in a review on anticipation in racket sports. Analysis of N = 115 studies reported in N = 91 articles confirms an underrepresentation of representative PAC conditions and reveals little change in PAC approaches over more than 40 years of research in that domain. We discuss potential reasons for these findings, the benefits of adopting the proposed PAC classification and reiterate the call for more action in anticipation research.

ObjectiveIn this study, we examined the impact of a range of methods to improve data quality on the demographic and health status representativeness of Amazon Mechanical Turk (MTurk) samples.MethodsWe developed and field-tested a general survey of health on MTurk in 2017 among 5755 participants and 2021 among 6752 participants. We collected information on participant demographic characteristics and health status and implemented different quality checks in 2017 and 2021.ResultsAdding data quality checks generally improves the representativeness of the final MTurk sample, but there are persistent differences in mental health and pain conditions, age, education, and income between the MTurk population and the broader US population.ConclusionWe conclude that data quality checks improve the data quality and representativeness.

Dense soil moisture observation networks serve as the primary means to validate large-scale satellite soil moisture products. However, maintaining intensive observation demands substantial labor and financial resources. It is therefore crucial to address the issue of how to sparsify ground observations while still achieving comparable validation results. Accordingly, this study takes an agropastoral interconnected zone of the Shandian River basin as an example. Representative soil moisture sites and the minimal number of required sites (NRS) were investigated at different sampling depths (3 and 50 cm) and at multiple scales (3, 9, 36, and 100 km). The average soil moisture estimated from the representative sites was then applied to validate SMAP L2, L3, and L4 multiscale soil moisture products. The findings indicate that the spatial heterogeneity of soil moisture within the Shandian River basin remained relatively consistent across varying spatial scales. However, it exhibited a notable increase in heterogeneity when moving vertically into deeper soil layers. Representative sites can more accurately determine the average soil moisture in a region, showing that SMAP notably overestimated root zone soil moisture and underestimated surface soil moisture. This research can serve as a theoretical guide for watershed-scale soil moisture estimation as well as a solid scientific foundation for improving the architecture of the watershed soil moisture network.

The study of electoral disputes and the transformation of power structures has been notably lacking in the historiography of republican universities during the first half of the 19th century. This article delves into the experiences of faculty members elected through the vote, which played a significant role in reshaping leadership and expanding intellectual culture at Universidad del Magdalena e Istmo between 1827 and 1840. During this period, somewhat uniquely, enlightened republicans established universities at the departmental (provincial) level in what is now the Republic of Colombia. However, there has been limited examination of the trajectories and selection processes of the educated elite from the standpoint of intellectual history. This article argues that the use of the electoral mechanism, in addition to determining representativeness and the rise of representative government in universities, was a pivotal factor in the affirmation of an education and a secular power. This, on one hand, eroded the clerical influence in higher education and, on the other, displaced ecclesiastics from positions of authority. The advent of the university within the framework of representative democracy also led to the emergence of other types of intellectuals, linked to humanistic knowledge, medicine, and even military education in the navy. Methodologically, the goal is to explore the political journeys of these intellectuals who managed to enter the university in the context of electoral disputes that reshaped academic leadership roles.

User-generated content on social media has led to a new form of communication known as electronic word of mouth, which generates millions of comments about goods and services on the internet every day. This openly accessible content is crucial for prospective consumers as it helps in decision-making, but it is also valuable for product or service providers, as it allows them to improve their businesses based on user reviews, some of which are highly detailed. Another interest group that benefits from these comments are researchers and academics, as it allows them to obtain and analyse information for their studies at a relatively low cost in terms of time and money. The present study aims to perform a sentiment analysis of comments posted by guests staying at a property offered by Airbnb to determine whether their opinions about their experience are positive or negative. However, before doing so, it is necessary to find out the percentage of people who write a review about the service received on Airbnb to verify the representativeness of the reviews on this platform. To achieve this, thousands of comments posted in one year on Airbnb for the four most touristic cities in Spain are analysed: Madrid, Barcelona, Seville, and Valencia. The results show that opinions on Airbnb are much more representative compared to other platforms, as a very high participation rate is calculated. Furthermore, these opinions are predominantly positive, indicating a high level of satisfaction with the service provided. [ABSTRACT FROM AUTHOR]

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Sequence analysis is increasingly used in the social sciences for the holistic analysis of life-course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a regression model. This approach may be problematic, as cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analyses. Furthermore, it is often more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for uncertain and mixed memberships may lead to wrong conclusions about the nature of the studied relationships. In this article, the authors bring forward and discuss the problems of the "traditional" use of sequence analysis clusters as variables and compare four approaches for creating explanatory variables from sequence dissimilarities using different types of data. The authors conduct simulation and empirical studies, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust analyses accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions, and similarity-based approaches such as representativeness should be preferred. [ABSTRACT FROM AUTHOR]

The study of electoral disputes and the transformation of power structures has been notably lacking in the historiography of republican universities during the first half of the 19th century. This article delves into the experiences of faculty members elected through the vote, which played a significant role in reshaping leadership and expanding intellectual culture at Universidad del Magdalena e Istmo between 1827 and 1840. During this period, somewhat uniquely, enlightened republicans established universities at the departmental (provincial) level in what is now the Republic of Colombia. However, there has been limited examination of the trajectories and selection processes of the educated elite from the standpoint of intellectual history. This article argues that the use of the electoral mechanism, in addition to determining representativeness and the rise of representative government in universities, was a pivotal factor in the affirmation of an education and a secular power. This, on one hand, eroded the clerical influence in higher education and, on the other, displaced ecclesiastics from positions of authority. The advent of the university within the framework of representative democracy also led to the emergence of other types of intellectuals, linked to humanistic knowledge, medicine, and even military education in the navy. Methodologically, the goal is to explore the political journeys of these intellectuals who managed to enter the university in the context of electoral disputes that reshaped academic leadership roles. [ABSTRACT FROM AUTHOR]

Introduction: An important field of research on the role of women in contemporary political history centres on participation and representativeness, as well as the analysis of involvement in public policy and decision-making. Research objectives: The paper aims to analyse the participation and representativeness of women in political history by developing three research directions focused on: (a) examining the status of women in recent historiography (1950-2019); (b) analysis of the main concepts and associated topics in contemporary scientific literature; (c) analysis of some emerging constructs associated with the "gender history" field. Research methodology: The research uses the Ngram collection technique within the Google Books platform for the period 1950-2019. The analysis also reveals the frequencies of the analysed concepts and the conceptual associations that derive from the analysis of the literature published in the period at the centre of the analysis. Results and discussions: The results of our research are relevant in the much broader context of the analysis of women's participation and representativeness, as well as for the implications for contemporary political history, two patterns of analysis being identified: (a) women's participation and social changes and b) female political representation and the role of public policies in the contemporary period. Conclusions: The research highlights the role of female participation and representativeness arguing the need for proportional access of women in public life. [ABSTRACT FROM AUTHOR]

The application of precision farming techniques leads to significant improvements both in economical and environmental point of view, facilitates the optimization of resource use and reduces farmers’ risks in terms of natural and disease hazards but enhances the risk from technological faults. The whole article is to explore the attitudes of farmers regarding the possibilities and prospects for the implementation of precision agriculture technologies, identifying and establishing the most possible and unifying views and perceptions obtained from a field study. In this article, a statistical grouping is used to process surveys conducted in farms in Bulgaria. Given the growing role of technology and its importance in expanding, improving and stabilizing farms, the role of precision agriculture is becoming stronger in the agriculture industry [ABSTRACT FROM AUTHOR]

Introduction & Background Financial transaction data are highly valuable sources of digital footprints data for behavioural and economic research, but to properly create impact we must closely consider their limitations. Financial institutions hold a wealth of consumer data with untapped potential for community intelligence. These datasets combine excellent coverage with extremely granular information on consumer finances, income and spending, yet these institutions face great challenges in leveraging this data for social good. Smart Data Foundry is a university-owned, non-profit organisation that facilitates safe access to these datasets for researchers and provides insights to enable government bodies to tackle today's major challenges including the cost-of-living crisis and climate change. Objectives & Approach We will explore the opportunities afforded by these datasets for social and economic research. For example, using pseudonymised individual consumer banking data from NatWest Group, we have developed metrics for understanding income volatility and economic insecurity in collaboration with the Joseph Rowntree Foundation. We can also use these data to study consumer spending patterns and responses to economic changes such as interest rate rises and the net zero transition. We will assess the limitations of the data including issues of representativeness, bias, and missing data, and describe methods and mitigations to account for these challenges. We also discuss the barriers to accessing this type of data, in both relationship development with data partners, and privacy and governance concerns. Relevance to Digital Footprints Individual level customer transaction data provides a rich and novel form of digital footprint for behavioural and economic analyses. Every point of income or expenditure is recorded in a uniquely valuable digital footprint by financial institutions. These can provide a variety of insights, such as responses to macroeconomic shocks across demographic sets, emerging areas of financial distress, and help us better understand the drivers and risks of financial vulnerability. In both its aggregated and individual form, the data can provide an additional layer of understanding for trends we may see in other data, such as health or administrative data. Conclusions & Implications Having addressed the challenges of data access and data quality, we demonstrate that consumer banking data is an incredibly valuable form of digital footprints data, capturing key information on consumer behaviour. We conclude with a call for further research to develop use cases of this data for social good.

The U.S. Centers for Disease Control and Prevention (CDC) received surveillance data on how many people tested positive for SARS-CoV-2, but there was little information about what individuals did to mitigate transmission. To fill the information gap, we conducted an online, probability-based survey among a nationally representative panel of adults living in the United States to better understand the behaviors of individuals following a positive SARS-CoV-2 test result. Given the low response rates commonly associated with panel surveys, we assessed how well the survey data aligned with CDC surveillance data from March, 2020 to March, 2022. We used CDC surveillance data to calculate monthly aggregated COVID-19 case counts and compared these to monthly COVID-19 case counts captured by our survey during the same period. We found high correlation between our overall survey data estimates and monthly case counts reported to the CDC during the analytic period (r: +0.94; p

Background: Representativeness in clinical trials (CT) serves as a metric of access to healthcare and reflects differences that may determine differential efficacy of medical interventions; thus, quantifying representativeness in CT participation is critical. Methods: This retrospective, descriptive study utilized patient demographic data extracted from the largest Midwestern non-profit healthcare system. Using data between January 1, 2019 and December 31, 2021, a CT Participant Sample of 4,537 system patients who were active CT participants was compared to a CT Patient Population of 195,726 system patients receiving care by the PI of active CTs, which represented the target population. Chi-square goodness-of-fit tests were used to test differences in distributions of demographic variables between groups, indicating disparity in CT participation. Two metrics adapted from literature - participation incidence disparity (PID) and participation incidence ratio (PIR) - were calculated to quantify absolute and relative disparity in representativeness proportions, respectively. Descriptive approaches to assessing representativeness are also provided. Results: Results showed significant differences by race/ethnicity (χ2 = 50.64; p

Abstract Background There is growing interest in whether linked administrative data have the potential to aid analyses subject to missing data in cohort studies. Methods Using linked 1958 National Child Development Study (NCDS; British cohort born in 1958, n = 18,558) and Hospital Episode Statistics (HES) data, we applied a LASSO variable selection approach to identify HES variables which are predictive of non-response at the age 55 sweep of NCDS. We then included these variables as auxiliary variables in multiple imputation (MI) analyses to explore the extent to which they helped restore sample representativeness of the respondents together with the imputed non-respondents in terms of early life variables (father’s social class at birth, cognitive ability at age 7) and relative to external population benchmarks (educational qualifications and marital status at age 55). Results We identified 10 HES variables that were predictive of non-response at age 55 in NCDS. For example, cohort members who had been treated for adult mental illness had more than 70% greater odds of bring non-respondents (odds ratio 1.73; 95% confidence interval 1.17, 2.51). Inclusion of these HES variables in MI analyses only helped to restore sample representativeness to a limited extent. Furthermore, there was essentially no additional gain in sample representativeness relative to analyses using only previously identified survey predictors of non-response (i.e. NCDS rather than HES variables). Conclusions Inclusion of HES variables only aided missing data handling in NCDS to a limited extent. However, these findings may not generalise to other analyses, cohorts or linked administrative datasets. This work provides a demonstration of the use of linked administrative data for the handling of missing cohort data which we hope will act as template for others.

Abstract Background There are increasing concerns that participants in health research in the UK are not representative of the UK population, risking widening health inequities. However, detailed information on the magnitude of the problem is limited. Therefore, we evaluated if the health research conducted in the Greater Manchester region was broadly representative of its diverse population. Methods We conducted an audit of all health research studies conducted exclusively in Greater Manchester, using data from a national research network. Two researchers selected studies that were (1) an interventional or observational study of a health outcome; (2) ‘closed’ for recruitment between May 2016 and May 2021 and (3) human research. They extracted study information (dates, contacts, sample recruited, clinical speciality). Participant characteristics were sourced from published and unpublished manuscripts and requested directly from principal investigators and named study contacts. Data were extracted, summarised and compared to the Greater Manchester population for the following metrics: ethnicity, sex, age, deprivation and smoking status. A weighted mean age estimate was calculated to account for variation in age reporting. Too few studies provided patient-level deprivation data so, using the area code of the recruitment site, the area level multiple deprivation, health deprivation and disability index and decile was derived. These data were geo-mapped using QGIS 3.26. Results Overall, 145/153 (95%) studies met inclusion criteria and participant information was sourced for 85/145 (59%) studies, representing 21,797 participants. Participant information was incomplete for all metrics. Where ethnicity (N = 10,259) data were available and compared to Greater Manchester estimates there was evidence that ethnic minorities were under-represented (6% versus 16%). Most of the recruitment occurred in central Manchester (50%) and with NHS hospital settings (74%). Conclusions Greater Manchester health research in 2016–2021 was centralised and under-represented ethnic minorities. We could not report which ethnic minority group was least represented because sourcing detailed participant information was challenging. Recommendations to improve the reporting of key participant characteristics with which to monitor representativeness in health research are discussed.

Background: Antipsychotics are effective in controlled environments for treatment of schizophrenia. However, there is hardly any research on the effects of antipsychotics in years-long follow-ups in antipsychotic trials. In addition, study samples of the antipsychotic trials differ quite notably from the day-to-day clinical treatment population. In this study, we aimed to compare the population-based Northern Finland Birth Cohort 1966 (NFBC1966) population to the samples in antipsychotic trials using typical inclusion and exclusion criteria found in randomized clinical trials (RCTs). We also compared long-term outcomes of individuals with schizophrenia meeting and not meeting inclusion criteria for clinical trials. Method: We gathered clinical antipsychotic trials and their inclusion and exclusion criteria. These inclusion and exclusion criteria were compared to the data of NFBC1966 to find out how many of the 54 participants in NFBC1966 34-year follow-up would meet these criteria, and how representative outcomes of the clinical antipsychotic trials are compared to a longitudinal population-based sample. Result: Depending on how strict the inclusion and exclusion criteria of the RCTs were, 10.5 to 24.6 per cent of the participants in the NFBC1966 34-year follow-up could have been included in RCTs using the criteria. Notably, 42.1 per cent of the participants in the NFBC1966 would be excluded without considering PANSS as inclusion criteria. There was no statistically significant difference in the distribution of PANSS and SOFAS scores between the included and excluded groups in longitudinal analysis. However, in the distribution of hospital treatment days in the groups included and excluded by criteria of RCTs, there was a statistically significant difference between the groups. Conclusions: Over one-third of the participants of this population sample could have been excluded from clinical trials without even considering psychotic symptoms as an inclusion criterion.

Melothria pendula L., a wild relative of cucurbit crops, is also used for food and as a medicinal plant in Mexico. The objective of this study was to ecogeographically characterize the known populations of M. pendula in Mexico, determining its adaptive range and possible sites for in situ and ex situ conservation. To achieve this goal, we compiled a dataset of 1270 occurrences of M. pendula from herbarium and botanical databases and individual observations. Adaptive scenarios were generated through the development of an ecogeographic land characterization (ELC) map, preceded by the identification of abiotic variables influencing the species’ distribution. Eleven bioclimatic, edaphic, and geophysical variables were found to be important for the species’ distribution. The ELC map obtained contained 21 ecogeographic categories, with 14 exhibiting the presence of M. pendula. By analyzing ecogeographic representativeness, 111 sites of high interest were selected for the efficient collection of M. pendula in Mexico. Eight high-priority hotspots for future in situ conservation of M. pendula were also identified based on their high ecogeographic diversity, with only three of these hotspots located within protected natural areas. In this study, ecogeographic approaches show their potential utility in conservation prioritization when genetic data are scarce, a very common condition in crop wild relatives.