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This paper presents a mathematical programming model to simultaneously create districts and allocate seats in Chile's Chamber of Deputies, improving representativeness. In addition, it explicitly incorporates constraints that ensure the contiguity of the communes that form the districts while respecting natural and administrative boundaries. Implementing specific strategies and methods has resulted in significant enhancements in particular metrics used to assess the degree of representativeness. These improvements have effectively addressed certain shortcomings and resulted in more accurate and reliable representation measurements in the given context. This study proposes a novel mathematical programming model that simultaneously tackles district creation and seat allocation for Chile's Chamber of Deputies. This integrated approach aims to achieve a more representative body. The results demonstrate a substantial decrease in malapportionment, from 11.07 in the 2015 reform to 6.55 under the proposed model. Furthermore, the sum of deviations has diminished, and the number of overrepresented districts has decreased from 17 to 13 out of 28 districts. Consequently, the malapportionment has been significantly reduced and falls within the permissible range of deviations, as outlined by the European Commission for Democracy through Law. [ABSTRACT FROM AUTHOR]

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]

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]

In the dynamic realm of equity investment decisions, this study challenges the traditional assumption of investor impartiality and rationality, arguing that investors' choices are often shaped more by beliefs and psychological inclinations than by objective reasoning alone. Drawing from behavioural finance theory, which contests the adequacy of the standard finance model in capturing the nuances of decision-making, this research delves into the psychological forces influencing private investors. Recognizing the prevalent biases coloring their decisions, the study investigates common prejudices impacting investor choices. Leveraging survey data from 375 equity investors, the research employs exploratory factor analysis and regression models to disentangle the effects of cognitive biases, such as the gambler's fallacy, overconfidence, aversion to ambiguity, lack of representativeness, aversion to loss, and anchoring. The findings challenge the assumption of investor rationality, offering valuable insights into the complexities of financial decision-making. Notably, the study underscores a robust and positive association between these biases and equity investment decisions, shedding light on the intricate interplay between psychological factors and investment behaviour. [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.

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]

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]

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]

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]

There are twenty experimental forest and range sites (EFRs) across the southeastern United States that are currently maintained by the USDA Forest Service (Forest Service) to conduct forest ecosystem research for addressing ecosystem management challenges. The overall objective of this study was to use multiple gridded datasets to assess the extent to which the twenty EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests. The EFRs represent the large variability of climate conditions across the region relatively well, but we identified small representation gaps. The representativeness of ecosystem structure by these EFRs can be improved by establishing EFRs in forests with relatively low tree cover, leaf area index, or tree canopy height. The current EFRs also represent the forest ecosystem functions of the region relatively well, although areas with intermediate and low aboveground biomass and water yield are not well represented. The trends in climate, ecosystem structure, and ecosystem functions were generally consistent between the region and the EFRs. Our study indicates that the current EFRs represent the region relatively well, but establishing additional EFRs in specific areas within the region could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices. Study Implications : This study across the experimental forests and ranges (EFRs) and the southeastern forest region fills the knowledge gap regarding climate, ecosystem structure, and ecosystem functions of EFRs in the context of the broader southeastern forest region. Understanding ecosystem functions and structures across the EFR network can help the Southern Research Station to address new research questions. Our study indicates that the current EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests well. However, establishing additional EFRs in certain regions could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices. [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.

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.

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.

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.

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]

The present work aims to analyze the impacts on political parties (and on democracy itself) of the ban on coalitions in proportional elections stipulated in Brazilian legislation through Constitutional Amendment 97/2017. To this end, it examines some of the most significant issues related to the subject, notably: the relationship between democracy, representativeness and parties; the majoritarian and proportional systems; and the trend towards fewer parties. In methodological terms, it relies mainly on bibliographic research. In the final part of the article, it is also supported by a quantitative survey of data provided by the Superior Electoral Court (TSE), with the aim of presenting a concrete example of an election. It is anticipated that, in addition to the barrier clause, the ban on coalitions could result in the elimination of smaller parties, compromising the logic of the representative system and the constitutional guarantee of multi-partyism and representativeness. The results show that there is a risk that the reduction in the number of parties will not succeed, and that the participation of parties representing minorities will be strangled, maintaining the presence of larger groups, less ideologically committed parties. The conclusion is that the reform introduced by the constitutional amendment needs to be further developed if the aim is to invest effectively against the reproduction of parties that are not committed to representativeness. [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]

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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]

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.

CTD profilers are used as reference instruments to qualify temperature and salinity data. Their metrological specifications can be controlled in a calibration bath, and calibration coefficients can be applied to correct the linearity of sensors and the trueness of measured data with a given uncertainty. However, in ocean areas with thermal gradients, the uncertainty of the measured data is questionable due to the thermal inertia of sensors and the movements of the CTD in relation to the roll or pitch of the boat. In order to evaluate these measurement uncertainties and in order to be able to use the upcast profiles, a double C–T sensor SBE 9 profiler was fixed under a carousel water sampler, the second C–T couple being at the top of the carousel frame. This configuration allows the evaluation of the temperature measurement deviations of recorded profiles. In order to quantify the different sources of instrumental uncertainties, the temperature signal has been modelled accounting for the movements induced by the boat. The result allows one to quantify what can be called the representativeness of CTD’s temperature measurements. This notion is very useful in the data assimilation process. A table quantifying the various sources of uncertainty has been created from profiles obtained during four offshore campaigns. In the future, it could be used to find the representativeness of similar profiles obtained with a single pair of sensors. Ship-based CTD profiles are generally considered as perfect or without uncertainty in data assimilation and in the qualification per comparison of other instruments (XBT, Argo profiles, etc.). Our findings imply that this hypothesis will have to be reconsidered.

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.

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]

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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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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]

Copyright of Revista Româna de Dreptul Muncii is the property of Wolters Kluwer Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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.