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In this article, we review the ways in which replication has been and could be featured in consumer behavior, using Journal of Consumer Research as a specific setting. We present a framework for thinking about the generalizability of research findings and differentiate various potential benefits that replication can have for understanding variability in consumer research findings. We then define four different types of replications, describe how researchers can use these approaches to produce distinct benefits, and give guidance regarding conducting, interpreting, and the potential contributions of these different types of replications. We conclude with a discussion of various ways in which replication could be more fully integrated into different phases of the scientific research process, taking into account the contribution necessary for publication. In particular, we identify opportunities to incorporate independent replication into original papers, to increase the replication-based contribution in papers that build on prior work, and to use systematic replication in conjunction with meta-analysis to synthesize and confirm conclusions from a mature research literature. More fully integrating replication into scientific practice can yield a new equilibrium, in which replication is routine, typically consistent with previous results, and recognized as necessary for establishing an empirical generalization. [ABSTRACT FROM AUTHOR]

Given the promise of the web push plus e-mail survey design for providing cost-effective and high-quality data (Patrick et al. 2018 , 2019) as an alternative to a paper-and-pencil mailed survey design for the longitudinal Monitoring the Future (MTF) study, the current study sought to further enhance the web push condition. The MTF sample is based on US nationally representative samples of 12th grade students surveyed annually. The MTF control group for the current study included participants who completed the in-school baseline survey in the 12th grade and were selected to participate in their first follow-up survey in 2017 via mailed surveys (N  = 1,222). A supplementary sample (N = ∼2,450) was assigned to one of the two sequential mixed-mode conditions. Those in condition 1 (N = 1,198), or mail push, were invited to complete mailed surveys and later given a web survey option. Those in condition 2 (N  = 1,173), or enhanced web push, were invited to complete a web survey (the same as in the 2014 study, but with the addition of text messages and quick response (QR) codes and the web survey was optimized for mobile devices) and then later given a mailed survey option. Research aims were to examine response rates across conditions, as well as how responses were distributed across mode (paper, web), devices (computer, smartphone, table), and method of accessing the web survey (hand-entered URL, QR code, e-mail link, SMS link). Response rates differed significantly: the MTF control group was 34.2 percent, mail push was 35.4 percent, and enhanced web push was 42.05 percent. The higher response rate in the enhanced web push condition suggests that the additional strategies were effective at bringing in more respondents. Key estimates produced by the enhanced web push condition did not differ from those of the MTF control group. [ABSTRACT FROM AUTHOR]

Using the concept of methodological congruence—where the different elements of a study 'fit' together—we explore both problematic and good practice in (reflexive) thematic analysis (TA) as reported in Health Promotion International (HPI). Aligning with the importance we place on 'owning your perspectives' we situate this exploration in relation to our understanding of the variation in approaches to TA and qualitative research more broadly. This contextualization is necessary for highlighting why we judge practices to be in/congruent, and to facilitate more knowing congruence in future research. We critically reviewed a 'sample' of 31 papers published in HPI between 2010 and 2023 citing Braun and Clarke as reference for TA. We overview a range of problematic and good features of the use of TA in HPI , before focusing on two domains that seemed to present key challenges: theory and themes. Methodological incongruence can occur when postpositivist values and practices unwittingly creep into ostensibly non-positivist TA; we encourage thoughtfully and what we term 'knowing' consideration of theory, and quality practices and criteria. Methodological incongruence can also occur through mismatched conceptualizations of themes—notably, the use of 'topic summaries' as themes for reflexive TA (and fragmented thematic structures with 'thin' themes). We provide examples from the reviewed papers to demonstrate good practice in researcher reflexivity, articulation of theoretical and methodological frameworks and congruent themes. However, mindful of power dynamics, we only discuss problematic practice in general terms, to protect author anonymity. To facilitate thoughtful, quality TA—of all kinds—we provide eight pointers for researchers (and reviewers) to guide quality practice, and facilitate the use of concepts, procedures and criteria that promote knowing methodological congruence. [ABSTRACT FROM AUTHOR]

Burns are the fourth most common cause of injury worldwide. The burden of burn injury is largely carried by low- and middle-income countries (LMICs) with children in these regions being particularly vulnerable to burns. The purpose of this scoping review is to identify knowledge gaps in global pediatric burn care experience in an effort to help prioritize future research. Using the 6-stage Arksey and O'Malley framework for conducting a scoping review, pediatric burn care literature was reviewed over a 10-year period from 2010 to 2020. Studies from low-resource settings were included and categorized by geographic location, study patient population, study type, type of burn, and level of evidence. Of 107 included studies, 34% and 49% originated from LMICs in South Asia and Sub-Saharan Africa, respectively. Qualitative/observational (73%) and epidemiological single-center (20%) publications comprised the majority of included papers. The majority (95%) of all papers regardless of geographical location were lower levels of evidence (Level 3 or below). Our study identified four primary knowledge gaps: 1) a paucity of high-quality studies to guide burn care in LMICs; 2) an under-representation of articles from certain geographical locations; 3) few therapeutic and economic articles to guide resource allocation; and 4) a lack of articles with long-term patient follow-up. Efforts to address these disparities could help reduce the pediatric burn burden of disease in resource-poor settings. [ABSTRACT FROM AUTHOR]

Background and Objectives Globally aging populations raise worldwide concerns about how an older population will be valued. Cross-culturally, many espouse that Eastern cultures revere their older adults more than Westerners, due to stronger collectivism and filial piety traditions. In contrast, this paper proposes a resource tension hypothesis, whereby rapid population aging causes pragmatic strain across all modernized societies, fostering ageism. Research Design and Methods Three studies supported this resource tension hypothesis, focusing on the pragmatic role of public pensions—a fundamental resource inherently pitting older versus younger generations—in fostering ageism. Study 1 tested the relationship between nation-level public pension rate and attitudes toward older adults by using World Values Survey and European Social Survey data sets. Study 2 further explored this relationship via priming both the pension-based resource scarcity and the intergenerational competition over the public pension. Study 3 offered an intervention—future-self-thinking via a photo ager—on reducing intergenerational tensions under pension scarcity conditions. Results Study 1 found a significant link between nation-level public pension rate and negative older adult attitudes across 39,700 World Values Survey, and 29,797 European Social Survey data points. Study 2 further supported the pension-ageism link via experimental methods. Participants who were reminded of the scarcity of pensions and intergenerational competition exhibited more negative attitudes toward older adults. Study 3 confirmed the effect of the future-self intervention on enhancing attitudes toward older adults even despite scarce pension resources. Discussion and Implications The findings support a resource explanation in driving perceptions of older adults, implicate pensions as a key mechanism driving intergenerational attitudes, and identify future-self thinking as a critical intervention. The present studies open up new research pathways for understanding and accommodating the globally aging population. [ABSTRACT FROM AUTHOR]

Often, comparative experiments involve a single treatment factor and two blocking factors, for example, augmented row–column, two‐phase, and incomplete row–column experiments. These experiments are widely used in agriculture. Finding good designs for these experiments is a major challenge when the number of treatments is large and the blocking structure is complex. In this paper, we first propose a new search algorithm that is combined with efficient update formulae, so that optimal designs with two blocking factors can be found within a reasonable time. Second, we compare augmented row–column designs generated with our new method to those obtained from CycDesigN, DiGGer, and the OPTEX procedure of SAS in terms of computing times as well as the quality of solutions. Third, we illustrate our proposed approach with four applications. We show an example where our efficient update formulae work while existing update formulae cannot be applied, and we use our search framework to generate augmented row–column, two‐phase, and incomplete row–column designs. We end the paper with a conclusion along with suggestions for potential applications. [ABSTRACT FROM AUTHOR]

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have grown in popularity as they offer a more individualized approach. As a result, sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, sample size and design considerations have generally been carried out in frequentist settings. However, standard frequentist formulae require assumptions on interim response rates and variance components. Misspecifying these can lead to incorrect sample size calculations and correspondingly inadequate levels of power. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the 'two priors' approach. Additionally, compared to the standard frequentist formulae, this methodology allows us to rely on fewer assumptions, integrate pre‐trial knowledge, and switch the focus from the standardized effect size to the MDD. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full‐scale SMART of an internet‐based adaptive stress management intervention on cardiovascular disease patients using data from its pilot study conducted in two Canadian provinces. [ABSTRACT FROM AUTHOR]

Batch marking is common and useful for many capture–recapture studies where individual marks cannot be applied due to various constraints such as timing, cost, or marking difficulty. When batch marks are used, observed data are not individual capture histories but a set of counts including the numbers of individuals first marked, marked individuals that are recaptured, and individuals captured but released without being marked (applicable to some studies) on each capture occasion. Fitting traditional capture–recapture models to such data requires one to identify all possible sets of capture–recapture histories that may lead to the observed data, which is computationally infeasible even for a small number of capture occasions. In this paper, we propose a latent multinomial model to deal with such data, where the observed vector of counts is a non‐invertible linear transformation of a latent vector that follows a multinomial distribution depending on model parameters. The latent multinomial model can be fitted efficiently through a saddlepoint approximation based maximum likelihood approach. The model framework is very flexible and can be applied to data collected with different study designs. Simulation studies indicate that reliable estimation results are obtained for all parameters of the proposed model. We apply the model to analysis of golden mantella data collected using batch marks in Central Madagascar. [ABSTRACT FROM AUTHOR]

Two‐stage randomized experiments become an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignments of other units in the same cluster. In this paper, we provide a methodological framework for general tools of statistical inference and power analysis for two‐stage randomized experiments. Under the randomization‐based framework, we consider the estimation of a new direct effect of interest as well as the average direct and spillover effects studied in the literature. We provide unbiased estimators of these causal quantities and their conservative variance estimators in a general setting. Using these results, we then develop hypothesis testing procedures and derive sample size formulas. We theoretically compare the two‐stage randomized design with the completely randomized and cluster randomized designs, which represent two limiting designs. Finally, we conduct simulation studies to evaluate the empirical performance of our sample size formulas. For empirical illustration, the proposed methodology is applied to the randomized evaluation of the Indian National Health Insurance Program. An open‐source software package is available for implementing the proposed methodology. [ABSTRACT FROM AUTHOR]

A central goal in designing clinical trials is to find the test that maximizes power (or equivalently minimizes required sample size) for finding a false null hypothesis subject to the constraint of type I error. When there is more than one test, such as in clinical trials with multiple endpoints, the issues of optimal design and optimal procedures become more complex. In this paper, we address the question of how such optimal tests should be defined and how they can be found. We review different notions of power and how they relate to study goals, and also consider the requirements of type I error control and the nature of the procedures. This leads us to an explicit optimization problem with objective and constraints that describe its specific desiderata. We present a complete solution for deriving optimal procedures for two hypotheses, which have desired monotonicity properties, and are computationally simple. For some of the optimization formulations this yields optimal procedures that are identical to existing procedures, such as Hommel's procedure or the procedure of Bittman et al. (2009), while for other cases it yields completely novel and more powerful procedures than existing ones. We demonstrate the nature of our novel procedures and their improved power extensively in a simulation and on the APEX study (Cohen et al., 2016). [ABSTRACT FROM AUTHOR]

In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a "balancing intercept," that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods. [ABSTRACT FROM AUTHOR]

In many practical experiments, both the level combinations of factors and the addition orders will affect the responses. However, virtually no construction methods have been provided for such experimental designs. This paper focuses on such experiments, introduces a new type of design called the ordering factorial design, and proposes the nominal main effect component-position model and interaction-main effect component-position model. To obtain efficient fractional designs, we provide some deterministic construction methods. The resulting designs are D -optimal, and the run sizes are much smaller than that of the full designs. Moreover, in some cases, some constructed designs are still D -optimal after reducing the number of components and factors. [ABSTRACT FROM AUTHOR]

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery. [ABSTRACT FROM AUTHOR]

This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed‐form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare‐disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented. [ABSTRACT FROM AUTHOR]

Age-related changes in immune competency and inflammation play a role in the decline of physical function. In this review of the conference on Function-Promoting Therapies held in March 2022, we discuss the biology of aging and geroscience with an emphasis on decline in physical function and the role of age-related changes in immune competence and inflammation. More recent studies in skeletal muscle and aging highlighting a crosstalk between skeletal muscle, neuromuscular feedback, and immune cell subsets are also discussed. The value of strategies targeting specific pathways that affect skeletal muscle and more systems-wide approaches that provide benefits in muscle homeostasis with aging are underscored. Goals in clinical trial design and the need for incorporating differences in life history when interpreting results from these intervention strategies are important. Where applicable, references are made to papers presented at the conference. We conclude by underscoring the need to incorporate age-related immune competency and inflammation when interpreting results from interventions that target specific pathways predicted to promote skeletal muscle function and tissue homeostasis. [ABSTRACT FROM AUTHOR]

Qualitative research methods, while rising in popularity, are still a relatively underutilized tool in public health research. Usually reserved for small samples, qualitative research techniques have the potential to enhance insights gained from large questionnaires and cohort studies, both deepening the interpretation of quantitative data and generating novel hypotheses that might otherwise be missed by standard approaches; this is especially true where exposures and outcomes are new, understudied, or rapidly changing, as in a pandemic. However, methods for the conduct of qualitative research within large samples are underdeveloped. Here, we describe a novel method of applying qualitative research methods to free-text comments collected in a large epidemiologic questionnaire. Specifically, this method includes: 1) a hierarchical system of coding through content analysis; 2) a qualitative data management application; and 3) an adaptation of Cohen's κ and percent agreement statistics for use by a team of coders, applying multiple codes per record from a large codebook. The methods outlined in this paper may help direct future applications of qualitative and mixed methods within large cohort studies. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present an original approach to estimate the gender pay gap (GPG). We propose a model‐based decomposition, similar to the most popular approaches, where the first component measures differences in group characteristics and the second component measures the unexplained effect; the latter being the real gap. The novel approach incorporates model selection and bias correction. The pay gap problem in a small area context is considered in this paper, although the approach is flexible to be applied to other contexts. Specifically, the methodology is validated for analysing wage differentials by economic activities in the region of Galicia (Spain) and by analysing simulated data from an experimental design that imitates the generation of real data. The good performance of the proposed estimators is shown in both cases, specifically when compared with those obtained from the widely used Oaxaca–Blinder approach. [ABSTRACT FROM AUTHOR]

"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information. [ABSTRACT FROM AUTHOR]

Pollinators are critical for agricultural production and food security, leading to many ongoing surveys of pollinators (especially bees) in crop and adjacent landscapes. These surveys have become increasingly important to better understand the community of potential pollinators, quantify relative insect abundance, and secure crop ecosystem services. However, as some bee populations are declining, there is a need to align and improve bee survey efforts, so that they can best meet research and conservation goals, particularly in light of the logistical and financial constraints of conducting such studies. Here, we mined the existing literature on bee surveys in or around agricultural lands to better understand how sampling methods can be optimized to maximize estimates of 2 key measures of bee communities (abundance and richness). After reviewing 72 papers spanning 20 yr of publication, we found that study duration, number of sites, sampling time, and sampling method most significantly influenced abundance, while the number of trips per year and collection method significantly influenced richness. Our analysis helps to derive thresholds, priorities, and recommendations that can be applied to future studies describing bee communities in agroecosystems. [ABSTRACT FROM AUTHOR]

Repeated claims that a dwindling supply of potential caregivers is creating a crisis in care for the U.S. aging population have not been well-grounded in empirical research. Concerns about the supply of family care do not adequately recognize factors that may modify the availability and willingness of family and friends to provide care to older persons in need of assistance or the increasing heterogeneity of the older population. In this paper, we set forth a framework that places family caregiving in the context of older adults' care needs, the alternatives available to them, and the outcomes of that care. We focus on care networks, rather than individuals, and discuss the demographic and social changes that may alter the formation of care networks in the future. Last, we identify research areas to prioritize in order to better support planning efforts to care for the aging U.S. population. [ABSTRACT FROM AUTHOR]

In this paper, we propose a framework for thinking through the design and conduct of descriptive epidemiologic studies. A well-defined descriptive question aims to quantify and characterize some feature of the health of a population and must clearly state: 1) the target population, characterized by person and place, and anchored in time; 2) the outcome, event, or health state or characteristic; and 3) the measure of occurrence that will be used to summarize the outcome (e.g. incidence, prevalence, average time to event, etc.). Additionally, 4) any auxiliary variables will be prespecified and their roles as stratification factors (to characterize the outcome distribution) or nuisance variables (to be standardized over) will be stated. We illustrate application of this framework to describe the prevalence of viral suppression on December 31, 2019, among people living with human immunodeficiency virus (HIV) who had been linked to HIV care in the United States. Application of this framework highlights biases that may arise from missing data, especially 1) differences between the target population and the analytical sample; 2) measurement error; 3) competing events, late entries, loss to follow-up, and inappropriate interpretation of the chosen measure of outcome occurrence; and 4) inappropriate adjustment. [ABSTRACT FROM AUTHOR]

Qualitative research is fundamental to understanding the nature and complexity of human phenomena. While cultural and psychometric validations exist for quantitative tools, the same cannot be said of qualitative ones. There are other many challenges when conducting a multinational qualitative study, which includes different cultural and linguistic 'biases'. This paper presents some key issues that researchers may encounter when designing and developing multinational and multicultural qualitative studies, and also provides some strategies to overcome difficulties and ensure rigour. [ABSTRACT FROM AUTHOR]

Health care is undergoing a profound technological and digital transformation and has become increasingly complex. It is important for burns professionals and researchers to adapt to these developments which may require new ways of thinking and subsequent new strategies. As Einstein has put it: "We must learn to see the world anew." The relatively new scientific discipline "Complexity science" can give more direction to this and is the metaphorical open door that should not go unnoticed in view of the burn care of the future. Complexity science studies "why the whole is more than the sum of the parts." It studies how multiple separate components interact with each other and their environment and how these interactions lead to "behavior of the system." Biological systems are always part of smaller and larger systems and exhibit the behavior of adaptivity, hence the name complex adaptive systems. From the perspective of complexity science, a severe burn injury is an extreme disruption of the "human body system." But this disruption also applies to the systems at the organ and cellular levels. All these systems follow the principles of complex systems. Awareness of the scaling process at multilevel helps to understand and manage the complex situation when dealing with severe burn cases. This paper aims to create awareness of the concept of complexity and to demonstrate the value and possibilities of complexity science methods and tools for the future of burn care through examples from preclinical, clinical, and organizational perspectives in burn care. [ABSTRACT FROM AUTHOR]

Several countries have created policy instruments seeking to direct research and innovation (R&I) toward addressing societal challenges. However, the design of such instruments might not always live up to their proclaimed transformative rationale. The aim of this paper is to examine empirically this matter. In a unique cross country comparison of four Grand Challenge-oriented R&I programs in the Nordic countries, we ask to what extent the design of new policy instruments for grand challenges are nested according to the rationale of transformative R&I policy. The findings show that, while all have individual transformative elements, they only exhibit weak or medium degrees of nesting. At a time of increasing transformative ambition of R&I policies, our findings make an important contribution to understanding and addressing the complexity of designing R&I transformative policy instruments. [ABSTRACT FROM AUTHOR]

Digital advertising platforms have emerged as a widely utilized data source in consumer research; yet, the interpretation of such data remains a source of confusion for many researchers. This article aims to address this issue by offering a comprehensive and accessible review of four prominent data collection methods proposed in the marketing literature: "informal studies," "multiple-ad studies without holdout," "single-ad studies with holdout," and "multiple-ad studies with holdout." By outlining the strengths and limitations of each method, we aim to enhance understanding regarding the inferences that can and cannot be drawn from the collected data. Furthermore, we present seven recommendations to effectively leverage these tools for programmatic consumer research. These recommendations provide guidance on how to use these tools to obtain causal and non-causal evidence for the effects of marketing interventions, and the associated psychological processes, in a digital environment regulated by targeting algorithms. We also give recommendations for how to describe the testing tools and the data they generate and urge platforms to be more transparent on how these tools work. [ABSTRACT FROM AUTHOR]

There is a long tradition of incorporating patient-reported outcomes (PROs) into nursing research and practice. Classical Test Theory (CTT) has been the traditional approach used to develop and refine PROs. Item response theory (IRT) offers promise for addressing measurement problems that have been difficult to solve using CTT. This paper presents foundational concepts in IRT to illustrate how it can be used to improve the development and refinement of PRO measures, with emphasis on interpreting key IRT parameters. [ABSTRACT FROM AUTHOR]

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives. [ABSTRACT FROM AUTHOR]

Participatory research in multiple forms is rapidly gaining ground and stands to provide valuable benefits in the area of health research. Citizen science, an increasingly popular form of participatory research, aims to involve laypersons in knowledge production and, as such, can offer new insights that may not be readily accessible by the traditional scientific research enterprise. Heralded as an advance in the democratization of science, citizen science stands as a potential player in evidence generation. Yet, while citizen science offers advantages through its emphasis on lay participation in uncovering information likely to be pertinent to improving health outcomes, without deliberate attention to pathways to inequity and oversight, some aspects can operate to perpetuate or exacerbate health inequities. Drawing on health disparities literature to assist in identifying possible pathways, this work aims to provide support for greater attention to 'process', which would include active engagement with intersecting inequities as a way of interrupting pathways to health inequity. If citizen science is to be true to its moniker, it cannot be 'equity-neutral' in a society where structural or systemic inequities are present. It must be equity-promoting. In the health context, the concerns of justice evolve into concerns of differential health impacts. To accomplish this, equity and justice, as well as representation and fair process, become critically important. Accordingly, this paper foregrounds proactive commitment to equity by confronting the reality of social inequalities present in a given society and adapting the project accordingly to minimize downstream health inequities. [ABSTRACT FROM AUTHOR]

Procode is a free of charge web-tool that allows automatic coding of occupational data (free-texts) by implementing Complement Naïve Bayes (CNB) as a machine-learning technique. The paper describes the algorithm, performance evaluation, and future goals regarding the tool's development. Almost 30 000 free-texts with manually assigned classification codes of French classification of occupations (PCS) and French classification of activities (NAF) were used to train CNB. A 5-fold cross-validation found that Procode predicts correct classification codes in 57–81 and 63–83% cases for PCS and NAF, respectively. Procode also integrates recoding between two classifications. In the first version of Procode, this operation, however, is only a simple search function of recoding links in existing crosswalks. Future focus of the project will be collection of the data to support automatic coding to other classification and to establish a more advanced method for recoding. [ABSTRACT FROM AUTHOR]

As our understanding of the microbiome has expanded, so has the recognition of its critical role in human health and disease, thereby emphasizing the importance of testing whether microbes are associated with environmental factors or clinical outcomes. However, many of the fundamental challenges that concern microbiome surveys arise from statistical and experimental design issues, such as the sparse and overdispersed nature of microbiome count data and the complex correlation structure among samples. For example, in the human microbiome project (HMP) dataset, the repeated observations across time points (level 1) are nested within body sites (level 2), which are further nested within subjects (level 3). Therefore, there is a great need for the development of specialized and sophisticated statistical tests. In this paper, we propose multilevel zero-inflated negative-binomial models for association analysis in microbiome surveys. We develop a variational approximation method for maximum likelihood estimation and inference. It uses optimization, rather than sampling, to approximate the log-likelihood and compute parameter estimates, provides a robust estimate of the covariance of parameter estimates and constructs a Wald-type test statistic for association testing. We evaluate and demonstrate the performance of our method using extensive simulation studies and an application to the HMP dataset. We have developed an R package MZINBVA to implement the proposed method, which is available from the GitHub repository https://github.com/liudoubletian/MZINBVA. [ABSTRACT FROM AUTHOR]

The mine ventilation system plays a role in purifying the air and providing a good working environment in coal mine production. Aiming at the unclear concept and inconsistency in solving problems in the process of intelligent construction of mine ventilation system, this paper conducts exploratory research on the connotation of mine ventilation intelligence, theoretical structure model, construction logic and scientific problems to be solved by means of literature review, on-site investigation and qualitative analysis. The research results show that the mine ventilation intelligent system needs to use real-time and full monitoring data, and by integrating brain science principles, machine learning and analysis and other technologies, it can immediately correct the air volume distribution defects, realize automatic power adjustment, remote facility management and control and accurate disaster identification. The theoretical model from the inside to the outside is the concept layer, the key scientific issues and technology layer, the model layer, the application layer and the theoretical support layer. The intelligent process of mine ventilation needs to comprehensively consider the relationship between the conscious human body, the cyber body and the ventilation physical entity in the processes of knowledge accumulation, knowledge modeling, knowledge movement, innovation and optimization, learning progress and feedback evolution. The intelligent ventilation system consists of perception layer, transmission layer and application layer. The research provides a theoretical basis for the intelligent construction of mine ventilation. [ABSTRACT FROM AUTHOR]

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW , a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM , npcausal , tmle , and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies. [ABSTRACT FROM AUTHOR]

In the context of increasing numbers of refugee students being integrated into the national school system of host countries globally, the necessity of inclusive school environments for these students is growing. Yet schools may not be well prepared and supported to meet the unique needs of refugee students, who may struggle to adjust to their new reality. In this paper, we present the results of a mixed methods case study that explored aspects of the social participation experiences of three Syrian refugee siblings in a public elementary school in Cyprus, as a telling case for the struggles of refugee children to socially participate with their peers in asylum-country public schools. Social network and interview data were collected to examine how effectively the three refugee siblings participate in school life and the factors that may hinder their social participation. The findings are fairly worrisome, as the three refugee siblings were found to maintain a lower social participation status than their non-refugee peers. [ABSTRACT FROM AUTHOR]

Starting in the 2010s, researchers in the experimental social sciences rapidly began to adopt increasingly open and reproducible scientific practices. These practices include publicly sharing deidentified data when possible, sharing analytical code, and preregistering study protocols. Empirical evidence from the social sciences suggests such practices are feasible, can improve analytical reproducibility, and can reduce selective reporting. In academic epidemiology, adoption of open-science practices has been slower than in the social sciences (with some notable exceptions, such as registering clinical trials). Epidemiologic studies are often large, complex, conceived after data have already been collected, and difficult to replicate directly by collecting new data. These characteristics make it especially important to ensure their integrity and analytical reproducibility. Open-science practices can also pay immediate dividends to researchers' own work by clarifying scientific reasoning and encouraging well-documented, organized workflows. We consider how established epidemiologists and early-career researchers alike can help midwife a culture of open science in epidemiology through their research practices, mentorship, and editorial activities. [ABSTRACT FROM AUTHOR]

In the last two decades, co-creation and social innovation have become important concepts in academic research and public policy. The two concepts are conceptually linked, but this relationship has hardly been problematized in academic literature. In addition, social innovation and especially co-creation are not defined in EU policies, but merely included because they support policy aims. The lack of problematization and definition not only hampers progress in the academic field, but is also constringing co-creation into an exercise of merely including stakeholders therefore neglecting the full potential of co-creation. The key question addressed in this article is therefore: how can we evaluate the application of co-creation in EU-funded social innovation projects? A literature review revealed that co-creation and social innovation have become connected only very recently in academic literature. In this publication, we analyse the meta narratives of this emerging body of literature and conclude that we can distinguish three distinct segments with their own characteristics. We used these insights to develop an adaptive evaluation framework. This framework can be used to assess the application of co-creation within social innovation in, for example, EU-funded projects. This could push the emerging academic field forward and open up new research themes and designs. We also suggest that the framework could specifically support policymakers in their efforts to evaluate processes of co-creation instead of focusing on the dominant impact evaluations. [ABSTRACT FROM AUTHOR]

We consider an experimental setting in which a matching of resources to participants has to be chosen repeatedly and returns from the individual chosen matches are unknown, but can be learned. Our setting covers two-sided and one-sided matching with (potentially complex) capacity constraints, such as refugee resettlement, social housing allocation, and foster care. We propose a variant of the Thompson sampling algorithm to solve such adaptive combinatorial allocation problems. We give a tight, prior-independent, finite-sample bound on the expected regret for this algorithm. Although the number of allocations grows exponentially in the number of matches, our bound does not. In simulations based on refugee resettlement data using a Bayesian hierarchical model, we find that the algorithm achieves half of the employment gains (relative to the status quo) that could be obtained in an optimal matching based on perfect knowledge of employment probabilities. [ABSTRACT FROM AUTHOR]

Environmental health literacy (EHL) is a rather recent concept that applies health literacy skills to environmental issues. Research in this field is still at the beginning, and there is currently no existing tool in the literature designed to comprehensively assess individual general EHL among university students. The aim of our study is to fill this gap through the validation of the Environmental Health Literacy Index (EHLI) in such a target group. We adapted a previously administered survey, originally completed by 4778 university students from various Italian universities. Starting from the original questionnaire, our methodology involved a three-round item selection process, followed by a comprehensive evaluation of the instrument's psychometric properties. The EHLI consists of 13 Likert-type items, covering three primary domains of health literacy: functional (six items), interactive (three items), and critical (four items). The Cronbach's alpha coefficient is 0.808 for the global scale, while it stands at 0.888 for the functional, 0.795 for the critical, and 0.471 for the interactive components. The area under the receiver operating characteristic curve reached a value of 0.643. Spearman correlation analysis revealed a significant yet slight correlation between EHLI and both functional health literacy score and the extent of pro-environmental behaviors adoption. Our study serves as an important initial step in developing a tool able to evaluate the EHL of university-aged individuals. Further research efforts may improve the questionnaire's validity and completeness, as well as to explore its applicability to different age groups. [ABSTRACT FROM AUTHOR]

This paper assesses the challenges and the benefits of 2 methodological approaches for improving the study of transnational mobilities of older adults: mixed methods and inclusiveness. The first approach refers to a mixed-methods research design based on surveys and qualitative interviews. We share our experience of conducting a "fully mixed-concurrent-equal-status-design" research project, for which we collected data through quantitative surveys on transnational practices of people aged 55+. Furthermore, we conducted semistructured interviews with adults aged 64+ living or who have lived in Switzerland and spend part of the year in Spain. The second approach relates to the use of an inclusive sample diversified in terms of nationality and migration backgrounds. The inclusive design affects the formulation of questions asked to all participants (and the response options provided in the survey), as well as decisions related to language choices and translations. These elements have to be considered to ensure that older adults from different backgrounds feel included. In a research project on transnational mobility of older adults, conducting an inclusive plus mixed-methods research project pushes researchers to find strategies to balance research objectives with available resources. Maximizing the research team's methodological background appears to be a suitable approach to address different population groups while working within a budget. [ABSTRACT FROM AUTHOR]

Background and Objectives This paper describes the development and evaluation of a short caregiving self-efficacy measure. The self-administered 8-item Caregiver Self-Efficacy Scale (CSES-8) was developed to reflect components of typical caregiver support interventions and to be practical for inclusion in future self-efficacy and caregiving research. Research Design and Methods We administered the CSES-8 in 2 samples: participants in an intervention for caregivers of persons with cognitive disabilities, and a voluntary online survey for caregivers of adults. We evaluated the completion rate, item-scale correlations, reliability, descriptive statistics, and preliminary construct validity of the CSES-8 in both samples, and sensitivity to change in the intervention sample. Results The intervention caregivers' sample (N = 158) was 85% female (mean age = 65 years). The online survey sample (N = 138) was 90% female (mean age = 78). In both samples, the CSES-8 had excellent internal consistency reliability (.89 and.88) and good distribution with sufficient variability to detect change. Test–retest reliability was good in the online sample (.73). As evidence of construct validity, most hypotheses were confirmed in both samples. The CSES-8 was sensitive to change at 6 months for caregivers in the intervention program (p <.001). Discussion and Implications The CSES-8 is short, comprehensive with respect to common components of interventions to improve caregivers' quality of life, and sensitive to change. It can serve a useful role exploring mechanisms by which caregiver intervention studies work, and it can be helpful in examining whether self-efficacy mediates the effect of these interventions on various outcomes such as psychological well-being. [ABSTRACT FROM AUTHOR]

Objective. The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. Methods. The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. Results. Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r =0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient=0.96), low random measurement error (percentage of minimal detectable change=15.6%), good group-level responsiveness (standardized response mean=0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. Conclusion. The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. Impact. This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records. [ABSTRACT FROM AUTHOR]

Background Accurate prognostic stratification of hepatocellular carcinoma (HCC) is vital for clinical trial enrollment and treatment allocation. Multiple scoring systems have been created to predict patient survival, but no standardized scoring systems account for radiologic tumor features. We sought to create a generalizable scoring system for HCC which incorporates standardized radiologic tumor features and more accurately predicts overall survival (OS) than established systems. Methods Clinicopathologic parameters were collected from a prospectively collected cohort of patients with HCC treated at a single institution. Imaging studies were evaluated for tumor characteristics. Patients were randomly divided into a training set for identification of covariates that impacted OS and a validation set. Cox models were used to determine the association of various factors with OS and a scoring system was created. Results We identified 383 patients with HCC with imaging and survival outcomes, n  = 255 in the training set and 128 in the validation cohort. Factors associated with OS on multivariate analysis included: tumor margin appearance on CT or MRI (hazard ratio [HR] 1.37, 95% CI, 1.01-1.88) with infiltrative margins portending worse outcomes than encapsulated margins, massive tumor morphology (HR 1.64, 95% CI, 1.06-2.54); >2 lesions (HR 2.06, 95% CI, 1.46-2.88), Child-Turcotte-Pugh class C (HR 3.7, 95% CI, 2.23-6.16), and portal vein thrombus (HR 2.41, 95% CI, 1.71-3.39). A new scoring system was developed and more predictive of OS than other well-established systems. Conclusions Incorporation of standardized imaging characteristics to established clinical and lab predictors of outcome resulted in an improved predictive scoring system for patients with HCC. [ABSTRACT FROM AUTHOR]

Deep sequencing-based bulked segregant analysis (BSA-seq) has become a popular approach for quantitative trait loci (QTL) mapping in recent years. Effective statistical methods for BSA-seq have been developed, but how to design a suitable experiment for BSA-seq remains unclear. In this paper, we show in theory how the major experimental factors (including population size, pool proportion, pool balance, and generation) and the intrinsic factors of a QTL (including heritability and degree of dominance) affect the power of QTL detection and the precision of QTL mapping in BSA-seq. Increasing population size can improve the power and precision, depending on the QTL heritability. The best proportion of each pool in the population is around 0.25. So, 0.25 is generally applicable in BSA-seq. Small pool proportion can greatly reduce the power and precision. Imbalance of pool pair in size also causes decrease of the power and precision. Additive effect is more important than dominance effect for QTL mapping. Increasing the generation of filial population produced by selfing can significantly increase the power and precision, especially from F2 to F3. These findings enable researchers to optimize the experimental design for BSAseq. A web-based program named BSA-seq Design Tool is available at http://124.71.74.135/BSA-seqDesignTool/and https://github.com/huanglikun/BSA-seqDesignTool. [ABSTRACT FROM AUTHOR]

Objective The purpose of this study was to create a Site of Excellence in Clinical Education (SECE-PT) tool with the essential criteria used to determine excellence in the provision of physical therapist clinical education at a clinical site using a consensus-building approach. Methods The development of the SECE-PT tool was divided into 2 parts. Part 1 involved the development of an initial set of proposed criteria, whereas part 2 employed a modified Delphi approach for consensus building. Purposive selection and snowball sampling techniques were used to recruit clinical instructors, recent graduates, site coordinators of clinical education, and directors of clinical education who met the inclusion criteria for the modified Delphi study. Three web-based survey rounds were used to achieve consensus, defined as a mean score of ≥7 on the 11-point Likert scale. The first round gathered demographic information on participants and collected information about clarity and redundancy in the criteria provided, the second gathered information once again about clarity and redundancy on the revised criteria provided, and the third asked participants to rate how essential it was for a site of excellence to demonstrate each of the final criteria. Results A total of 123 participants, equally representing clinical and academic perspectives, completed the demographic survey and round 1. Ninety-four participants completed round 2, and 80 participants completed the third and final round. Consensus revealed that 44 criteria were deemed essential for a SECE-PT to demonstrate. Conclusion This study provides a measure to evaluate clinical sites providing clinical education. The SECE-PT tool should be widely adopted to evaluate the quality of the clinical site providing the education to student physical therapists. Impact The SECE-PT tool can be used by clinical sites for self-assessment to examine aspects of their clinical education programs and determine whether parts of their program should be further developed. This can provide a framework for discussion and collaboration between clinical and academic partners, as well as regional consortia. [ABSTRACT FROM AUTHOR]

Objective The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings. Methods Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance. Results Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]). Conclusion Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance. Impact External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors. [ABSTRACT FROM AUTHOR]

Objective Mixed methods research (MMR) integrates quantitative and qualitative methods throughout the research process to answer complex research questions. MMR designs align with the guiding frameworks of patient-centered care and social determinants of health by effectively examining the role of contextual factors and human experiences in influencing health and rehabilitation outcomes. Reporting standards and critical appraisal tools ensure the quality and transparency of the research process. MMR standards exist; yet, there is a need for reporting guidelines and an appraisal tool that meets field standards, is applicable across rehabilitation fields of study, and can accommodate the range of possibilities for combining research approaches and methods. Methods Mixed Methods Reporting in Rehabilitation & Health Sciences (MMR-RHS) was developed using a systematic consensus-building process in accordance with published guidance and was preregistered with the Enhancing the Quality and Transparency of Health Research Network. MMR-RHS evolved through a sequence of steps, including extensive literature review, expert consultation, stakeholder feedback, pilot testing, and tool refinement. Results MMR-RHS consists of 20 criteria that align with field standards for rigor and transparency, with an emphasis on integration throughout the research process, a key component of MMR. Conclusions A systematic process was utilized to develop the reporting standards and an appraisal tool for MMR in rehabilitation and health science. The tool is comprehensive, includes a set of criteria grounded in MMR literature, and is flexible for application to a range of MMR designs commonly seen in rehabilitation research. Impact The MMR-RHS may improve the quality and transparency of MMR by supporting investigators, authors, reviewers, and editors during project development, manuscript preparation, and critical review. The tool may assist readers in critical appraisal, knowledge translation, and application of published MMR findings. Ultimately, the MMR-RHS may help legitimize mixed methods in rehabilitation and health research, an important step toward understanding the complexities of health care, patient outcomes, and evolving societal health needs. [ABSTRACT FROM AUTHOR]

Summary There are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. Availability and implementation LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. [ABSTRACT FROM AUTHOR]

We explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) risk , which entails inherent randomness within a given probability model; (2) model ambiguity , which entails uncertainty about the probability model to be used; and (3) model misspecification , which entails uncertainty about the presence of the correct probability model among the set of models considered. Using a new experimental design, we isolate and measure attitudes toward each layer separately. We conduct our experiment on three different subject pools and document, the existence of a behavioral distinction between the three layers. In addition to providing new insights into the underlying processes behind ambiguity aversion, we provide the first empirical evidence of the role of model misspecification in decision-making under uncertainty. [ABSTRACT FROM AUTHOR]

We design a laboratory experiment to identify whether a preference for randomisation defines a stable type across different choice environments. In games and individual decisions, subjects face 20 simultaneous repetitions of the same choice. Subjects can randomise by making different choices across the repetitions. We find that randomisation does define a type that is predictable across domains. A sizeable fraction of individuals randomise in all domains, even in questions that offer a stochastically dominant option. For some mixers, dominated randomisation is responsive to intervention. We explore theoretical foundations for mixing, and find that most preference-based models are unable to accommodate our results. [ABSTRACT FROM AUTHOR]

The offset agreement (OA) of the Brazilian Air Force (FAB) is a demand-side policy aiming to improve innovative capacities. However, it may be constrained by companies' absorptive capacities, relationship features, and sectoral specificities. This study examines these issues in a small but representative sample of domestic beneficiary firms from the FAB's OA. The model in the study by Zahra and George, along with contingent factors such as power relationship and social integration mechanisms, inspired the design of the research tools. The results point to similar and high firms' absorptive capacities, regardless of the origins of capital. However, results show distinct benefit perceptions given by contingent factors, companies' strategies, and sectoral specificities, which may denote distinct possibilities on cumulativeness and appropriability from technology transfer. We expect that these findings may improve the planning, monitoring, evaluation, and adjustments of this demand-side policy, attracting attention to new studies on the theme. [ABSTRACT FROM AUTHOR]