Multilevel Semiparametric Latent Variable Modeling in R with "galamm".

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a sine qua non condition for the expansion of Brazilian eucalyptus silviculture. This study's objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone's reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone's reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.

Mechanism of key volatiles in osmanthus scented green tea on the improvement of its aroma and taste quality.

The intensity of green tea's floral and sweet flavors was enhanced after being scented by osmanthus (OSGT). However, the mechanism of flavor enhancement by key volatiles remains unknown. Here, the role of key volatiles in OSGT on aroma and taste was explored by sensory experiment-guided flavor analysis. Binary mixed models of (E)-ß-ionone, dihydro-ß-ionone, and α-ionone showed additive interactions on floral aroma enhancement, the interactions were increased with increasing concentrations. At the concentration in OSGT, binary mixed models of (E)-ß-ionone, geraniol, linalool, and γ-decalactone showed additive interactions on sweet aroma enhancement. (E)-ß-ionone, geraniol, linalool, and γ-decalactone all significantly increased the perceived intensity of sweetness of sucrose solutions. Additionally, molecular docking revealed the perception mechanism of olfactory and taste receptors to the above characterized volatiles, with hydrogen bonding and hydrophobic interactions being the main interactions. This study highlights the importance of characteristic volatiles in enhancing the flavor of OSGT.

Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores.

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

Understanding the Genetic Basis of Blueberry Postharvest Traits to Define Better Breeding Strategies.

Blueberry (Vaccinium spp.) is among the most-consumed soft fruit and has been recognized as an important source of health-promoting compounds. Highly perishable and susceptible to rapid spoilage due to fruit softening and decay during postharvest storage, modern breeding programs are looking to maximize quality and extend the market life of fresh blueberries. However, it is uncertain how genetically controlled postharvest quality traits are in blueberries. This study aimed to investigate the prediction ability and genetic basis of the main fruit quality traits affected during blueberry postharvest to create breeding strategies for developing cultivars with an extended shelf life. To achieve this goal, we carried out target genotyping in a breeding population of 588 individuals and evaluated for several fruit quality traits after one day, one week, three weeks, and seven weeks of postharvest storage at 1 °C. Using longitudinal genome-based methods, we estimated genetic parameters and predicted unobserved phenotypes. Our results showed large diversity, moderate heritability, and consistent predictive accuracies along the postharvest storage for most of the traits. Regarding fruit quality, firmness showed the largest variation during postharvest storage, with a surprising number of genotypes maintaining or increasing their firmness even after seven weeks of cold storage. Our results suggest that we can effectively improve blueberry postharvest quality through breeding and use genomic prediction to maximize the genetic gains in the long term. We also emphasize the potential of using longitudinal genomic prediction models to predict fruit quality at extended postharvest periods by integrating known phenotypic data from harvest.

The Benefits of Permutation-Based Genome-Wide Association Studies.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we will discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis thaliana phenotypes from the AraPheno database.

DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.

Biomedical research now commonly integrates diverse data types or views from the same individuals to better understand the pathobiology of complex diseases, but the challenge lies in meaningfully integrating these diverse views. Existing methods often require the same type of data from all views (cross-sectional data only or longitudinal data only) or do not consider any class outcome in the integration method, which presents limitations. To overcome these limitations, we have developed a pipeline that harnesses the power of statistical and deep learning methods to integrate cross-sectional and longitudinal data from multiple sources. In addition, it identifies key variables that contribute to the association between views and the separation between classes, providing deeper biological insights. This pipeline includes variable selection/ranking using linear and nonlinear methods, feature extraction using functional principal component analysis and Euler characteristics, and joint integration and classification using dense feed-forward networks for cross-sectional data and recurrent neural networks for longitudinal data. We applied this pipeline to cross-sectional and longitudinal multiomics data (metagenomics, transcriptomics and metabolomics) from an inflammatory bowel disease (IBD) study and identified microbial pathways, metabolites and genes that discriminate by IBD status, providing information on the etiology of IBD. We conducted simulations to compare the two feature extraction methods.

Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population.

Early or late pubertal onset can lead to disease in adulthood, including cancer, obesity, type 2 diabetes, metabolic disorders, bone fractures, and psychopathologies. Thus, knowing the age at which puberty is attained is crucial as it can serve as a risk factor for future diseases. Pubertal development is divided into five stages of sexual maturation in boys and girls according to the standardized Tanner scale. We performed genome-wide association studies (GWAS) on the "Growth and Obesity Chilean Cohort Study" cohort composed of admixed children with mainly European and Native American ancestry. Using joint models that integrate time-to-event data with longitudinal trajectories of body mass index (BMI), we identified genetic variants associated with phenotypic transitions between pairs of Tanner stages. We identified $42$ novel significant associations, most of them in boys. The GWAS on Tanner $3\rightarrow 4$ transition in boys captured an association peak around the growth-related genes LARS2 and LIMD1 genes, the former of which causes ovarian dysfunction when mutated. The associated variants are expression and splicing Quantitative Trait Loci regulating gene expression and alternative splicing in multiple tissues. Further, higher individual Native American genetic ancestry proportions predicted a significantly earlier puberty onset in boys but not in girls. Finally, the joint models identified a longitudinal BMI parameter significantly associated with several Tanner stages' transitions, confirming the association of BMI with pubertal timing.

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials.

INTRODUCTION: Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods. METHODS: We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial. RESULTS: Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm. DISCUSSION: Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods. HIGHLIGHTS: We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.

Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data.

A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data.

Beyond fight or flight: The protective role of pre-pandemic meditation practice against anxiety and perceived stress.

The COVID-19 pandemic generated distinct mental health challenges, characterised by stress and anxiety due to its unpredictable duration and continuous threat. This study examined the role of meditation practice on anxiety symptoms and perceived stress, considering co-variables such as self-compassion, acceptance, awareness, brooding, lockdown duration, and sociodemographic characteristics. The study used a longitudinal design and data were collected through online surveys from April 2020 to January 2021 (at four different time points) and included 238 participants from Portugal (165 had prior experience with meditation practices, 73 were non-meditators) with a mean age of 43.08 years (SD = 10.96). Linear mixed models revealed that over time, during the lockdown, the non-meditators group demonstrated a greater increase of anxiety symptoms (ß = -0.226, SE = 0.06, p = 0.006) and perceived stress (ß = -0.20, SE = 0.06, p = 0.004), whereas the meditators group showed non-significant (p > 0.05) variations in anxiety and stress symptoms during the same period of time. The effect of meditation on anxiety symptoms was moderated by sex, days of lockdown, self-compassion, and acceptance. The effect of meditation on perceived stress was moderated by sex, years of education, days of lockdown, and levels of awareness. Additionally, the study explored the potential predictive effect of different meditation session lengths, indicating that longer meditation practices offered greater protection against an increase in anxiety symptoms. These findings highlight the importance of cultivating self-regulation skills and investing in preventive mental health strategies to promote well-being and autonomy. Mental health professionals should prioritise educating communities on evidence-based practices like meditation and compassion exercises to enhance overall health.

A host of issues: pseudoreplication in host-microbiota studies.

Pseudoreplication compromises the validity of research by treating non-independent samples as independent replicates. This review examines the prevalence of pseudoreplication in host-microbiota studies, highlighting the critical need for rigorous experimental design and appropriate statistical analysis. We systematically reviewed 115 manuscripts on host-microbiota interactions. Our analysis revealed that 22% of the papers contained pseudoreplication, primarily due to co-housed organisms, whereas 52% lacked sufficient methodological details. The remaining 26% adequately addressed pseudoreplication through proper experimental design or statistical analysis. The high incidence of pseudoreplication and insufficient information underscores the importance of methodological reporting and statistical rigor to ensure reproducibility of host-microbiota research.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Ice phenology interactions with water and air temperatures in high mountain lakes.

Ice phenology is of great importance for the thermal structure of lakes and ponds and the biology of lake species. Under the current climate change conditions, ice-cover duration has been reduced by an advance in ice-off, and a delay in ice-on, and future projections foresee this trend as continuing. Here, we describe the current ice phenology of Pyrenean high mountain lakes and ponds, including ice-cover duration and ice-on and ice-off dates. We used mixed models to identify the variables that explained the observed patterns, extrapolated them across all water bodies in the mountain range, and related the seasonality of air and water temperatures with ice phenology using structural equation models. Ice phenology was obtained from the temperature series of 85 lakes and ponds for fourteen years, including 2001 to 2004 and 2009 to 2019. We discovered that high autumn precipitation was related to earlier ice-on dates, and that earlier ice-off dates were associated with higher following-summer water temperatures. We found a greater predictability of ice-off dates and ice-cover duration than ice-on dates. Altitude was the most important variable explaining the variation in ice phenology, followed by latitude, which was related to climatic differences among the northern and southern slopes of the mountain range. The lake area was significant for ice-on dates and ice-cover duration. The interannual variability in air temperature and radiation was remarkable for the ice-off date and ice-cover duration but not for the ice-on date. In contrast, wind speed was related to an earlier ice-off date and shorter ice-cover duration. All the measured lakes and ponds froze in winter during the studied period, a feature maintained in the extrapolation to the whole set of water bodies.

The perspective of the logarithmic field sprayer technology.

A logarithmic sprayer was suggested about 70 years ago, but it has not yet been seriously used in research and development, and subsequent registration of plant protection products. Logarithmic sprayers have resorted to mere demonstration experiments to show end users and others how plant protection products work. Fitting dose-response curves in field experiments, however, generates much essential information, e.g., extraction of various effective field rate levels (e.g., ED20, ED50, and ED80). One of the reasons for it rarely being used in the registration of plant protection products is that the dose-response curve regression was hitherto difficult to fit; the registration requirement solely focuses on analyses of variance. Another alleged obstacle is that the logarithmic plots have systematically, not randomly distributed field rates. This paper goes through some of the problems of how to non-randomly analyze field rates by taking autocorrelation into account to make the logarithmic sprayer palatable as registration documentation by assessing efficacy, selectivity, environmental side effects, general toxicity of plant protection products, and cost-effectiveness. The development in precision agriculture, drone technology, and automation of data capture and subsequent analysis could make the logarithmic sprayer a cost-effective alternative to numerous ANOVA experiments with very few fixed field rates to aid the precision spraying of pesticides and thus reduce unnecessary environmental side effects. © 2024 Society of Chemical Industry.

Linear modeling of zonal level crop production in Ethiopia.

Accounting for zonal-level variations and identifying factors that have linear effects on crop production help to make better decisions and plan new policies for effective crop production and food security. The main objective of this study is to identify potential subsets of covariates and estimate their linear effects on crop production. A linear mixed effects model (random--intercept) is used on agricultural sample survey data for Meher seasons from 2012/13 to 2019/20 to explore and identify the best variance-covariance structure for the longitudinal data on 90 zones with eight repeated observations and different sampling weights. The minimum, mean, and maximum crop production by farmers across the country are 1.616, 8.693, and 147.843 quintals, respectively, and about 98 % of farmers produced less than 25 quintals. There is a small rate of increase in mean and median crop production by farmers across the years, and the variability between zones is highest in the year 2019/20 and in the Somali region. The histogram, kernel density, and P-P plots suggested a common logarithm transformation on the crop production variable. Results from the data exploration and variance-covariance structure selection methods suggested a heterogeneous compound symmetry (CSH) structure. Covariates region, year, proportion of farmers who practice pure-agriculture and other-agriculture types, proportion of farmers who use any type of fertilizer, farmer's age, area used, farmer association crop production, indigenous seed used, improved seed used, UREA fertilizer used, other fertilizers used, and percentage of crop damaged are significant in linearly explaining/affecting log crop production, and among these area used, farmers association crop production, UREA fertilizer used, and indigenous seed used have relatively highest effect on log crop production. Zones Wolayita, North-Shewa (Am), West-Arsi, West-Welega, Dawro, and Guji are top/good performers while zones Southwest-Shewa, Waghimra, Guraghe, South-Omo, Keffa, North-Wello, South-Wello, and Eastern Tigray are bottom/poor performers in crop production. Model assumptions and influence diagnostics results suggested the linearity of the model and normality of random effects and residuals are not violated, even though some zones have influences on either model parameters, precisions of estimates of these parameters, and predicted values.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

BACKGROUND: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs. METHODS: We compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed. RESULTS: For most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate. CONCLUSIONS: We recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

The effectiveness of an online short-format Recovery College model: a co-learning model to support mental health.

BACKGROUND: Our societies are facing mental health challenges, which have been compounded by the Covid-19. This event led people to isolate themselves and to stop seeking the help they needed. In response to this situation, the Health and Recovery Learning Center, applying the Recovery College (RC) model, modified its training program to a shorter online format. This study examines the effectiveness of a single RC training course delivered in a shortened online format to a diverse population at risk of mental health deterioration in the context of Covid-19. METHODS: This quasi-experimental study used a one-group pretest-posttest design with repeated measures. Three hundred and fifteen (n = 315) learners agreed to take part in the study and completed questionnaires on wellbeing, anxiety, resilience, self-management, empowerment and stigmatizing attitudes and behaviors. RESULTS: Analyses of variance using a linear mixed models revealed that attending a RC training course had, over time, a statistically significant effect on wellbeing (p = 0.004), anxiety (p < 0.001), self-esteem/self-efficacy (p = 0.005), disclosure/help-seeking (p < 0.001) and a slight effect on resilience (p = 0.019) and optimism/control over the future (p = 0.01). CONCLUSIONS: This study is the first to measure participation in a single online short-format RC training course, with a diversity of learners and a large sample. These results support the hypothesis that an online short-format training course can reduce psychological distress and increase self-efficacy and help-seeking. TRIAL REGISTRATION: This study was previously approved by two certified ethics committees: Comité d'éthique de la recherche du CIUSSS EMTL, which acted as the committee responsible for the multicenter study, reference number MP-12-2021-2421, and Comité d'éthique avec les êtres humains de l'UQTR, reference number CER-20-270-07.01.