Modèle de mélange de lois multinormales appliqué à l'analyse de comportements et d'habiletés cognitives d'enfants

Cette étude aborde le thème de l’utilisation des modèles de mélange de lois pour analyser des données de comportements et d’habiletés cognitives mesurées à plusieurs moments au cours du développement des enfants. L’estimation des mélanges de lois multinormales en utilisant l’algorithme EM est expliquée en détail. Cet algorithme simplifie beaucoup les calculs, car il permet d’estimer les paramètres de chaque groupe séparément, permettant ainsi de modéliser plus facilement la covariance des observations à travers le temps. Ce dernier point est souvent mis de côté dans les analyses de mélanges. Cette étude porte sur les conséquences d’une mauvaise spécification de la covariance sur l’estimation du nombre de groupes formant un mélange. La conséquence principale est la surestimation du nombre de groupes, c’est-à-dire qu’on estime des groupes qui n’existent pas. En particulier, l’hypothèse d’indépendance des observations à travers le temps lorsque ces dernières étaient corrélées résultait en l’estimation de plusieurs groupes qui n’existaient pas. Cette surestimation du nombre de groupes entraîne aussi une surparamétrisation, c’est-à-dire qu’on utilise plus de paramètres qu’il n’est nécessaire pour modéliser les données. Finalement, des modèles de mélanges ont été estimés sur des données de comportements et d’habiletés cognitives. Nous avons estimé les mélanges en supposant d’abord une structure de covariance puis l’indépendance. On se rend compte que dans la plupart des cas l’ajout d’une structure de covariance a pour conséquence d’estimer moins de groupes et les résultats sont plus simples et plus clairs à interpréter.This study is about the use of mixture to model behavioral and cognitive data measured repeatedly across development in children. Estimation of multinormal mixture models using the EM algorithm is explained in detail. This algorithm simplifies computation of mixture models because the parameters in each group are estimated separately, allowing to model covariance across time more easily. This last point is often disregarded when estimating mixture models. This study focused on the consequences of a misspecified covariance matrix when estimating the number of groups in a mixture. The main consequence is an overestimation of the number of groups, i.e. we estimate groups that do not exist. In particular, the independence assumption of the observations across time when they were in fact correlated resulted in estimating many non existing groups. This overestimation of the number of groups also resulted in an overfit of the model, i.e. we used more parameters than necessary. Finally mixture models were fitted to behavioral and cognitive data. We fitted the data first assuming a covariance structure, then assuming independence. In most cases, the analyses conducted assuming a covariance structure ended up having fewer groups and the results were simpler and clearer to interpret

Validation de la version française canadienne du Perception of Prevalence of Aggression Scale auprès d’un échantillon d’intervenants en protection de la jeunesse

Objectif. L’objectif de cette étude est d’évaluer la validité de construit de la version française de l’échelle Perception Of Prevalence of Aggression Scale (POPAS), un questionnaire auto-rapporté mesurant l’exposition à la violence au travail commise par la clientèle du milieu de la santé et des services sociaux. Méthode. Un échantillon de 310 intervenants en protection de la jeunesse est utilisé afin de confirmer la structure interne à quatre facteurs de l’instrument. À défaut de confirmer cette structure, un modèle d’équation structurelle exploratoire est utilisé. Les facteurs retenus sont soumis aux tests d’alpha de Cronbach qui permettent d’évaluer leur cohérence interne. Ils sont corrélés avec la version française du Posttraumatic Stress Disorder Checklist Scale (PCLS) et du nombre de jours d’absence du travail causé par la violence afin d’évaluer la validité convergente. Il sont également corrélées avec le Felt Accountability (FA) afin d’évaluer la validité divergente. Des analyses de comparaison en fonction du lieu de travail permettent d’explorer la validité de critère. Résultats. L’analyse factorielle confirmatoire ne confirme pas la structure à quatre facteurs du POPAS. L’équation structurelle exploratoire valide une structure à trois facteurs : « violence verbale », « violence physique » et « violence envers soi-même ». Les deux premiers possèdent une bonne cohérence interne. Les corrélations positives entre ces deux facteurs et le PCLS, ainsi qu’entre ces deux facteurs et le nombre de jours d’absence appuient la validité convergente du POPAS. Toutefois, l’absence de corrélation significative entre le dernier facteur et le PCLS, et entre ce facteur et le nombre de jour d’absence n’appuient pas cette convergence. L’absence de corrélation des facteurs avec le FA appuie la validité divergente du POPAS. Les différences observées selon les environnements de travail attestent aussi de la validité de critère. Discussion. La validité de construit de la version française canadienne du POPAS suggère que l’outil permet d’évaluer la fréquence subjective de différentes formes de violence au travail vécues par les intervenants en protection de la jeunesse. Il offre ainsi une alternative aux données officielles (c.-à-d. déclaration d’incidents à l’employeur) qui reflètent peu la réalité de ces travailleurs compte tenu de la sous-déclaration des incidents de violence dans ce milieuObjective. The objective of this study is to evaluate the construct validity of the French Canadian version of the Perception Of Prevalence of Aggression Scale (POPAS), a self-report questionnaire measuring exposure to workplace violence committed by clients in the health and social services sector. Method. A sample of 310 youth protection workers is utilized to confirm the four-factor internal structure of the instrument. If this structure is not confirmed, an exploratory structural equation model is used. The selected factors undergo Cronbach alpha tests that assess their internal consistency. They are correlated with the French version of the Posttraumatic Stress Disorder Checklist Scale (PCLS) and the number of absentee days caused by violence in order to measure convergent validity. There are also correlated with the Felt Accountability (FA) scale to assess divergent validity. Comparison analyses according to work environments assess criterion validity. Results. The confirmatory factor analysis does not corroborate the four-factor structure of the POPAS. The exploratory structural equation model validates a three-factor structure: ‘‘verbal violence’’, ‘‘physical violence’’ and ‘‘violence against oneself’’. The first two possess good internal consistency. The positive correlations between these two factors and the PCLS, as well as between these two factors and the number of absentee days, support the convergent validity of POPAS. However, the absence of a significant correlation between the last factor and the PCLS, as well as between this factor and the number of absentee days, does not support convergence. The lack of correlation between the factors and the FA supports the divergent validity of the POPAS. The differences observed as they relate to work environments also attest to criterion validity. Discussion. The construct validity of the French Canadian version of the POPAS suggests that this instrument allows for an evaluation of the subjective frequency of different forms of workplace violence experienced by youth protection workers. It therefore represents an alternative to the use of official data (i.e. incident reports made to the employer), which poorly reflect the reality of these workers given the underreporting of violent incidents in this secto

RetinaVR: Democratizing Vitreoretinal Surgery Training with a Portable and Affordable Virtual Reality Simulator in the Metaverse

We developed and validated RetinaVR, an affordable and immersive virtual reality simulator for vitreoretinal surgery training, using the Meta Quest 2 VR headset. We focused on four core fundamental skills: core vitrectomy, peripheral shaving, membrane peeling, and endolaser application. The validation study involved 10 novice ophthalmology residents and 10 expert vitreoretinal surgeons. We demonstrated construct validity, as shown by the varying user performance in a way that correlates with experimental runs, age, sex, and expertise. RetinaVR shows promise as a portable and affordable simulator, with potential to democratize surgical simulation access, especially in developing countries

StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables

StepMix is an open-source software package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with BCH and ML corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides interfaces in both Python and R.Comment: Sacha Morin and Robin Legault contributed equall

Capabilities of GPT-4 in ophthalmology: an analysis of model entropy and progress towards human-level medical question answering

Background: Evidence on the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model (LLM), in the ophthalmology question-answering domain is needed. // Methods: We tested GPT-4 on two 260-question multiple choice question sets from the Basic and Clinical Science Course (BCSC) Self-Assessment Program and the OphthoQuestions question banks. We compared the accuracy of GPT-4 models with varying temperatures (creativity setting) and evaluated their responses in a subset of questions. We also compared the best-performing GPT-4 model to GPT-3.5 and to historical human performance. // Results: GPT-4–0.3 (GPT-4 with a temperature of 0.3) achieved the highest accuracy among GPT-4 models, with 75.8% on the BCSC set and 70.0% on the OphthoQuestions set. The combined accuracy was 72.9%, which represents an 18.3% raw improvement in accuracy compared with GPT-3.5 (p<0.001). Human graders preferred responses from models with a temperature higher than 0 (more creative). Exam section, question difficulty and cognitive level were all predictive of GPT-4-0.3 answer accuracy. GPT-4-0.3’s performance was numerically superior to human performance on the BCSC (75.8% vs 73.3%) and OphthoQuestions (70.0% vs 63.0%), but the difference was not statistically significant (p=0.55 and p=0.09). // Conclusion: GPT-4, an LLM trained on non-ophthalmology-specific data, performs significantly better than its predecessor on simulated ophthalmology board-style exams. Remarkably, its performance tended to be superior to historical human performance, but that difference was not statistically significant in our study