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The last decade has seen the proliferation of meta-analyses dedicated to perfectionism. Due to the volume of meta-analyses available, some stocktaking is now needed to catalogue existing meta-analytical research, assess the qualities of the work, and direct future research. To fulfil these aims, we conducted the first umbrella review of research examining the correlates of perfectionism. Following a preregistered protocol, a systematic search provided 43 meta-analyses (79 criterion variables, 379 effects, k = 3,992, N = 694,422). The meta-analyses examined a range of criterion variables covering, primarily, mental health and well-being but also included motivation and performance both across and within cross-specific domains (e.g., education, workplace, and sport). Perfectionistic concerns were consistently related to mental ill-health and ill-being. Perfectionistic strivings displayed a similar pattern of relationships but were smaller in size. As a result, overall, perfectionism was also related to mental ill-health and ill-being. The typical risk of bias evident in the meta-analyses was assessed as high with consistent areas of weakness relating to the absence of unpublished research and lack of assessment of methodological quality of primary studies. Some degree of confidence in the findings of the affected research is diminished in these regards. In addition to addressing these issues in future work, to strengthen current evidence, researchers are encouraged to address more complex questions by applying meta-analytic techniques more routinely to the prediction of change over time, incremental predictive ability, and tests of explanatory models. (PsycInfo Database Record (c) 2025 APA, all rights reserved) (Source: journal abstract)

Suppressor variables increase the predictive power of one or more predictors by suppressing irrelevant variance. Although theoretically and statistically useful, no research has addressed the frequency or interpretation of statistical suppression (SS) in the psychological literature. In two studies, we explored the nature and interpretation of SS. In the first study, we reviewed regression analyses to determine the frequency with which SS occurs in psychological articles published in 2017. Approximately one-third of articles showed evidence of SS, although researchers almost never acknowledged or attempted to interpret the SS. In the second study, we reviewed articles containing the keyword \"suppression\" to assess the interpretations provided by researchers that identified SS. Results indicate that most researchers do not attempt to classify or interpret SS. Therefore, although SS is common in psychology, scarcely any attempts are made to identify, classify, and/or interpret it. Les variables suppressives accroissent l'efficacité prédictive d'un ou plusieurs « prédicteurs » en supprimant la variance non pertinente. Bien que la suppression statistique (SS) soit utile sur les plans théorique et statistique, aucune recherche ne s'est penchée sur la fréquence ou l'interprétation de la SS dans les ouvrages psychologiques. Deux de nos études se sont intéressées à la nature et à l'interprétation de la SS. Dans la première, nous avons passé en revue des analyses de régression afin de déterminer la fréquence à laquelle la SS apparaît dans des articles de psychologie publiés en 2017. Environ le tiers de ces articles mettaient en évidence la SS, sans que les chercheurs la reconnaissent ou essaient de l'interpréter (ou presque jamais). Dans la deuxième étude, nous avons passé en revue des articles contenant le mot clé « suppression » afin d'évaluer les interprétations fournies par les chercheurs ayant identifié la SS. Les résultats montrent que la plupart des chercheurs n'essaient pas de préciser ou d'interpréter la SS. Par conséquent, bien que la SS soit un concept courant en psychologie, bien peu d'efforts sont déployés pour l'identifier, la catégoriser et (ou) l'interpréter. Public Significance Statement When researchers explore relationships among variables, they often include multiple variables that might have an impact on the outcome variable (i.e., predictor variables). In most instances, including extra predictor variables reduces the magnitude of the relationship between a specific predictor and the outcome, since shared variables are removed. However, when statistical suppression is present, including an extra predictor variable increases the magnitude of the relationship between a specific predictor variable and the outcome. In this article, we highlight the value of identifying and interpreting statistical suppression relationships.

Attentional and executive functions deficits are associated with combined type ADHD. Standardized tests have been developed to measure them. This study aims to document the predictive value of tests for the diagnosis of ADHD. This is a retrospective study with 125 youth aged 8 to 15 (M = 10.39, 30 girls and 95 boys) who seek help for adjustment problems. After their assessment, participants are divided into two groups, the ADHD group (n = 68) or the comparison group (n = 57), where ADHD is not suspected. The results show that three tests (1-cognitive inhibition, 2-inhibition of a motor response, 3-sustained attention) are most accurate in predicting an ADHD diagnosis. Discriminant analysis is meaningful and accounts for 57% of the variance. The results indicate that 79.3% of the participants were correctly classified, 80% in the ADHD group (sensitivity) and 78.4% in the comparison group (specificity). Therefore these tests are useful for predicting ADHD. These findings can be used by professionals to select tests for assessing ADHD. However, further studies should verify whether these tests contribute to the accuracy of diagnosis when using a multimodal approach that includes behavioral observation. (PsycInfo Database Record (c) 2025 APA, all rights reserved) (Source: journal abstract)

This study explored the validity of the PCL/PCL-R factor scores in predicting institutional adjustment and recidivism in forensic clients and prison inmates. Forty-two studies in which institutional adjustment, release outcome (recidivism), or both were assessed prospectively with the PCL/PCL-R yielded 50 effect size estimates between the PCL/PCL-R factor scores and measures of institutional adjustment/recidivism. A meta-analysis of these findings disclosed that Factor 2 (Antisocial/Unstable Lifestyle) correlated moderately well with institutional adjustment and recidivism, whereas Factor 1 (Affective/Interpersonal Traits) was less robustly associated with these outcomes. Direct comparisons of the mean effect sizes attained by Factors 1 and 2 revealed that Factor 2 was significantly more predictive of total outcomes, general recidivism, violent recidivism, and outcomes from the 12 most methodological sound studies than Factor 1. There was less differentiation between Factors 1 and 2 on measures of institutional adjustment.