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  • 1
    Online-Ressource
    Online-Ressource
    American Psychological Association (APA) ; 2023
    In:  Psychological Methods ( 2023-08-10)
    In: Psychological Methods, American Psychological Association (APA), ( 2023-08-10)
    Materialart: Online-Ressource
    ISSN: 1939-1463 , 1082-989X
    Sprache: Englisch
    Verlag: American Psychological Association (APA)
    Publikationsdatum: 2023
    ZDB Id: 2103345-6
    SSG: 5,2
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 2
    In: European Psychiatry, Royal College of Psychiatrists, Vol. 66, No. 1 ( 2023)
    Kurzfassung: If people with episodic mental-health conditions lose their job due to an episode of their mental illness, they often experience personal negative consequences. Therefore, reintegration after sick leave is critical to avoid unfavorable courses of disease, longer inability to work, long payment of sickness benefits, and unemployment. Existing return-to-work (RTW) programs have mainly focused on “common mental disorders” and often used very elaborate and costly interventions without yielding convincing effects. It was the aim of the RETURN study to evaluate an easy-to-implement RTW intervention specifically addressing persons with mental illnesses being so severe that they require inpatient treatment. Methods The RETURN study was a multi-center, cluster-randomized controlled trial in acute psychiatric wards addressing inpatients suffering from a psychiatric disorder. In intervention wards, case managers (RTW experts) were introduced who supported patients in their RTW process, while in control wards treatment, as usual, was continued. Results A total of 268 patients were recruited for the trial. Patients in the intervention group had more often returned to their workplace at 6 and 12 months, which was also mirrored in more days at work. These group differences were statistically significant at 6 months. However, for the main outcome (days at work at 12 months), differences were no longer statistically significant ( p  = 0.14). Intervention patients returned to their workplace earlier than patients in the control group ( p  = 0.040). Conclusions The RETURN intervention has shown the potential of case-management interventions when addressing RTW. Further analyses, especially the qualitative ones, may help to better understand limitations and potential areas for improvement.
    Materialart: Online-Ressource
    ISSN: 0924-9338 , 1778-3585
    RVK:
    Sprache: Englisch
    Verlag: Royal College of Psychiatrists
    Publikationsdatum: 2023
    ZDB Id: 2005377-0
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 3
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    SAGE Publications ; 2020
    In:  Educational and Psychological Measurement Vol. 80, No. 4 ( 2020-08), p. 756-774
    In: Educational and Psychological Measurement, SAGE Publications, Vol. 80, No. 4 ( 2020-08), p. 756-774
    Kurzfassung: Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis—and especially the process of factor retention. Determining the correct number of factors is crucial for the analysis, yet little is known about how to deal with missingness in this process. Therefore, in a simulation study, six missing data methods (an expectation–maximization algorithm, predictive mean matching, Bayesian regression, random forest imputation, complete case analysis, and pairwise complete observations) were compared with respect to the accuracy of the parallel analysis chosen as retention criterion. Data were simulated for correlated and uncorrelated factor structures with two, four, or six factors; 12, 24, or 48 variables; 250, 500, or 1,000 observations and three different missing data mechanisms. Two different procedures combining multiply imputed data sets were tested. The results showed that no missing data method was always superior, yet random forest imputation performed best for the majority of conditions—in particular when parallel analysis was applied to the averaged correlation matrix rather than to each imputed data set separately. Complete case analysis and pairwise complete observations were often inferior to multiple imputation.
    Materialart: Online-Ressource
    ISSN: 0013-1644 , 1552-3888
    RVK:
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2020
    ZDB Id: 1500101-5
    ZDB Id: 206630-0
    SSG: 5,2
    SSG: 5,3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 4
    In: Journal of the American Society of Nephrology, Ovid Technologies (Wolters Kluwer Health), Vol. 34, No. 7 ( 2023-7), p. 1191-1206
    Kurzfassung: Endocytosis, recycling, and degradation of proteins are essential functions of mammalian cells, especially for terminally differentiated cells with limited regeneration rates and complex morphology, such as podocytes. To improve our understanding on how disturbances of these trafficking pathways are linked to podocyte depletion and slit diaphragm (SD) injury, the authors explored the role of the small GTPase Rab7, which is linked to endosomal, lysosomal, and autophagic pathways, using as model systems mice and Drosophila with podocyte-specific or nephrocyte-specific loss of Rab7, and a human podocyte cell line depleted for Rab7. Their findings point to maturation and fusion events during endolysosomal and autophagic maturation as key processes for podocyte homeostasis and function and identify altered lysosomal pH values as a putative novel mechanism for podocytopathies. Background Endocytosis, recycling, and degradation of proteins are essential functions of mammalian cells, especially for terminally differentiated cells with limited regeneration rates, such as podocytes. How disturbances within these trafficking pathways may act as factors in proteinuric glomerular diseases is poorly understood. Methods To explore how disturbances in trafficking pathways may act as factors in proteinuric glomerular diseases, we focused on Rab7, a highly conserved GTPase that controls the homeostasis of late endolysosomal and autophagic processes. We generated mouse and Drosophila in vivo models lacking Rab7 exclusively in podocytes or nephrocytes, and performed histologic and ultrastructural analyses. To further investigate Rab7 function on lysosomal and autophagic structures, we used immortalized human cell lines depleted for Rab7. Results Depletion of Rab7 in mice, Drosophila , and immortalized human cell lines resulted in an accumulation of diverse vesicular structures resembling multivesicular bodies, autophagosomes, and autoendolysosomes. Mice lacking Rab7 developed a severe and lethal renal phenotype with early-onset proteinuria and global or focal segmental glomerulosclerosis, accompanied by an altered distribution of slit diaphragm proteins. Remarkably, structures resembling multivesicular bodies began forming within 2 weeks after birth, prior to the glomerular injuries. In Drosophila nephrocytes, Rab7 knockdown resulted in the accumulation of vesicles and reduced slit diaphragms. In vitro , Rab7 knockout led to similar enlarged vesicles and altered lysosomal pH values, accompanied by an accumulation of lysosomal marker proteins. Conclusions Disruption within the final common pathway of endocytic and autophagic processes may be a novel and insufficiently understood mechanism regulating podocyte health and disease.
    Materialart: Online-Ressource
    ISSN: 1046-6673 , 1533-3450
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2023
    ZDB Id: 2029124-3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 5
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    Online-Ressource
    Informa UK Limited ; 2023
    In:  Structural Equation Modeling: A Multidisciplinary Journal
    In: Structural Equation Modeling: A Multidisciplinary Journal, Informa UK Limited
    Materialart: Online-Ressource
    ISSN: 1070-5511 , 1532-8007
    Sprache: Englisch
    Verlag: Informa UK Limited
    Publikationsdatum: 2023
    ZDB Id: 2021254-9
    SSG: 3,4
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 6
    Online-Ressource
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    SAGE Publications ; 2022
    In:  Applied Psychological Measurement Vol. 46, No. 5 ( 2022-07), p. 406-421
    In: Applied Psychological Measurement, SAGE Publications, Vol. 46, No. 5 ( 2022-07), p. 406-421
    Kurzfassung: Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.
    Materialart: Online-Ressource
    ISSN: 0146-6216 , 1552-3497
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2022
    ZDB Id: 224215-1
    ZDB Id: 2002941-X
    SSG: 5,2
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 7
    Online-Ressource
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    Hogrefe Publishing Group ; 2022
    In:  Journal of Personnel Psychology Vol. 21, No. 1 ( 2022-01), p. 37-47
    In: Journal of Personnel Psychology, Hogrefe Publishing Group, Vol. 21, No. 1 ( 2022-01), p. 37-47
    Kurzfassung: Abstract. In recent years, machine learning (ML) modeling (often referred to as artificial intelligence) has become increasingly popular for personnel selection purposes. Numerous organizations use ML-based procedures for screening large candidate pools, while some companies try to automate the hiring process as far as possible. Since ML models can handle large sets of predictor variables and are therefore able to incorporate many different data sources (often more than common procedures can consider), they promise a higher predictive accuracy and objectivity in selecting the best candidate than traditional personal selection processes. However, there are some pitfalls and challenges that have to be taken into account when using ML for a sensitive issue as personnel selection. In this paper, we address these major challenges – namely the definition of a valid criterion, transparency regarding collected data and decision mechanisms, algorithmic fairness, changing data conditions, and adequate performance evaluation – and discuss some recommendations for implementing fair, transparent, and accurate ML-based selection algorithms.
    Materialart: Online-Ressource
    ISSN: 1866-5888 , 2190-5150
    Sprache: Englisch
    Verlag: Hogrefe Publishing Group
    Publikationsdatum: 2022
    ZDB Id: 2542411-7
    SSG: 3,2
    SSG: 5,2
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 8
    Online-Ressource
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    Hogrefe Publishing Group ; 2020
    In:  European Journal of Psychological Assessment Vol. 36, No. 4 ( 2020-07), p. 563-572
    In: European Journal of Psychological Assessment, Hogrefe Publishing Group, Vol. 36, No. 4 ( 2020-07), p. 563-572
    Kurzfassung: Abstract. Several guidelines on how to construct questionnaire items exist, even though the literature lacks empirical evidence for their effectiveness. To investigate whether the addition of negations and vague quantifiers worsens the psychometric properties of an established questionnaire, 872 participants completed one version of the Positive and Negative Affect Schedule (PANAS) – the German original, a negated version, a version with vague quantifiers or a version with both negations and vague quantifiers. Reliability estimates, item-total correlations, Confirmatory Factor Analysis (CFA) model fit, and fit to the Partial Credit Model (PCM) were compared among the four conditions. No PANAS version was clearly superior as no systematic pattern in the psychometric properties was found. Our findings question the general applicability of the guidelines of item construction as well as the effectiveness of widely used statistical analyses assessing the quality of scales. The results should encourage researchers to put a stronger focus on careful item construction as relying on psychometric properties might not be sufficient to develop valid questionnaires.
    Materialart: Online-Ressource
    ISSN: 1015-5759 , 2151-2426
    Sprache: Englisch
    Verlag: Hogrefe Publishing Group
    Publikationsdatum: 2020
    ZDB Id: 2090873-8
    SSG: 5,2
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 9
    Online-Ressource
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    SAGE Publications ; 2022
    In:  Educational and Psychological Measurement Vol. 82, No. 3 ( 2022-06), p. 444-464
    In: Educational and Psychological Measurement, SAGE Publications, Vol. 82, No. 3 ( 2022-06), p. 444-464
    Kurzfassung: Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process. Hence, in this study, we evaluated the performance of different factor retention criteria—the Factor Forest, parallel analysis based on a principal component analysis as well as parallel analysis based on the common factor model and the comparison data approach—in combination with different missing data methods, namely an expectation-maximization algorithm called Amelia, predictive mean matching, and random forest imputation within the multiple imputations by chained equations (MICE) framework as well as pairwise deletion with regard to their accuracy in determining the number of factors when data are missing. Data were simulated for different sample sizes, numbers of factors, numbers of manifest variables (indicators), between-factor correlations, missing data mechanisms and proportions of missing values. In the majority of conditions and for all factor retention criteria except the comparison data approach, the missing data mechanism had little impact on the accuracy and pairwise deletion performed comparably well as the more sophisticated imputation methods. In some conditions, especially small-sample cases and when comparison data were used to determine the number of factors, random forest imputation was preferable to other missing data methods, though. Accordingly, depending on data characteristics and the selected factor retention criterion, choosing an appropriate missing data method is crucial to obtain a valid estimate of the number of factors to extract.
    Materialart: Online-Ressource
    ISSN: 0013-1644 , 1552-3888
    RVK:
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2022
    ZDB Id: 1500101-5
    ZDB Id: 206630-0
    SSG: 5,2
    SSG: 5,3
    Standort Signatur Einschränkungen Verfügbarkeit
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  • 10
    Online-Ressource
    Online-Ressource
    Oxford University Press (OUP) ; 2023
    In:  Journal of the Royal Statistical Society Series B: Statistical Methodology Vol. 85, No. 4 ( 2023-09-29), p. 1087-1088
    In: Journal of the Royal Statistical Society Series B: Statistical Methodology, Oxford University Press (OUP), Vol. 85, No. 4 ( 2023-09-29), p. 1087-1088
    Materialart: Online-Ressource
    ISSN: 1369-7412 , 1467-9868
    Sprache: Englisch
    Verlag: Oxford University Press (OUP)
    Publikationsdatum: 2023
    ZDB Id: 204795-0
    ZDB Id: 1490719-7
    Standort Signatur Einschränkungen Verfügbarkeit
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