Editorial: Advances in Research on Age in the Workplace and Retirement

The Editorial on the Research Topic. Advances in Research on Age in theWorkplace and Retirement

The selection, optimization, and compensation model in the work context:A systematic review and meta-analysis of two decades of research

Free to read Over the past two decades, the selection, optimization, and compensation (SOC) model has been applied in the work context to investigate antecedents and outcomes of employees' use of action regulation strategies. We systematically review, meta-analyze, and critically discuss the literature on SOC strategy use at work and outline directions for future research and practice. The systematic review illustrates the breadth of constructs that have been studied in relation to SOC strategy use, and that SOC strategy use can mediate and moderate relationships of person and contextual antecedents with work outcomes. Results of the meta-analysis show that SOC strategy use is positively related to age (rc = .04), job autonomy (rc = .17), self-reported job performance (rc = .23), non-self-reported job performance (rc = .21), job satisfaction (rc = .25), and job engagement (rc = .38), whereas SOC strategy use is not significantly related to job tenure, job demands, and job strain. Overall, our findings underline the importance of the SOC model for the work context, and they also suggest that its measurement and reporting standards need to be improved to become a reliable guide for future research and organizational practice

Using the Selection, Optimization, and Compensation Model of Action-Regulation to Explain College Students’ Grades and Study Satisfaction

Statistics on study disruptions and delays and their negative impact on academic performance call for action-regulation strategies that students can use to manage their performance and well-being. In the present research, we rely on the action-regulation model of selection, optimization, and compensation (SOC), which was developed in the life span developmental literature. The aim of the present study was to establish indirect links between two specific SOC components (i.e., elective selection and optimiza- tion) and study outcomes (i.e., end-of-first-year average grade and study satisfaction) through higher self-efficacy beliefs. In 2 prospective studies conducted during 2 subsequent academic years, we tested our research model with first-year undergraduate students (n ﰀ 366 in Study 1 and n ﰀ 242 in Study 2). Results of both studies indicate that there are positive indirect relations between optimization, but not elective selection, and favorable study outcomes through self-efficacy beliefs. The present study con- tributes to SOC theory and the educational sciences by showing that the SOC model of action-regulation can be helpful in explaining college students’ grades and study satisfaction

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

Abstract

Integrating the social identity and aging literatures, this work tested the hypothesis that there are two independent, but simultaneous, responses by which adults transitioning into old age can buffer themselves against age discrimination: an individual response, which entails adopting a younger subjective age when facing discrimination, and a collective response, which involves increasing identification with the group of older adults. In three experimental studies with a total number of 488 older adults (50 to 75 years of age), we manipulated age discrimination in a job application scenario and measured the effects of both responses on perceived health and self-esteem. Statistical analyses include individual study results as well as a meta-analysis on the combined results of the three studies. Findings show consistent evidence only for the individual response, which was in turn associated with well-being. Furthermore, challenging previous research, the two responses (adopting a younger subjective age and increasing group identification) were not only theoretically, but also empirically distinct. This research complements prior research by signaling the value of considering both responses to discrimination as complementary rather than mutually exclusive