Don't Call It Smart: Working From Home During the Pandemic Crisis.

The recent COVID-19 pandemic and related social distancing measures have significantly changed worldwide employment conditions. In developed economies, institutions and organizations, both public and private, are called upon to reflect on new organizational models of work and human resource management, which - in fact - should offer workers sufficient flexibility in adapting their work schedules remotely to their personal (and family) needs. This study aims to explore, within a Job Demands-Resources framework, whether and to what extent job demands (workload and social isolation), organizational job resources (perceived organizational support), and personal resources (self-efficacy, vision about the future and commitment to organizational change) have affected workers' quality of life during the pandemic, taking into account the potential mediating role of job satisfaction and perceived stress. Using data from a sample of 293 workers, we estimate measurement and structural models, according to the Item Response Theory and the Path analysis frameworks, which allow us to operationalize the latent traits and study the complex structure of relationships between the latent dimensions. We inserted in the model as control variables, the socio-economic and demographic characteristics of the respondents, with particular emphasis on gender differences and the presence and age of children. The study offers insights into the relationship between remote work and quality of life, and the need to rethink human resource management policies considering the opportunities and critical issues highlighted by working full-time remotely.

Italian Teachers' Well-Being Within the High School Context: Evidence From a Large Scale Survey.

This paper aims to investigate the relationship between Italian teachers' well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers' well-being, we investigate several aspects related to the teachers' working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers' well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers' positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers' job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher's well-being. Findings and implications are discussed.

Introduction to bifactor polytomous item response theory analysis.

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.