The Psychology of Sustainability and Sustainable Development: Advancing Decent Work, Inclusivity, and Positive Strength-Based Primary Preventive Interventions for Vulnerable Workers

Abstract

This study discusses the contribution of the psychology of sustainability and sustainable development to the wellbeing of vulnerable workers. The psychology of sustainability and sustainable development is a current area of the research study in the field of sustainability science. It deals with sustainability as a framework to recognize and integrate the value of the psychological approach in the construction of processes linked to sustainable development. Accordingly, the psychology of sustainability and sustainable development could provide sustainable development processes for the employment of vulnerable workers. The contribution starts with the definition of the coordinates of a sustainable development process for vulnerable workers, anchoring it to the principles of decent work and inclusivity. Subsequently, positive variables involved in the sustainable development processes and their relationship with decent work and inclusivity are discussed. Moreover, positive healthy organizations are introduced as work environments capable to take care of the wellbeing, also, of vulnerable workers. Lastly, the applications of positive strength-based primary preventive interventions for vulnerable workers are analyzed. Perspectives for further research studies and interventions are also examined.

Decent work in Italy: A network analysis

“…The main strengths are that the network analysis was carried out for the first time to study the construct of decent work (Duffy et al, 2017), following state-of-the-art guidelines (Epskamp et al, 2017, 2018; Epskamp & Fried, 2018), and that our results are stable and trustworthy.…”

“…Network stability was calculated via the correlation stability (CS) coefficient, non-parametric bootstrapped difference test for EI, non-parametric bootstrapped difference test for edge weights and bootstrap tests of edge weight accuracy (Epskamp et al, 2018). A CS coefficient of > 0.50 indicates a stable strength (Epskamp et al, 2018).…”

“…Network stability was calculated via the correlation stability (CS) coefficient, non-parametric bootstrapped difference test for EI, non-parametric bootstrapped difference test for edge weights and bootstrap tests of edge weight accuracy (Epskamp et al, 2018). A CS coefficient of > 0.50 indicates a stable strength (Epskamp et al, 2018). The non-parametric bootstrapped difference test for edge weights (2000 bootstraps on 95% confidence intervals, CIs, of estimated differences) was employed to determine the statistically significant differences among edges.…”

“…The non-parametric bootstrapped difference test for EI (2000 bootstraps on 95% CIs of estimated differences) was employed to determine the statistically significant difference between the EI calculated for each node. Central boxes display the row value of EI, gray boxes indicate no statistically significant differences and black boxes indicate statistically significant differences (Epskamp et al, 2018). Finally, the bootstrap tests of the edge weight accuracy furnish a plotted curve of the 95% CIs, where larger CIs indicate lower precision and narrower CIs indicate higher precision (Epskamp et al, 2018).…”

“…Central boxes display the row value of EI, gray boxes indicate no statistically significant differences and black boxes indicate statistically significant differences (Epskamp et al, 2018). Finally, the bootstrap tests of the edge weight accuracy furnish a plotted curve of the 95% CIs, where larger CIs indicate lower precision and narrower CIs indicate higher precision (Epskamp et al, 2018). The R packages igraph (Nepusz, 2021) and bootnet (Epskamp, 2021c) were used.…”