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Psychosocial stressors at work have been identified as significant risk factors for several mental and physical health problems. These stressors must be compensated by psychosocial resources to prevent or reduce adverse effects on health. Questionnaires measuring these stressors and resources already exist, but none integrate digital stress, ethical culture, and psychosocial safety climate; factors that are increasingly linked to workers' health. This study aims to develop and establish the psychometric properties of one of the most comprehensive instruments measuring the psychosocial work environment to date: the Occupational Health and Well-being Questionnaire (OHWQ). A cross-sectional validation study is proposed to develop the OHWQ and document its psychometric properties. The OHWQ was developed from validated instruments to which new items were added. The questionnaire includes psychosocial dimensions, along with indicators of psychological distress, musculoskeletal disorders, and well-being. It was administered to a sample of 2770 participants from a population working in the academic field. Exploratory and confirmatory factor analyses and the calculation of Cronbach's α coefficient were used to identify the variables, items, and, dimensions of the OHWQ and to document its main psychometric properties. The acceptability of the measurement model was evaluated by the reliability of the items, internal consistency between the items, and the convergent and discriminant validity. Construct validity was assessed using exploratory and confirmatory factor analyses. Using factor analyses and cut-off rules, the new instrument has 124 items grouped into 22 dimensions. The OHWQ demonstrated satisfactory reliability and validity, as well as reasonable fit indices. The internal consistency of the scales was also good (Cronbach's α = 0.68–0.96, median = 0.85). The OHWQ demonstrated good psychometric properties. It could be useful for both research purposes and for workplaces interested in developing concrete action plans aimed at improving the balance between psychosocial work stressors and resources. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a distributed algorithmic solution, termed Coalition formation and deployment algorithm , to achieve network configurations where agents cluster into coincident groups that are distributed optimally over the environment. The motivation for this problem comes from spatial estimation tasks executed with unreliable sensors. We propose a probabilistic strategy that combines a repeated game governing the formation of coalitions with a spatial motion component governing their location. For a class of probabilistic coalition switching laws, we establish the convergence of the agents to coincident groups of a desired size in finite time and the asymptotic convergence of the overall network to the optimal deployment, both with probability 1. We also investigate the algorithm’s time and communication complexity. Specifically, we upper bound the expected completion time of executions that use the proportional-to-number-of-unmatched-agents coalition switching law under arbitrary and complete communication topologies. We also upper bound the number of messages required per timestep to execute our strategy. The proposed algorithm is robust to agent addition and subtraction. From a coalitional game perspective, the algorithm is novel in that the players’ information is limited to the neighboring clusters. From a motion coordination perspective, the algorithm is novel because it brings together the basic tasks of rendezvous (individual agents into clusters) and deployment (clusters in the environment). Simulations illustrate the correctness, robustness, and complexity results. [Copyright &y& Elsevier]