Dr. Satoshi Maeda
Chuo University (Japan)
https://doi.org/10.53656/phil2024-03-07
Abstract. This paper investigates the connection between psychological safety at work and physical health outcomes. Employing data from roughly a thousand respondents in Tokyo in 2021 and utilizing machine learning techniques along with traditional statistics, the study reveals that this popular concept in the field of business management and organization behavior, “psychological safety” at work, can enhance physical health of workers under certain conditions. The finding that its effect remains even when mental stress levels are controlled along with basic ascriptive variables, extends the conventional notion around “psychological safety.” The recently developed Causal Forest Double Machine Learning (Causal Forest DML) analysis was used to generate a decision tree, shedding light on the structure of causal inference and indicating the key role of “age” and “personal income” as determinants of the effect of psychological safety on physical health.
Keywords: Psychological safety, workplace stress, Tokyo, Causal Forest DML, machine learning, physical health
Свързани статии:
Artifi cial Intelligence in Cybersecurity: Rigorous Critical Review, Methodological Challenges and Future Research Directions
Challenging the Students‘ Sense of Mathematics Via Decoding Problems in Chernorizec Hrabar Tournament
Research on the Basic Verbal Vocabulary in Bulgarian for Students in the Initial Stage of Education through Online Games
The Concept of Transcendental Apperception and its Role in the Second Edition of Kant’s Transcendental Deduction
