Evidence-based strategies to define, differentiate, and prevent serious injury and fatality exposure in construction

The construction industry has made strides to prevent most injuries, yet the most severe ones continue to plague the industry at alarming rates. Construction organizations are still exposed to a similar risk of fatal injuries as in past decades. This issue has motivated researchers to question whether all types of injuries share the same causes, and whether they need the same management strategies. However, the lack of a shared definition on what constitutes a ‘serious’ injury, coupled with insufficient validation of serious injury and fatality (SIF) prevention strategies across multiple organizations limit how much we know about the effectiveness of SIF prevention efforts. To address this gap, this dissertation seeks to answer: How can organizations better define, differentiate, and reduce SIF exposure in construction sites? Therefore, the objectives of this dissertation were to (1) create an injury classification model for defining SIFs and validate this model through a noise experiment, (2) identify the unique precursors of SIFs that distinguish them from less serious-injuries through blind assessments from an expert panel and real injury cases, (3) measure and isolate the impact of energy-based safety training on the unique precursors of SIFs through a multiple baseline experiment, and (4) test the statistical relationships between the SIF differentiators and long-term injury rates. To demonstrate the experimental protocol and further test statistical relationships between the unique precursors of SIFs and injury rates, the study recruited more than 30 companies representing the utility, oil and gas, and construction sectors. This collaboration resulted in over 2000 field observations of pre-job safety meetings and physical construction work involving high-energy hazards.The findings of this work demonstrate that short-term exposure to SIFs can be managed through better pre-job safety briefs and targeted controls of hazardous energy sources. Organizations that trained their crews on the energy wheel, high-energy hazards, and direct controls, were able to increase their proportion of direct controls for hazardous energy and the quality of their pre-job safety briefs by an average of 6.1%, and 15.7%, respectively. Furthermore, the findings show that shared definitions and consistent data collection protocols enhance the assessment of SIF prevention efforts yet there is still more work to do to fine tune the data collection process in the field. Future researchers may expand this work by testing other potentially unique precursors of SIFs, by validating or challenging these empirical findings with additional data, and by expanding the understanding of what is different about SIFs through qualitative interviews with front-line personnel. Practically, this work will potentially enhance the way that organizations measure the impact of their safety interventions and their return on investment. Methodologically, this work demonstrates the feasibility of running field experiments through industry collaboration to draw causal inferences on key safety initiatives.