Key Takeaways

- Understanding how to prevent serious injuries and fatalities (SIFs) is a priority for the safety profession. Potential SIF (PSIF) events may be used to significantly broaden learning. However, an experiment revealed that current methods of defining PSIF events result in so much inconsistency in classification (noise) that they have limited utility.

- To address this core limitation, the safety classification and learning (SCL) model was created by an integrated team of academic and industry professionals. This model is based on the science of energy-based safety, controls analysis and principles of human performance.

- A community of practice was created to facilitate implementation and diffusion of the SCL model via calibration, revision, data sharing, sector-level trending and advocacy.


In the safety profession, nothing is more important than preventing serious injuries and fatalities (SIFs). Despite widespread efforts, however, SIFs continue to plague every major industry. In 2021 alone, 5,190 fatal injuries occurred in U.S. workplaces (BLS, 2022), resulting in $6 billion of direct costs and immeasurable harm to the well-being of the workforce and their families (NSC, n.d.). Although safety professionals have made great strides in the prevention of recordable injuries, the rate of SIFs has generally plateaued and even increased in recent years (BLS, 2022). For example, as observed in Figure 1 (p. 20), the rate of OSHA-recordable injuries declined in the electric utility sector by approximately 50% over the past decade while the rate of fatal injuries has remained relatively stable. When examining 3.2 trillion worker hours of data across industrial sectors, Hallowell et al. (2021) found a similar statistical disconnect. These trends provide compelling evidence that reductions in lower-severity injuries do not translate to proportional reductions in SIFs, which directly contradicts antiquated theories stemming from the unfortunately ubiquitous Heinrich pyramid (Heinrich, 1931). Therefore, targeted methods are needed for SIF-specific learning and prevention.

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