Interim Report for Creative Sentencing: Building Resilience into Safety Management Systems: Precursors and Controls to Reduce Serious Injuries and Fatalities (SIFs)
Creators: Lefsrud, Lianne M.
Sattari, Fereshteh
Gellatly, Ian R.
Wasel, Claire
Charuvil Elizabeth, Rose Marie
Abdolmaleki, Amir
Jones, Samantha
O’Neill, Thomas
Publisher: name: University of Alberta Library
Year: 2024

<p><strong>Executive Summary</strong><br>
On January 13, 2021, a contractor, Patrick Poitras was operating a John Deere bulldozer on a frozen tailings pond at the Suncor base mine, when the ice broke and the dozer fell through, drowning the operator. Suncor and the contractor Christina River Construction each pled guilty to one count of a violation of Alberta’s Occupational Health and Safety (OHS) Act. The purpose of this project is to examine the hazards and precursors of serious injuries and fatalities (characteristics of the work/environment, human and organizational factors, and management system), in Alberta’s oilsands industry, identify indicators and controls, and implement system-level assurance of controls.Shareholders and customers are demanding increased accountability, transparency, and comparability of companies’ risk management. Despite the benefits of technological risk management methods to achieve these goals, there is a chasm between companies’ safety intent and implementation: 96% appreciate the value of technology, and 77% say safety is part of their corporate culture, but only 32% use leading indicators and just 11% fully use technology. These gaps contribute to serious injuries and fatalities (SIFs).<br>
SIFs have complex precursors that are not easily visible in companies’ physical environment, reports, or what workers say – but only in watching the ‘work as done’ and how it differs from ‘work as intended’. Even for routine work, controls are often absent, bypassed, or non-functional. This interim report shares our first year’s progress in examining human, organizational, and cultural factors – especially the interactions and differences between owners/operators, contractors, and subcontractors.<br>
We are developing new methods to analyze near misses, audits, inspections, and project data and applying new lenses from psychological models of efficacy and competency to reveal precursors that are often overlooked but useful to predict (and prevent!) serious incidents. We are developing surveys and interviews to better understand the organizational and safety culture, team climate, and human psychosocial factors that may influence safety behavior and SIFs.<br>
We have held two SIF workshops to identify, implement, and monitor critical controls for hazards with the potential for SIFs. During a November 2023 workshop in Ft. McMurray with 115 workers, we developed bowties to visualize the causes, consequences, and controls for six major incident hazards: 1) heavy-light vehicle interaction, 2) lifting and hoisting, 3) active haul operation, 4) confined space, 5) dropped objects, and 6) working at heights. These bowtie models can be used to visualize and understand the systems, causes, and consequences that are often complex and hidden. They will be useful for future training and critical control assurance.<br>
<strong>Overview of our planned research over the next year</strong><br>
<em>Human and Organizational Factors as Precursors for Safety Performance:</em><br>
There is a lack of knowledge regarding the range of person and social/group factors that shape day-to-day safety decisions, behaviors, and outcomes. This is especially true in complex and dynamic work environments where safety hazards impose serious risks to employees and the organization. A data-informed method is needed to explore the key person and social/group factors that impact employee safety performance. This study seeks to identify the human and organizational factors that contribute to the occurrence of SIFs, as well as how these variables may interact to influence day-to-day safety behaviors among work units. The expected results for this study include identifying the most influential person and social/group factors that cause incidents, and subsequent implementation of data-informed decision-making methods to enhance safety practices.<br>
<em>Using Machine Learning and Natural Language Processing to Enhance Safety Performance and Quality of Occupational Health and Safety (OHS) Procedures by Focusing on Missing Socio-Technical Factors in the Mining Industry:</em><br>
There is a lack of comprehensive studies investigating the socio-technical (ST) factors that influence safety performance among contractors and employees in dynamic work environments like the mining industry, thus highlighting the need for a data-informed method to identify the contributing ST factors. Therefore, this study aims to identify socio-technical factors not captured during safety inspections using machine learning, natural language processing, and text mining techniques. The study&#39;s findings aim to provide insights for decision-makers about the strategies needed to enhance safety performance in the mining industry.</p>