Analyzing Occupational Accidents and Exoskeleton Potential in the Construction Industry in Québec, Canada
Bibliographic record
Abstract
Occupational applications Our analysis of construction firm accident data in Québec, Canada, spanning from January 2019 to June 2022, provides critical insights for ergonomics and human factors practitioners. The predominant accidents involved being struck by objects (31%) and falls (15%), with lacerations and bruises comprising 37% of injuries. Notably, carpenters had the highest accident rate (22%), prompting attention to task-specific safety measures. We also examined musculoskeletal injuries (MSI), finding that bodily reactions (46%) and overexertion (44%) were primary causes. Importantly, we qualitatively explored the potential of exoskeletons as possible proactive safety measures; our results suggested that in 50% of MSI cases, exoskeletons might have helped to mitigate or eliminate risks. These findings underscore the potential for exoskeletons to enhance safety and productivity in the construction industry, offering opportunities for intervention and preventive measures in ergonomics practice.TECHNICAL ABSTRACT Background: The construction industry is a hazardous working environment, having a relatively high risk of accidents and injuries compared to other industries. Purpose: We aimed to describe the characteristics of work accidents in a large construction firm in Québec, Canada, using accident data from the Health, Safety, and Environment Department. Methods: Our dataset spanned from January 2019 to June 2022 and included 2065 complete entries for analysis. Accidents were categorized using a standardized classification scheme, augmented with additional accident types to provide more precision. Results: The most common type of accident was being struck by an object, comprising 31% of reported incidents, followed by falls at 15%. Lacerations and bruises were the most prevalent injuries, accounting for 37% of cases, with injuries to the hand (31%) and the head/eye (24%) being the most frequent. Among the trades analyzed, carpenters had the highest accident rate at 22%, closely followed by laborers at 20%. We also focused on occupational musculoskeletal injuries (MSI) in the dataset to qualitatively investigate the viability of exoskeletons as a proactive safety measure. Of the 268 incidents categorized as MSI-related (13% of the 2065 events), bodily reactions (46%) and overexertion (44%) were the leading causes. A detailed qualitative analysis of the event descriptions suggested that if exoskeletons had been made available and used, they could have contributed to reducing or eliminating MSI risk in 50% of cases. Conclusions: These results contribute to enhancing safety and productivity in the construction industry by providing insights into work accidents and task characteristics that can be used to improve exoskeleton design and compatibility with the work to be performed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".