Analyzing Occupational Accidents and Exoskeleton Potential in the Construction Industry in Québec, Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,015 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».