Energy-based safety risk management : using hazard energy to predict injury severity
Notice bibliographique
Résumé
Worker injuries and fatalities have long been problematic in the construction industry. To address this ongoing concern, recent research has focused on risk-based approaches to proactive safety management. Although the quantity and quality of safety risk data has improved in recent years, available data do not link directly to natural principles and are, therefore, limited in their application and scientific extension. This study offers a new explanation of safety risk using the concept of energy where the underlying proposition is that all hazards are truly defined by the exposure to one or more of ten distinct forms of energy (e.g., gravity, motion, electrical). This concept of safety energy was introduced by William Haddon, was operationalized in a past Construction Industry Institute (CII) research team, and is currently being tested by an active CII research team. The present study aims to link energy transfer to safety risk for the first time. Inspired by natural disaster modeling, the concept of energy is translated to risk by defining the severity of a potential event as the ratio of the magnitude of the energy to the resiliency of the impacted human body part and the pressure exerted on impacted body part. Additionally, the likelihood component of risk is defined by the combination of human, social, technological, and other factors that contribute to the chance that there is an unwanted transfer of energy. To test this proposition, energy-based risk data were extracted from two sources: (1) a random sample of 40 injury reports taken from a larger database containing approximately 7,250 injury reports obtained from 281 private construction organizations and (2) a random sample of National Institute of Occupational Safety and Health Fatality Assessment and Control Evaluation (NIOSH FACE) reports. For each report, a combination of manual and automated content analyses was used to extract the following data: the chief energy source(s) contributing to the incident, the quantity of energy involved, the part of the body affected, and the severity of the outcome. Generalized linear models derived from initial results demonstrate that energy possesses legitimacy in predicting the severity of an injury that will result from a particular hazard, tentatively confirming the proposed theory. This research indicates that energy-based safety risk analysis is a promising line of scientific inquiry with predictive validity that has the potential to increase our understanding of the natural phenomena that contribute to injuries. This research corroborates previous hazard recognition research that introduced the energy principle of hazard classification but challenges the scientific merit of past safety risk data.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».