Using Employee Risk Identification Reports to Measure Safety Performance and Set Safety Priorities
Notice bibliographique
Résumé
Using Employee Risk Identification Reports to Measure Safety Performance and Set Safety Priorities R. Dickes; R. Dickes Schlumberger Search for other works by this author on: This Site Google Scholar T. Wood T. Wood Schlumberger Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. Paper Number: SPE-86743-MS https://doi.org/10.2118/86743-MS Published: March 29 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Dickes, R., and T. Wood. "Using Employee Risk Identification Reports to Measure Safety Performance and Set Safety Priorities." Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. doi: https://doi.org/10.2118/86743-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search AbstractEmployee risk reports are a valuable part of a safety program. Managers frequently encourage employees to identify, correct, and report risks they discover while performing their jobs. The efforts by managers can have an immediate impact on safety performance by reducing or eliminating hazards in the workplace. However, the additional benefits that can be gained if employee reports are routinely analyzed for trends are often not realized.If these reports are analyzed, they provide constant and valuable feedback. This feedback can be used to detect negative trends and set priorities for future actions and improvements. In addition, reporting the results of an analysis to employees can increase employee participation in the risk-reporting system.The case study described in this paper shows the positive benefits achieved through risk-report analysis over a 2-year period. The feedback from this analysis was used to set priorities and was reported directly to employees, resulting in a more than 30% increase in employee risk reporting.IntroductionBeginning in the late 1980s, one major oilfield service company started programs to encourage employees to identify hazards they encountered at work, and to share this information with coworkers. The methods used to collect and share this information were informal systems managed at each operating location. However, within a decade the company developed a reporting system1 that enabled an employee to report a hazard and share it with coworkers worldwide.The introduction of a worldwide reporting system not only enabled information sharing, but also created an opportunity for managers to use this employee feedback to identify trends and set priorities worldwide. To do this, we completed a systematic review of the reports pertaining to ionizing radiation submitted during the period from January 1, 2001 through December 31, 2002.Completing this review, we detected several important facts. The radiation safety category covers a wide range of possible topics. In spite of this, employee reports centered on eight key topics or subcategories, with three subcategories dominating. A regional analysis of the data showed worldwide consistency in the reports. Within each of three regions, the eight subcategories and the three dominant subcategories were consistent, and employees submitted reports in nearly equal percentages in each subcategory.Company management incorporated these important discoveries into decisions when setting priorities for their radiation safety program. The company communicated these priorities to its employees and this feedback resulted in a 30% increase in the number of reports.History of Risk ReportingSafety risk reporting is a process to encourage employees to report to their supervisors and managers the risks they identify while performing their jobs. Risk reporting within the oilfield service company began in the late 1980s as field employees began reporting hazardous or unsafe conditions during the course of jobs being conducted on offshore drilling platforms. This process was primitive compared to the system used today. The information was simply captured on a piece of paper and submitted to the facility manager. Even though there was no official means of capturing, analyzing, or disseminating this information, safety managers realized the potential value of these risk reports. Safety managers generally agreed that two potential benefits existed:Accidents could be reduced when unsafe conditions were eliminated, before they resulted or contributed to an accident.Accidents could be reduced when employees learned valuable lessons from the risk reports made by other employees, and could take proactive steps to identify and either resolve or mitigate the unsafe condition at other locations.By the early 1990s, a concerted effort began to standardize the report form with the introduction of what was known as the Risk Identification Report (RIR). Each field location was responsible for tracking, categorizing, and disseminating the information. Regional offices manually compiled and maintained this information from each field location. Keywords: employee report, society of petroleum engineers, risk assessment, risk management, database, measure safety performance, hazard category, subcategory, encourage employee, upstream oil & gas Subjects: Safety, Risk Management and Decision-Making, Professionalism, Training, and Education, Risk, uncertainty, and risk assessment This content is only available via PDF. 2004. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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 | 0,027 | 0,135 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,020 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,004 |
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 ».