Mixed methods approach for secondary data using survey reports from an exploration industry database
Notice bibliographique
Résumé
The rich information acquired from incident and near miss reporting has been studied within high-risk industries and such industries have used statistics acquired from past incident reports to reveal trends to improve occupational health and safety. The proposed project aims to understand the specific nature of injury severity reports within Canada’s mineral exploration field to enhance existing occupational health and safety. The proposed research is unique as the data arises from the entire mineral exploration industry in Canada, gathered by Prospectors and Developers Association of Canada (PDAC), to represent a group of companies, working across Canada. Data of this magnitude, over such a long time span, in this workforce, has never been conducted before. Much research in H&S in the mining field has focused on mine workers, not on mineral exploration, due to the difference in numbers. It is difficult to extrapolate knowledge from other fields to mineral exploration, because of its high specialization, creating unique H&S challenges. Although closely linked to production mining, Mineral Exploration requires a different health and safety approach. This workforce has unique health and safety needs that arise due to: the nature of the working environment; remote locations subject to extreme weather and terrain; difficulty recruiting skilled workers in times of economic booms due to production pressures or conflicts of interest between H&S superiors and trainees; lack of available resources in the field which vary and are dependent on financial capacities of each company ; and H&S efforts heavily influenced by a company’s market capitalization meaning smaller companies often times not having one person specifically in charge of H&S or potentially be less obliged to follow or partake in H&S procedures, and in large companies it is more likely to have a whole group whose sole focus is H&S and the environment. Determining which factors influence health and safety within mineral exploration is therefore a crucial first step to better understand the safety culture, safety consciousness, and the specific needs of this field. Given that the health and safety environment of mineral exploration is multidimensional, it is pertinent that research be conducted directly within this field to bridge gaps in prevention and practice. The expected outcome for this project was twofold: i) to highlight the health and safety trends in the industry; and ii) to determine common trends, areas of importance, critical issues, and actionable training suggestions, to mitigate risk for workers. This was done by taking survey data and showcasing points for industry and occupational health and safety advocates through knowledge transfer components, to provide a deeper understanding of various components that contribute to injuries and fatalities.
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 | 0,094 | 0,208 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,006 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,006 |
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 ».