NextGen Decision-Making for Health and Environmental Risks
Notice bibliographique
Résumé
Risk decision-making has evolved from specifying the main steps involved in risk assessment and how this links to the overall analysis and governance of risks, to complex and fully integrated risk science frameworks. With the advancement in science and technology, there has also been a significant change in the types of evidence available for identifying and characterizing hazards, determining potential exposure profiles, and evaluating the probability of health and environmental risks. These efforts have resulted in several publications focused on providing precision on specific areas within the risk assessment-management process and new approaches for undertaking risk assessments. There has also been a transition to more complex and holistic risk science frameworks, methodologies, and guidelines. The term next generation risk decision-making is used to capture these contemporary approaches. Further, while there is specific guidance for a particular approach, limited attention has been paid to the underlying considerations for developing these contemporary and more complex and holistic approaches to risk decision-making. In this thesis, we address this knowledge gap through an embedded research model designed to determine the ongoing modernization efforts and approaches to next generation risk decision-making within both the Canadian and international regulatory context (national and federal level). The embedded research model is coupled with an independent knowledge mobilization and transfer strategy that relies on the engagement and collaboration, with diverse experts and thought leaders, to determine fundamental risk and ethical principles for risk decision-making. Further, a scoping review provides a mechanism to visualize the evolution in risk science over the last fifty years, and characterize best practices and ten key attributes for risk decision-making. To help integrate our findings, we rely on a realist paradigm to generate an a priori theoretical construct and kaleidoscope model, which translates the principles, best practices, and attributes into key considerations for developing an approach to next generation risk decision-making. Our findings are widely applicable to any organization or governmental body interested in learning about next generation risk decision-making, including adapting or developing their own approach to risk decision-making, informed by the results presented in this thesis. The embedded research model, knowledge mobilization and transfer strategy, realist paradigm, and publication in open science, peer-reviewed journals ensures that our findings are available for the broader community interested in next generation risk decision-making.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».