Exploring the design of new human health risk assessment approaches for Indigenous community contexts
Notice bibliographique
Résumé
Environmental pollution poses unique and complex health risks to many Indigenous communities in Canada. These risks arise through both disproportionate exposure to contaminants and associated impacts on culture, spirituality, language, and traditional food systems that are unique to Indigenous communities. To date, institutionalized human health risk assessment (HHRA) approaches have not been developed or implemented with these unique contexts in mind, and thus often fail to capture health risks of relevance to Indigenous Peoples and communities. Recent proposed amendments to the Canadian Environmental Protection Act through Bill S-5 demonstrates regulatory interest in developing new approaches to risk assessments that are more efficient and ethical than conventional methods. This paradigm shift presents an opportunity to increase the relevance of HHRA approaches for the Indigenous communities in which they may be ultimately applied. However, despite the need for Indigenous community relevant HHRA approaches, and increasing regulatory support in this area, to date there has been minimal research conducted on the design of HHRA approaches for and by Indigenous Peoples and in Indigenous community contexts.The objective of this thesis is to explore, ideate, test, and develop new approaches to human health risk assessment that are relevant for use for Indigenous community contexts, in collaboration with communities themselves. Doing so hinges on the consideration of diverse perspectives, and thus the research uses an interdisciplinary methodological design and data collection approach. The chapters follow the iterative process of design thinking (empathize, define, ideate, prototype, test). Chapter 3 presents an initial scoping review on the topic of contaminated sites and Indigenous Peoples, which compares information from three data streams finding an overall diverse and disparate body of literature on the topic and identifying areas for further research. Chapter 4 presents a multi-sector survey study exploring human health and ecological risk assessment practice in Indigenous communities in Canada, which narrows in on key challenges and priorities and compares these amongst sectors. Chapter 5 tests the use of an HHRA approach of regulatory interest, RISK21, through two collaborative case studies involving three distinct Indigenous communities (Chipewyan Prairie First Nation, Cold Lake First Nations, and Apsáalooke (Crow) Tribe). Chapter 6 presents an initial regional-level pilot test of an existing approach to organizing and measuring Indigenous Health Indicators of relevance to risk assessment work. Chapter 7 provides an overview of an iterative methodological approach to designing an Indigenous Health Indicators tool by the Kanien'kehá:ka community of Kanesatake, and presents the initial findings of semi-structured, qualitative interviews on this topic. The health indicators tool may be used by the Kanesatake Environment Department to contextualize environmental assessments with community-defined health information. Together, the chapters in this thesis aim to support the development of HHRA approaches for contaminants that are relevant, useful, and meaningful for the Indigenous communities in which, and by whom, they may ultimately be used. To do so, this work includes individual community-level design work towards risk assessment tools that are useful locally, simultaneously providing an example of a methodological ‘roadmap’ for other communities that may increase understanding of how to design, test, and validate new risk assessment tools to suit their unique contexts. The work also encompasses contributions to a broader understanding of some of the challenges and priorities with HHRA design and implementation at a national scale, which is important for eventual regulatory development and adoption
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 | 0,040 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,007 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,005 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».