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Enregistrement W4394962400 · doi:10.1002/ieam.4927

Exploring practices, challenges, and priorities for human health and ecological risk assessments in Indigenous communities in Canada

2024· article· en· W4394962400 sur OpenAlexafffundabout
Katherine Chong, Gordon M. Hickey, Hing Man Chan, Niladri Basu

Notice bibliographique

RevueIntegrated Environmental Assessment and Management · 2024
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental and Social Impact Assessments
Établissements canadiensUniversity of OttawaMcGill University
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
Mots-clésIndigenousRisk assessmentEnvironmental healthEnvironmental planningEnvironmental resource managementGeographyBusinessMedicineEcology

Résumé

récupéré en direct d'OpenAlex

Abstract Indigenous peoples in Canada are disproportionately exposed to environmental contaminants and may face elevated health risks related to their unique cultural, spiritual, and economic relationships with the land, including the use of traditional food systems. However, to date, institutionalized approaches to assess risks to human and ecological health from contaminants have not been well developed or implemented with Indigenous community contexts in mind. There is regulatory interest in developing new approach methods for risk assessment, and thus an opportunity to increase their relevance to Indigenous communities in which they will be ultimately applied. Therefore, we conducted an anonymous mixed-methods survey of those involved with risk assessment in Indigenous communities in Canada to: (1) understand risk assessment practice in Indigenous communities, (2) explore challenges with conventional assessment methods and compare these across sectors, and (3) gather perspectives on the development of new approaches. In all, 38 completed survey responses were received (14% response rate). Respondents were from Indigenous community environment and health offices (21% of respondents), Indigenous governments (8%), federal and provincial governments (21%), and academia (45%). Risk communication was seen as the most challenging part of risk assessment (71% responded “difficult”), and nearly all respondents agreed that time (86%), cost (76%), and resource availability (86%) were “moderate” to “serious” problems. Few respondents (16%) had heard of “new approach methods” for risk assessment, and 76% of respondents (and 100% of community-based respondents) agreed on the need to develop improved risk assessment approaches. To modernize risk assessment, respondents recommended advancing cumulative risk assessment methods, improving risk communication, and promoting Indigenous leadership and Traditional Knowledge in assessment activities. Integr Environ Assess Manag 2024;20:1677–1692. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points The online survey was completed by 38 respondents from across Canada who had experience with human health and ecological risk assessments, spanning a diversity of sectors including Indigenous communities, provincial and federal governments, and academia. Cumulative risk assessment, risk communication, and resource availability were identified as some of the most challenging aspects of risk assessment in the context of Indigenous communities in Canada and may be key considerations in the development of new, community-relevant methods. Perspectives differed among groups (community-based respondents, federal and provincial employees, and academics), signaling a need for further research in this area and implementation of strategies to improve multisectoral collaboration and communication. Most (76%) respondents agreed on the need for new approaches to risk assessment for Indigenous community contexts, and this agreement was unanimous (100%) among those who worked in communities themselves.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,318
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,096
Tête enseignante GPT0,337
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2024
Routes d'admission3
Résumé présentoui

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