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Record W7162088532 · doi:10.82308/1246

Exploring the design of new human health risk assessment approaches for Indigenous community contexts

2025· dissertation· en· W7162088532 on OpenAlexaboutno aff
Katie Chong

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousRelevance (law)Traditional knowledgeRisk assessmentPublic healthCommunity health

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0090.006
Open science0.0050.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.286
GPT teacher head0.421
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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