Funding the pandemic response for Indigenous Peoples: an equity-based analysis of COVID-19 using a Health Equity Impact Assessment (HEIA) Indigenous lens tool
Bibliographic record
Abstract
This study examines the allocation of COVID-19 funding for Indigenous Peoples in Canada, Australia, New Zealand, and the United States during the pandemic's first wave. Indigenous communities, already facing health disparities, systemic discrimination, and historical forces of colonisation, found themselves further vulnerable to the virus. Analysing the funding policies of these countries, we employed a Health Equity Impact Assessment (HEIA) tool and an Indigenous Lens Tool supplement to evaluate potential impacts. Our results identify three major funding equity issues: unique health and service needs, socioeconomic disparities, and limited access to community and culturally safe health services. Despite efforts for equitable funding, a lack of meaningful consultation led to shortcomings, as seen in Canada's state of emergency declaration and legal disputes in the United States. New Zealand stood out for integrating Māori perspectives, showcasing the importance of consultation. The study calls for a reconciliation-minded path, aligning with Truth and Reconciliation principles, the UN Declaration on the Rights of Indigenous Peoples, and evolving government support. The paper concludes that co-creating equitable funding policies grounded in Indigenous knowledge requires partnership, meaningful consultation, and organisational cultural humility. Even in emergencies, these measures ensure responsiveness and respect for Indigenous self-determination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".