Recognising the heterogeneity of Indigenous Peoples during the COVID-19 pandemic: a scoping review across Canada, Australia, New Zealand and the USA
Bibliographic record
Abstract
Objectives: The COVID-19 pandemic has had a disproportionate impact on the health of Indigenous Peoples in Canada, Australia, New Zealand and the USA, as reflected in the growing literature. However, Indigenous Peoples are often homogenised, with key differences often overlooked, failing to capture the complexity of issues and may lead to suboptimal public health policy-making. The objective of this review was to assess the extent to which the heterogeneity of the Indigenous Peoples in Canada, Australia, New Zealand and the USA has been reflected in COVID-19 research. Design: This study took the form of a scoping review. Data sources: Medline, Embase, CINAHL and Web of Science were searched for studies investigating COVID-19 pandemic outcomes among Indigenous Peoples in Canada, Australia, New Zealand and the USA. The search dates included January 2019 to January 2024. Eligibility criteria: All citations yielded by this search were subjected to title and abstract screening, full-text review and data extraction. We included original, peer-reviewed research investigating COVID-19-related outcomes among Indigenous Peoples in Canada, Australia, New Zealand or the USA. Data extraction and synthesis: Data extraction was conducted as an iterative process, reaching consensus between two of the study authors. All included studies were analysed through a combination of quantitative descriptive summary and qualitative thematic analysis. Results: Of the 9795 citations found by the initial search, 428 citations were deemed eligible for inclusion. Of these citations: 72.9% compared Indigenous participants to non-Indigenous participants; 10.0% aggregated Indigenous and non-white participants; and 17.1% provided findings for Indigenous participants exclusively. Conclusions: By overlooking the heterogeneity that exists among Indigenous Peoples in Canada, Australia, New Zealand and the USA, researchers and policy-makers run the risk of masking inequities and the unique needs of groups of Indigenous Peoples. This may lead to inefficient policy recommendations and unintentionally perpetuate health disparities during public health crises.
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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 | 0.043 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.028 | 0.037 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".