Indigenous peoples and the COVID-19 pandemic: a systematic scoping review
Bibliographic record
Abstract
Past influenza pandemics including the Spanish flu and H1N1 have disproportionately affected Indigenous Peoples. We conducted a systematic scoping review to provide an overview of the state of understanding of the experience of Indigenous peoples during the first 18 months of the COVID-19 pandemic, in doing so we capture the state of knowledge available to governments and decision makers for addressing the needs of Indigenous peoples in these early months of the pandemic. We addressed three questions: (a) How is COVID-19 impacting the health and livelihoods of Indigenous peoples, (b) What system level challenges are Indigenous peoples experiencing, (c) How are Indigenous peoples responding? We searched Web of Science, Scopus, and PubMed databases and UN organization websites for publications about Indigenous peoples and COVID-19. Results were analyzed using descriptive statistics and content analysis. A total of 153 publications were included: 140 peer-reviewed articles and 13 from UN organizations. Editorial/commentaries were the most (43%) frequent type of publication. Analysis identified Indigenous peoples from 19 different countries, although 56% of publications were centered upon those in Brazil, United States, and Canada. The majority (90%) of articles focused upon the general adult population, few (<2%) used a gender lens. A small number of articles documented COVID-19 testing (0.04%), incidence (18%), or mortality (16%). Five themes of system level challenges affecting exposure and livelihoods evolved: ecological, poverty, communication, education and health care services. Responses were formal and informal strategies from governments, Indigenous organizations and communities. A lack of ethnically disaggregated health data and a gender lens are constraining our knowledge, which is clustered around a limited number of Indigenous peoples in mostly high-income countries. Many Indigenous peoples have autonomously implemented their own coping strategies while government responses have been largely reactive and inadequate. To 'build back better' we must address these knowledge gaps.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".