COVID-19 among Indigenous communities: Case studies on Indigenous nursing responses in Australia, Canada, New Zealand, and the United States
Bibliographic record
Abstract
Globally, Indigenous Peoples experience disparate COVID-19 outcomes.This paper presents case studies from Aotearoa New Zealand, Australia, Canada, and the United States of America and explores aspects of government policies, public health actions, and Indigenous nursing leadership for Indigenous communities during a pandemic.Government under-performance in establishing Indigenous-specific plans and resources, burdened those countries with higher COVID-19 cases and mortality rates.First, availability of quality data is an essential element of any public health strategy, and involves disaggregated, ethnic-specific data on Indigenous COVID-19 cases, mortality rates, and vaccination rates.When data is unavailable, Indigenous Peoples are rendered invisible.Data sovereignty principles must be utilised to ensure that there is Indigenous ownership and protections of these data.Second, out of necessity, Indigenous communities expressed their self-determination by uniting to protect their Nursing Praxis in Aotearoa New Zealand 2021 Vol 37 Special Issue COVID-19 72 support and resources.Holistic approaches to COVID-19 responses by Indigenous peoples must consider the wider determinants of wellbeing including food and housing security.Findings from these case studies, demonstrate that Indigenous self-determination, data sovereignty, holistic approaches to pandemic responses alongside with Governmental policies, resources should inform vaccination strategies and future pandemic readiness plans.Finally, in any pandemic of COVID-19-scale, Indigenous nurses' leadership and experience must be leveraged for a calm, trusted and efficient response.Keywords / Ngā kupu matua: case study / mātai tūāhua; COVID-19; data sovereignty / mana raraunga; global / ā-ao; Indigenous / iwi taketake; inequities / ngā korenga e
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".