Highlighting models of Indigenous leadership and self-governance for COVID-19 vaccination programmes
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately impacted Indigenous populations worldwide placing much importance on rapid and equitable vaccination. Nevertheless, many Indigenous communities have reported high vaccine hesitancy and low COVID-19 vaccine uptake. This may be attributed to various factors, including a lack of support for Indigenous leadership efforts to protect their communities and the pervasive infodemic targeting First Nations Peoples. In August 2022, we hosted an international symposium to bring together Indigenous and non-Indigenous community leaders, clinicians, and researchers to discuss pandemic experiences and lessons learnt. This commentary highlights examples of harnessing Indigenous leadership and self-governance to design and deliver tailored community-based and culturally appropriate COVID-19 vaccination programmes that improved vaccine uptake in Australia and Canada. These case studies demonstrate that Indigenous social-governance systems need to be valued, respected, and upheld if we are to make meaningful efforts to address health inequities among Indigenous communities during future pandemics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".