Strategies to address COVID-19 vaccine hesitancy in First Nations peoples: a systematic review
Bibliographic record
Abstract
BACKGROUND: First Nations peoples face disproportionate vaccine-preventable risks due to social, economic, and healthcare disparities. Additionally, during the COVID-19 pandemic, there was also mistrust and hesitancy about the COVID-19 vaccines among First Nations peoples. These are rooted in factors such as colonial histories, discriminatory medical practices, and unreliable information. OBJECTIVE: To examine strategies to address COVID-19 vaccine hesitancy among First Nations peoples globally. METHODS: A systematic review was conducted. Searches were undertaken in OVID MEDLINE, OVID EMBASE, OVID PsycINFO, CINAHL, and Informit. Searches were date limited from 2020. Items included in this review provided primary data that discussed strategies used to address COVID-19 vaccine hesitancy in First Nations peoples. RESULTS: We identified several key strategies across four countries - Australia, the USA, Canada, and Guatemala in seventeen papers. These included understanding communities' needs, collaborating with communities, tailored messaging, addressing underlying systemic traumas and social health gaps, and early logistics planning. CONCLUSION: The inclusion of First Nations-centred strategies to reduce COVID-19 vaccine hesitancy is essential to delivering an equitable pandemic response. Implementation of these strategies in the continued effort to vaccinate against COVID-19 and in future pandemics is integral to ensure that First Nations peoples are not disproportionately affected by disease.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".