Where past meets present: Indigenous vaccine hesitancy in Saskatchewan
Bibliographic record
Abstract
In Canada, colonisation, both historic and ongoing, increases Indigenous vaccine hesitancy and the threat posed by infectious diseases. This research investigated Indigenous vaccine hesitancy in a First Nation community in Saskatchewan, ways it can be overcome, and the influence of a colonial history as well as modernity. Research followed Indigenous research methodologies, a community-based participatory research design, and used mixed methods. Social media posts (interventions) were piloted on a community Facebook page in January and February (2022). These interventions tested different messaging techniques in a search for effective strategies. The analysis that followed compared the number of likes and views of the different techniques to each other, a control post, and community-developed posts implemented by the community's pandemic response team. At the end of the research, a sharing circle occurred and was followed by culturally appropriate data analysis (Nanâtawihowin Âcimowina Kika-Môsahkinikêhk Papiskîci-Itascikêwin Astâcikowina procedure). Results demonstrated the importance of exploring an Indigenous community's self-determined solution, at the very least, alongside the exploration of external solutions. Further, some sources of vaccine hesitancy, such as cultural barriers, can also be used to promote vaccine confidence. When attempting to overcome barriers, empathy is crucial as vaccine fears exist, and antivaccine groups are prepared to take advantage of empathetic failures. Additionally, the wider community has a powerful influence on vaccine confidence. Messaging, therefore, should avoid polarising vaccine-confident and vaccine-hesitant people to the point where the benefits of community influence are limited. Finally, you need to understand people and their beliefs to understand how to overcome hesitancy. To gain this understanding, there is no substitute for listening.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".