An exploration of the role of trust and rapport in enhancing vaccine uptake among Anishinaabe in rural northern Ontario
Bibliographic record
Abstract
This article examines the complicated terrain of immunization acceptance and access among Indigenous peoples in northern Ontario by drawing on conversations held prior to 2019 that explored knowledge about Haemophilus influenzae type a (Hia) infection specifically and attitudes toward vaccines more broadly. In the decade preceding COVID-19, Hia emerged as a leading cause of morbidity and mortality in Indigenous communities in northern Canada. Before developing new vaccines, it is imperative to hold conversations with the communities most affected and to learn more about Indigenous peoples' perceptions of and knowledge about vaccines, both generally and Hia specifically. We conducted focus groups and one-on-one conversations with Anishinaabe Peoples in northwestern Ontario. Our findings illustrate that decisions to vaccinate are informed by a host of social, institutional, and ideological factors and historical and contemporary relationships with government institutions and health practitioners. In particular, Indigenous community members perceived their relationships with social and health services and education institutions as coercive. Thus, public health approaches cannot continue to operate in ways that prioritize interventions for Indigenous peoples and communities so that they "do the right thing." More emphasis is needed on health service and social service provider knowledge, skills, attitudes and practices-redirecting the onus onto those within the health care system. Solutions must respect Indigenous nationhood and the right of self-determination. Finally, we suggest the term vaccine hesitancy may not entirely capture the breadth of experiences that many Indigenous Peoples and communities have and continue to have within the health care system in Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".