Indigenous Peoples Living with Multiple Sclerosis in Canada
Bibliographic record
Abstract
Indigenous Peoples in Canada are comprised of First Nations, Inuit and Métis and are the youngest and fastest growing population in the country. However, there is limited knowledge of how they are affected by multiple sclerosis (MS), the most common nontraumatic neurological disease of young adults, with Canada having one of the highest prevalences in the world. In this narrative review, we outline the limited studies conducted with Indigenous Peoples living with MS in Canada and the gaps in the literature. From the limited data we have, the prevalence of MS in Indigenous Peoples is lower, but the disease appears to be more aggressive. Given the dearth of Canadian data, we explore the worldwide MS studies of Indigenous populations. Lastly, we explore ways in which we can improve our understanding of MS among Indigenous Peoples in Canada, which entails building trust and meaningful relationships with these communities and acknowledging past and ongoing injustices. Furthermore, healthcare professionals conducting research with Indigenous Peoples should undergo training in cultural safety and data sovereignty, including principles of ownership, control, access and possession to have greater engagement with Indigenous communities to conduct more relevant research. With joint efforts between healthcare professionals and Indigenous communities, the scientific research community can be positioned to conduct better, more appropriate and desperately needed research, ultimately with improvements in the delivery of care to Indigenous Peoples living with MS 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".