Anti-Indigenous Racism and its Impacts in the Oral Health Care of Indigenous Women, Two-Spirit, Transgender, and Gender-Diverse Peoples in Canada
Bibliographic record
Abstract
Oral health care is just one example of the pervasive impacts of Anti-Indigenous Racism (AIR) on various facets of Indigenous health, with Indigenous Peoples in Canada experiencing poorer oral health than their non-Indigenous counterparts. However, there is limited investigation into the specific oral health experiences of Indigenous Women, Two-Spirit, Transgender, and Gender Diverse (IW2STGD+) Peoples. To address this gap, a mixed-methods research study was conducted. The study engaged IW2STGD+ Peoples in exploring their oral health care experiences, focusing on racism and discrimination. Surveys, along with virtual and in-person Sharing Circles, were conducted with IW2STGD+ individuals and oral healthcare providers across Turtle Island and Inuit Nunangat. Engagements with IW2STGD+ Peoples and oral healthcare providers yielded insights into shortcomings, accessibility issues, and service needs related to oral health care. The research study resulted in the development of recommendations and potential metrics for success aimed at enhancing the overall experience of IW2STGD+ individuals with oral health care needs. Aligned with the Calls to Action outlined by the Truth and Reconciliation Commission of Canada, addressing AIR in oral health care education, access, and delivery remains a crucial component of advancing reconciliation with Indigenous Peoples.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| 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".