Identity as visibility: perspectives and experiences of Indigenous Peoples in Manitoba with racial, ethnic, and indigenous identifier data collection within healthcare.
Bibliographic record
Abstract
Race, ethnicity, and Indigenous identity (REI) data collection, a strategy to address health and healthcare inequities, is emerging in Canada. Traditional teachings remind us to embrace our Indigenous identities. Yet, Indigenous identity within healthcare is often connected with racism, and mistreatment; an important context surrounding REI data. As Manitoba implements REI data collection at point of healthcare, reflecting on historical and current narratives surrounding REI data collection is beneficial. This dissertation advances collective knowledge of Indigenous understanding and experiences of REI data collection. As an Anishinaabekwe, researcher, and Manitoba REI team member, I bring experiential knowledge and relationships to this dissertation. Informed by the Contemporary Indigenous Empowerment Theory (CIET), this dissertation highlights relationships of power, social justice, and Indigenous identity/voices. A qualitative research design includes four methods identified below with key findings. Story-gathering with 20 self-identifying First Nations, Inuit, and Métis people in Manitoba opened space for Indigenous voices to inform our understanding of REI data collection and its impacts. Their meaning of Indigenous identity, experiences in healthcare, and perspectives on, and best practice for, REI data collection, informed how self-declaration of Indigenous identity is a highly complex matter. An environmental scan of REI data collection in Manitoba was guided by CIET to highlight relations of power, social justice, and Indigenous voices, deepening our knowledge of REI data collection. Creating awareness and evidence for health organizations, researchers, practitioners, patients, and the public to understand the current context of REI data collection in Manitoba. A scoping review examined how Indigenous identity, voice, and methodologies are upheld in health research. Findings suggest identity transparency of researchers and research participants requires strengthening and raising collective understanding and honouring of Indigenous research methodologies, ethical guidelines, and principles is imperative. Tying these three studies together, are my reflections on the use of ceremony and traditional knowledge throughout the research process. Indigenous identity empowerment is a pathway to healing. REI data collection within healthcare creates an opportunity to shift Indigenous identity from one of risk and harm to one of protective visibility and resistance against racism – if we are willing to do the relational work required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".