Indigenous community engagement in administrative data research: Lessons from the Qanuinngitsiarutiksait study
Bibliographic record
Abstract
ObjectivesThe Qanuinngitsiarutiksait program of research was developed at the request of and in partnership with a group of Inuit Elders, to document patterns of service utilization (health, social services, housing, justice) of Inuit living in Manitoba, and traveling to Manitoba to access health services. MethodIn this retrospective cohort study, we used administrative data routinely collected by Manitoba agencies. Ethical oversight was provided by the university of Manitoba Ethics Board and by an Inuit organization located in Manitoba. We developed an algorithm to identify Inuit in administrative datasets. Inuit Elders were involved at every step, providing feedback on planned analyses by sharing stories related to the research questions to ensure that the direction of the data extraction resonated with their experience. In addition, analytical conferences were held with elders, to ensure that researchers’ understanding of the findings resonated with Elders. ResultsThe group of Elders (6) we worked with had a variety of strengths: some had served as members of parliament and were comfortable with data presentations; others could provide a wealth of knowledge around Inuit knowledge and experiences, but remain intimidated by data. At the onset, we spent time creating a protocol with Inuit elders to explore how they wanted information to be shared with them (graphs, infographics, stories). We settled for a variety of means to accommodate different skill sets and support their own development. Having stories informing planned analyses ensured that we focused on what was important to this community. Stories also provided invaluable context to our analyses, and informed the need for analytical refinement and supportive program development. ConclusionCanada has become a leader in requiring that Indigenous communities be actively engaged in research aiming to document their needs and concerns. Our project demonstrates that meaningful Indigenous engagement is essential and possible in all research.
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 | 0.334 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.038 | 0.033 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.011 | 0.034 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".