Efforts to Introduce an Indigenous Identifier in a Canadian Provincial Health Authority
Bibliographic record
Abstract
Across Canada, Indigenous identity data is not routinely collected in healthcare settings. This paper focuses on [province] and whether the largest and only province-wide healthcare services organization in Canada, [healthcare organization name] should implement an Indigenous identifier in their new electronic medical record system. Due to the current absence of an Indigenous identifier, it is difficult to identify Indigenous peoples in administrative healthcare data to guide healthcare decision-making specifically for Indigenous peoples and to assist with population-specific disease surveillance and prevention programs. Moreover, it is often challenging for Indigenous nations, communities, and organizations to access this data. The data that does exist in [province] frequently excludes large segments of the Indigenous population. Indigenous researchers globally have called for improved health data in alignment with Indigenous research principles, data governance and sovereignty. This paper reports on an exploratory study of potential avenues to enhance availability of Indigenous specific health data in [province]. Of interest are possible supports, concerns, and potential barriers for the introduction of a policy to collect self-disclosed Indigenous identity data in the [org] new electronic medical record system. This research included interviews and focus group sessions with diverse subject matter experts including academics, Indigenous health practitioners and healthcare administrators. The project was embedded in the [removed] which is an [province] based research network for improving primary health with and for 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 | 0.060 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".