Voluntary self-disclosed Indigenous identity of patients in four Canadian health care settings: A multiple-site qualitative case study
Bibliographic record
Abstract
OBJECTIVES: The lack of Indigenous health care data in Canada makes it challenging to plan health care services and inform Indigenous leadership on the health care needs of their respective Nations and communities. Several Canadian health care organizations have implemented a voluntary Indigenous identifier of patients within their electronic medical records. This study examines facilitators and barriers to implementing such a voluntary self-reported Indigenous identifier, from the perspective of key stakeholders who work at four Canadian health providers where an Indigenous identifier has been implemented. METHODS: The four Canadian sites comprise three hospitals and one health authority. At each site, key stakeholders participated in semi-structured qualitative interviews. Interviews were transcribed and coded. Relevant documents that were publicly available or provided by each site were reviewed. RESULTS: There were four primary findings. First, for the introduction of an Indigenous identifier to be successful there must be pre-existing strong and trusting relationships between Indigenous communities and health care organizations. Second, health care organizations must provide training for those who ask clientele to self-identify as Indigenous, to overcome issues such as any patient backlash. Third, for the relationship between Indigenous people and health organizations to flourish, data governance must be Indigenous-led. Finally, the collection of Indigenous identifier data can enhance Indigenous health care services and health care service planning and delivery. CONCLUSIONS: Due to the ongoing distrust of government and health care services among Indigenous peoples and communities, special considerations are required prior to the implementation of an Indigenous identifier. Of primary importance is how health care organizations can contribute to Indigenous data governance and minimize potential harms associated with the collection of such data. The findings of this study can be used to guide other health care sites and Indigenous leaders aspiring for more robust health data by implementing voluntary Indigenous identity data collection.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".