Collecting Race-Based Data in Health Research: A Critical Analysis of the Ongoing Challenges and Next Steps for Canada
Bibliographic record
Abstract
The COVID-19 pandemic has had a global effect. The disproportionate impact on Indigenous peoples and racialized groups has brought ethical challenges to the forefront in research and clinical practice. In Canada, the Tri-Council Policy Statement (TCPS2), and specifically the principle of justice, emphasizes additional care for individuals “whose circumstances make them vulnerable”, including Indigenous and racialized communities. In the absence of race-based data to measure and inform health research and clinical practice, we run the risk of causing more harm and contributing to ongoing injustices. However, without an accepted framework for collecting, maintaining, and reporting race-based data in Canada, more guidance is needed on how to do this well. Importantly, a framework for collecting race-based data should build on existing guidance from Indigenous and other structurally marginalized communities, the TCPS2, recommendations from the World Health Organization, and involve relevant stakeholders. In this paper, we describe historical examples of unethical studies on Indigenous and racialized groups, discuss the challenges and potential benefits of collecting race-based data, and conclude with objectives for a pan-Canadian framework to inform how race-based data is collected, stored, and accessed in health 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 | 0.316 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.053 | 0.036 |
| Scholarly communication | 0.041 | 0.011 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.008 | 0.027 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".