Race-based sampling, measurement and monitoring in health data: promising practices to address racial health inequities and their determinants in Black Canadians
Bibliographic record
Abstract
INTRODUCTION: Racial health inequities are explained by inequities in access to medicaladvice and treatment, and the physiological effects of inequities in material conditions and everyday life; however, Canadian evidence on racial health inequities is limited. This review describes promising practices in population survey methods and approaches that can strengthen sampling, measurement and monitoring of racial health inequities and determinants of health for population subgroups within Canada-particularly Black Canadians. METHODS: We employed three steps to identify promising practices in Canada's peer countries and their applicability to the Canadian context. First, we conducted a scan of websites based on prior knowledge of population-level health surveys and datasets. Second, we conducted a review of publications from 2010 to 2020 to identify any missed surveys and datasets. Third, we conducted a targeted review of Canadian population-level health surveys and data sources to identify challenges to and opportunities for implementing these promising practices. RESULTS: We identified 20 relevant surveys and data sources from the US, the UK, Australia and New Zealand. In several of Canada's peer countries, information on arealevel racial or ethnic concentration of residents is used to conduct targeted sampling strategies, increasing the non-White sample. Our search of the available Canadian datasets found that Canadian health surveys and administrative sources do not routinely incorporate these strategies. CONCLUSION: Canada could improve the measurement and monitoring of racial health inequities by applying enhanced sampling practices to collect racial data in surveys and improving procedures for administrative and other routinely collected data sources. There are also novel predictive methods being used to improve sampling of non-White groups, though further investigation of these methods is required.
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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 | 0.334 | 0.462 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".