Practices for collecting, analyzing and disseminating data on health and its social determinants among Black populations in Quebec: a scoping review
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic highlighted the deficiencies in healthcare systems both within and outside of Canada, affecting racialized populations, particularly Black communities, who face an increased risk of infection and mortality from the disease. Although Black populations in Quebec make up more than 25% of the Black population in Canada, detailed data on the impact of COVID-19 on these communities are only available at the national level. This scoping review documents the methods and issues related to the collection, analysis and dissemination of data on the health of Black populations in Quebec, and its social determinants. METHODS: We conducted a review of studies published in English and French from January 2010 to June 2024 by consulting six databases. This review exclusively comprised studies involving data collection from racialized populations, including Black populations in Quebec, and excluded Canada-wide studies involving only a subsample of Black populations in Quebec. The main keywords used were: "data on race", "ethnic data collection", "race data collection", "culturally appropriate", "health", "survey", "questionnaire", "racial groups", "racialized groups", "Black and minority ethnic people", "people of colour", "migrants", "Quebec", "collecte de données", "minorité", "noir" and "ethnicité". RESULTS: We selected 43 studies covering four sectors: health, social services, education and employment. We identified the main issues, methods and strategies used to recruit members of Black communities and to collect and analyze data according to ethnoracial categories while minimizing bias to better understand the sociocultural and socioeconomic context of the target populations. CONCLUSION: Our review highlights the importance of collecting data on racialized groups, particularly Black communities in Quebec, to support public policies aimed at promoting health equity.
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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 | 0.093 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.054 | 0.063 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| 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; 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".