Culturally and structurally competent approaches to health research with Black communities in Atlantic Canada: a rapid review
Bibliographic record
Abstract
INTRODUCTION: Anti-Black racism is deeply entrenched in Canadian institutions and has deleterious impacts on Black populations. Black populations have resided in the Atlantic region since the late 17th century. Despite longstanding histories, Atlantic Black populations face significant inequities, including the highest rates of child poverty among Black children across Canada. Community consultations in Atlantic Canada have highlighted a desire to bring attention to these health inequities. The purpose of this review was to identify existing literature pertaining to Black health research in Atlantic Canada and highlight culturally appropriate practices. METHODS: The search strategy was developed with a librarian and focussed on health research pertaining to Black populations in the Atlantic provinces of Canada, covering eight databases. All articles were imported into Covidence for screening, with independent reviewers assessing titles, abstracts and full texts. RESULTS: Forty-seven studies met the inclusion criteria. Findings demonstrated the pervasiveness and impact of racism, the importance of community engagement as a key cultural consideration and the adoption of participatory action research frameworks as culturally appropriate. CONCLUSION: This review revealed opportunities for improving Black health research in Canada's Atlantic provinces. Future research warrants attention to this region and the use of culturally and structurally appropriate research approaches and methods. Recommendations include improved education on Black history and a training module within existing ethical guidelines for culturally and structurally competent research with Black communities.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.028 | 0.041 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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; 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".