Prevalence and factors related to type 2 diabetes among Black individuals in Canada: A systematic review and meta-analysis
Bibliographic record
Abstract
This study examines the prevalence and factors associated with diabetes among Black individuals in Canada, compared to other racial groups. A comprehensive search strategy was executed across ten databases: MEDLINE, APA PsycInfo, CBCA, CINAHL, Scopus, Cochrane CENTRAL, CPI. Q, Embase, Érudit, and Web of Science. A random effects meta-analysis and narrative review were performed. The meta-analysis included 11 studies comprising 83,942 Black individuals, finding a pooled diabetes prevalence of 6.3 %. No significant gender or racial-ethnic group differences were observed. However, when compared directly to other racial group, Black individuals were more likely to have diabetes compared to White (OR = 1.29) and Asian (OR = 1.16) participants. The prevalence was unaffected by moderators such as age, gender or publication year. A narrative review highlighted associations with gender, socioeconomic status, immigration status, and healthcare utilization. Findings highlight a higher diabetes prevalence among Black individuals compared to White and Asian populations and significant research gaps. Key areas for future study include examining the impact of social determinants like racial discrimination, immigration status, and socioeconomic factors on diabetes risk. Additionally, classifying data by ethnicity within Canadian populations and focusing on healthcare quality are essential to developing targeted prevention and intervention strategies for 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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".