Incidence, factors, and disparities related to cancer among Black individuals in Canada: A scoping review
Bibliographic record
Abstract
BACKGROUND: In Canada, two of five individuals will be diagnosed with cancer in their lifetime and one in four will die from this disease. Given the disparities observed in health research among Black individuals, we conducted a scoping review to analyze the state of cancer research in Canadian Black communities regarding prevalence, incidence, screening, mortality, and related factors to observe advances and identify gaps and disparities. METHODS: A comprehensive search strategy was developed and executed in December 2021 across 10 databases (e.g., Embase). Of 3451 studies generated by the search, 19 were retained for extraction and included in this study. RESULTS: Studies were focused on a variety of cancer types among Black individuals including anal, breast, cervical, colorectal, gastric, lung, and prostate cancers. They included data on incidence, stage of cancer at diagnosis, type of care received, diagnostic interval length, and screening. A few studies also demonstrated racial disparities among Black individuals. This research reveals disparities in screening, incidence, and quality of care among Black individuals in Canada. CONCLUSIONS: Given the gaps observed in cancer studies among Black individuals, federal and provincial governments and universities should consider creating special funds to generate research on this important health issue. PLAIN LANGUAGE SUMMARY: Important gaps were observed on research on cancer among Black communities in Canada. Studies included in the scoping review highlights disparities in screening, incidence, and quality of care among Black individuals in Canada.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".