Bibliographic record
Abstract
Background: Understanding pain disparities is critical for fostering health equity and guiding effective health policies. However, little is known about racial/ethnic disparities in pain among adults in Canada. Aims: We provide a comprehensive analysis of racial/ethnic disparities in pain among Canadian adults, focusing on two dimensions of pain - frequent pain and interfering pain. Methods: We use two-wave cross-sectional data collected in 2020 and 2022 from a representative sample of 4,637 adults aged 18 and older residing in Canada. We calculate the prevalence of pain among White, Black, East/Southeast Asian, South Asian, Indigenous, Multiracial, and "Other" groups and estimate relative differences adjusted for key covariates in a multivariable framework. Results: The data reveal large and statistically significant pain disparities; specific patterns, however, vary across the two pain outcomes and by gender. Indigenous Canadians have relatively high prevalence of both frequent pain (38.4%) and interfering pain (27.8%), while East/Southeast Asian Canadians have the lowest prevalence of both (8.2% and 14.4%, respectively). Black Canadians have a relatively low prevalence of frequent pain (16.9%) but a very high prevalence of interfering pain (27.8%). Covariates are associated with pain levels but less so with the racial/ethnic patterns in pain. Conclusions: Our analysis highlights substantial racial/ethnic disparities in pain prevalence among Canadian adults. Further research is essential to better understand the root causes of the observed disparities and ultimately improve the lives of Canadians living with pain.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".