The Prevalence of Chronic Pain in Canada
Bibliographic record
Abstract
Published population estimates of the prevalence of chronic pain have been highly variable due, in part, to differences in definitions and study methodologies. Designing health care delivery models that address chronic pain and reduce its impact, however, require accurate, up‐to‐date prevalence data. This article first reviews studies that examined the prevalence of chronic pain both internationally and in Canada. The ensuing sections describe a telephone‐based survey of a well‐defined population of adults using a detailed and sequential definition of chronic pain, and well‐validated and reliable data collection tools for establishing the prevalence of chronic pain in Canada. BACKGROUND: While chronic pain appears to be relatively common, published population prevalence estimates have been highly variable, partly due to differences in the definition of chronic pain and in survey methodologies. OBJECTIVES: To estimate the prevalence of chronic pain in Canada using clear case definitions and a validated survey instrument. METHODS: A telephone survey was administered to a representative sample of adults from across Canada using the same screening questionnaire that had been used in a recent large, multicountry study conducted in Europe. RESULTS: The prevalence of chronic pain prevalence for adults older than 18 years of age was 18.9%. This was comparable with the overall mean reported using identical survey questions and criteria for chronic pain used in the European study. Chronic pain prevalence was greater in older adults, and females had a higher prevalence at older ages compared with males. Approximately one‐half of those with chronic pain reported suffering for more than 10 years. Approximately one‐third of those reporting chronic pain rated the intensity in the very severe range. The lower back was the most common site of chronic pain, and arthritis was the most frequently named cause. CONCLUSIONS: A consensus is developing that there is a high prevalence of chronic pain within adult populations living in industrialized nations. Recent studies have formulated survey questions carefully and have used large samples. Unfortunately, a substantial proportion of Canadian adults continue to live with chronic pain that is longstanding and severe.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".