Chronic Pain in Canada: Have We Improved Our Management of Chronic Noncancer Pain?
Bibliographic record
Abstract
BACKGROUND: Chronic noncancer pain (CNCP) is a global issue, not only affecting individual suffering, but also impacting the delivery of health care and the strength of local economies. OBJECTIVES: The current study (the Canadian Chronic Pain Study II [CCPSII]) was designed to assess any changes in the prevalence and treatment of CNCP, as well as in attitudes toward the use of strong analgesics, compared with a 2001 study (the CCPSI), and to provide a snapshot of the current standards of care for pain management in Canada. METHODS: Standard, computer-assisted telephone interview survey methodology was applied in two segments, ie, a general population survey and a survey targeting randomly selected primary care physicians (PCPs) who treat moderate to severe CNCP. RESULTS AND DISCUSSION: The patient-reported prevalence of CNCP within Canada has not markedly changed since 2001 but the duration of suffering has decreased. There have been minor changes in regional distribution and generally more patients receive medical treatment, which includes prescription analgesics. Physicians continue to demonstrate opiophobia in their prescribing practices; however, although this is lessened relating to addiction, abuse remains an important concern to PCPs. Canadian PCPs, in general, are implementing standard assessments, treatment approaches, evaluation of treatment success and tools to prevent abuse and diversion, in accordance with guidelines from the Canadian Pain Society and other pain societies globally, although there remains room for improvement and standardization.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".