Impact of the Opioid Epidemic and Associated Prescribing Restrictions on People Who Live With Chronic Noncancer Pain in Canada
Bibliographic record
Abstract
OBJECTIVES: Little is known about the consequences of the opioid epidemic on people living with chronic noncancer pain (CNCP). This study examined this issue in people who lived in the most impacted province by opioid overdoses in Canada (British Columbia [BC]) or one of the least impacted (Quebec [QC]), and examined the factors associated with opioid use. MATERIALS AND METHODS: This cross-sectional study was carried out in adults living in BC (N=304) and QC (N=1071) who reported CNCP (≥3 months) and completed an online questionnaire that was tailored to their opioid status. RESULTS: Almost twice as many participants in BC as in QC were proposed to cease their opioid medication in the past year (P<0.001). The proportion who reported having hoarded opioids in fear of not being able to get more in the future was also significantly higher in BC (P<0.001) compared with QC. In addition, they were significantly more likely to have had their opioid dose decreased than those in QC (P=0.001). No significant association was found between opioid discontinuation and province of residence. Two-thirds of the BC participants felt that the media coverage of the opioid crisis was very to extremely detrimental to CNCP patients in general, this percentage being significantly higher than in QC (P<0.001). DISCUSSION: The opioid epidemic and associated prescribing restrictions have had harmful effects on Canadians with CNCP. The clinical community, the general public, and the media need to be aware of these negative consequences to decrease patients' stigmatization and minimize inadequate treatment of CNCP.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".