Where we live matters: a comparison of chronic pain treatment between remote and non-remote regions of Quebec, Canada
Bibliographic record
Abstract
Objective Where a person lives is a recognized socioeconomic determinant of health and influences healthcare access. This study aimed to compare the pain treatment profile of persons with chronic pain (CP) living in remote regions to those living in non-remote regions (near or in major urban centers). Methods A cross-sectional study was performed among persons living with CP across Quebec. In a web-based questionnaire, participants were asked to report in which of the 17 administrative regions they were living (six considered “remote”). Pain treatment profile was drawn up using seven variables: use of prescribed pain medications, over-the-counter pain medications, non-pharmacological pain treatments, multimodal approach, access to a trusted healthcare professional for pain management, excessive polypharmacy (≥10 medications), and use of cannabis for pain. Results 1,399 participants completed the questionnaire (women: 83.4%, mean age: 50 years, living in remote regions: 23.8%). As compared to persons living in remote regions, those living in non-remote regions were more likely to report using prescribed pain medications (83.8% vs. 67.4%), a multimodal approach (81.5% vs. 75.5%), experience excessive polypharmacy (28.1% vs. 19.1%), and report using cannabis for pain (33.1% vs. 20.7%) (bivariable p < 0.05). Only the use of prescribed medications as well as cannabis remained significantly associated with the region of residence in the multivariable models. Discussion There are differences in treatment profiles of persons with CP depending on the region they live. Our results highlight the importance of considering remoteness, and not only rurality, when it comes to better understanding the determinants of pain management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.004 | 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".