Does Gender Moderate the Relationship Between Chronic Pain and Substance Use Disorder? Insights From a National Canadian Population Survey
Bibliographic record
Abstract
Background Though it has been shown that men have a higher lifetime prevalence of substance use disorder and a lower prevalence of chronic pain than women, there is little research to date focusing on gender differences in the relationship between chronic pain and substance use disorder. This study examined whether gender moderates the relationship of chronic pain and substance use disorder. We also sought to examine the gender differences in the associations between specific pain types—arthritis, migraine, and back pain, and substance use disorder. Methods The data were drawn from the 2012 Canadian Community Health Survey-Mental Health (CCHS-MH 2012) with individuals aged 20 years and older living in the 10 Canadian provinces (N = 23,089). A two-level logistic mixed effects model was used to account for provincial differences. Results Our findings indicated gender moderated the association between arthritis as well as migraine, and substance use disorder. However, no moderation effect of gender on the relationship between back pain and substance use disorder was found. Specifically, the strength of the association between arthritis and substance use disorder was stronger among men (ORinteraction = 0.62, 95% CI: 0.53 to 0.73), whereas the strength of the association between migraine and substance use disorder was stronger among women (ORinteraction = 1.45, 95% CI: 1.18 to 1.79). In addition, geographical location was found to explain a small proportion (2.3%-2.4%) of the overall variance in SUD. Conclusions The results suggest that gender moderated the relations between arthritis as well as migraine, and substance use disorder, respectively. Treatment programs for pain and substance misuse might benefit from an approach tailored to gender differences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".