An Examination of Comorbid Generalized Anxiety Disorder and Chronic Pain on Substance Misuse in a Canadian Population-Based Survey
Bibliographic record
Abstract
OBJECTIVES: Chronic pain and generalized anxiety disorder (GAD) are co-occurring, and both conditions are independently associated with substance misuse. However, limited research has examined the impact of comorbid GAD and chronic pain on substance misuse. The aim of this article was to examine the associations between comorbid GAD and chronic pain conditions compared to GAD only with nonmedical opioid use, drug abuse/dependence, and alcohol abuse/dependence in a Canadian, population-based sample. METHODS: = 25,113). Multiple logistic regressions assessed the associations between comorbid GAD and chronic pain conditions (migraine, back pain, and arthritis) on substance misuse. RESULTS: Comorbid GAD + back pain and GAD + migraine were associated with increased odds of nonmedical opioid use compared to GAD only. However, the relationship was no longer significant after controlling for additional chronic pain conditions. No significant relationship was found between GAD + chronic pain conditions with drug or alcohol abuse/dependence. CONCLUSIONS: Comorbid GAD + back pain and GAD + migraine have a unique association with nonmedical opioid use in Canadians compared to GAD only, and chronic pain multimorbidity may be driving this relationship. Results emphasize the need for screening for substance misuse and prescription access in the context of GAD and comorbid chronic pain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".