The effect of depressive symptoms on pain in a substance-using population with persistent pain: a cross-sectional cohort study
Bibliographic record
Abstract
BACKGROUND: In light of the ongoing opioid overdose crisis, there is an urgent need for research on the impacts of mental health among people presenting with concurrent pain and substance use. This study examined the effect of depressive symptoms on pain severity and functional interference among people who use drugs (PWUD) during a community-wide overdose crisis. METHODS: From December 1st 2016 to December 31st 2018, 288 participants in two cohort studies of PWUD in Vancouver, Canada completed interviewer-administered questionnaires that included the Brief Pain Inventory and PROMIS Emotional Distress-Depression instruments. Generalized linear regression modelling (GLM) was used to examine the cross-sectional effect of depressive symptoms and other confounding factors on pain severity and interference. RESULTS: Moderate to severe depressive symptoms were significantly associated with greater pain-related functional interference (adjusted β = 1.24, 95% confidence interval [CI] = 0.33-2.15), but not significantly associated with greater average pain severity (adjusted β = 0.22, 95% CI = - 0.3 - 0.82), when controlling for confounding variables. Reported daily heroin use (adjusted β = 1.26, 95% CI = 0.47-2.05) and non-fatal overdose (adjusted β = 1.02, 95% CI = 0.08-1.96) were also significantly associated with greater pain-related functional interference. CONCLUSIONS: In a substance-using population, greater pain-related functional interference was positively associated with depressive symptoms as well as overdose and daily heroin use. These findings emphasize the need to address the functional impact of pain, mental health comorbidity, and high-risk substance use that may contribute to overdose and other harms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".