Comorbid Depression among Untreated Illicit Opiate Users: Results from a Multisite Canadian Study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe patterns of major depression (MDD) in a cohort of untreated illicit opiate users recruited from 5 Canadian urban centres, identify sociodemographic characteristics of opiate users that predict MDD, and determine whether opiate users suffering from depression exhibit different drug use patterns than do participants without depression. METHOD: Baseline data were collected from 679 untreated opiate users in Vancouver, Edmonton, Toronto, Montreal, and Quebec City. Using the Composite International Diagnostic Interview Short Form for Major Depression, we assessed sociodemographics, drug use, health status, health service use, and depression. We examined depression rates across study sites; logistic regression analyses predicted MDD from demographic information and city. Chi-square analyses were used to compare injection drug use and cocaine or crack use among participants with and without depression. RESULTS: Almost one-half (49.3%) of the sample met the cut-off score for MDD. Being female, white, and living outside Vancouver independently predicted MDD. Opiate users suffering from depression were more likely than users without depression to share injection equipment and paraphernalia and were also more likely to use cocaine (Ps < 0.05). CONCLUSIONS: Comorbid depression is common among untreated opiate users across Canada; targeted interventions are needed for this population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| 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".