Crack across Canada: comparing crack users and crack non‐users in a Canadian multi‐city cohort of illicit opioid users
Bibliographic record
Abstract
AIMS: To examine possible differences between crack users and crack non-users across Canada. DESIGN: Cohort study of illicit opioid and other drug users in five cities across Canada. SETTING: Vancouver, Edmonton, Toronto, Montreal and Quebec City, Canada. PARTICIPANTS: Regular illicit opioid and other street drug users not in treatment at time of assessment. MEASUREMENTS: Participants (n = 677) were assessed at baseline (2002) by way of an interviewer-administered questionnaire, a psychiatric diagnostic instrument (Composite International Diagnostic Interview), and salivary antibody tests for infectious disease. FINDINGS: Approximately half the sample had used crack in the past 30 days, although prevalence rates differed strongly between study sites. When examined by discriminant analysis, crack users in the study population were more likely to have: no permanent housing, have illegal and sex work income, indicate physical health problems and hepatitis C virus (HCV) antibodies, use walk-in clinics, use heroin and to have been arrested and in detention (in past year). They were less likely to report depressive symptoms, and use Dilaudid (hydromorphone) and alcohol. CONCLUSION: These results illustrate crack users' pronounced social marginalization (as expressed by homelessness and high involvement in illegal activities) as well as extensive health problems compared to non-crack users in the Canadian context. The development of targeted interventions-addressing the dynamics of social marginalization-of this population is urgently needed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".