Differences between co-users of cocaine and crack among Canadian illicit opioid users
Bibliographic record
Abstract
Fragestellung: Es wurden Unterschiede zwischen Ko-Gebrauchern von Kokain und Crack in einer kanadischen Kohorte illegaler Opioid-Konsumenten (»OPICAN«) untersucht. Methodik: Kohortenteilnehmer wurden durch Schneeball-Methoden rekrutiert und mittels eines standardisierten Instrumenten-Protokolls befragt. Prävalenzraten verschiedener Substanzen sowie Unterschiede zwischen ausgewählten Indikatoren und den beiden Subgruppen wurden bivariat geprüft. Ergebnisse: Zirka die Hälfte der Studienteilnehmer indizierten Ko-Konsum von Crack beziehungsweise Kokain. Erstere Gruppe zeichnete sich primär durch sozioökonomische Marginalisierung, zweitere durch eine höhere Prävalenz von Depression aus. Schlussfolgerungen: Ko-Gebraucher von Opioiden mit Kokain und Crack in Kanada können als distinkte Subkulturen mit spezifischen Risikofaktoren angesehen werden. Ausgewählte Implikationen für Interventionen werden angesprochen. Objectives: Differences between cocaine and crack co-users at baseline in a cohort of illicit opioid users in five Canadian cities (»OPICAN«) were explored. Methods: Cohort subjects were recruited by snowball methods, and assessed through a standardised protocol. Drug-use prevalence rates and bivariate correlations between selected indicators and the two sub-groups were assessed. Results: Cocaine and crack co-use were prevalent in the study cohort (55 % each). Crack co-users were primarily characterised by socio-economic marginalization, whereas cocaine co-users presented a higher prevalence of depression. Conclusions: Canadian opioid users co-using crack or cocaine represent distinct sub-cultures with specific risk factors. Some selective implications for interventions are discussed.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".