Drug use patterns in the presence of crack in downtown Montréal
Bibliographic record
Abstract
INTRODUCTION AND AIMS: A study was undertaken to verify reports of an increasing presence of crack in downtown Montréal, and to investigate the influence of crack availability on current drug use patterns among street-based cocaine users. DESIGN AND METHODS: The study combined both qualitative and quantitative methods. These included long-term intensive participant observation carried out by an ethnographer familiar with the field and a survey. The ethnographic component involved observations and unstructured interviews with 64 street-based cocaine users. Sampling was based on a combination of snowballing and purposeful recruitment methods. For the survey, structured interviews were conducted with a convenience sample of 387 cocaine users attending HIV/HCV prevention programs, downtown Montréal. RESULTS: A gradual shift has occurred in the last 10 years, with the crack street market overtaking the powder cocaine street market. Although the data pointed to an increase in crack smoking, 54.5% of survey participants both smoked and injected cocaine. Drug market forces were major contributing factors to the observed modes of cocaine consumption. While the study focused primarily on cocaine users, it became apparent from the ethnographic fieldwork that prescription opioids (POs) were very present on the streets. According to the survey, 52.7% of participants consumed opioids, essentially POs, with 88% of them injecting these drugs. DISCUSSION AND CONCLUSIONS: Despite the increased availability of crack, injection is still present among cocaine users due at least in part to the concurrent increasing popularity of POs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".