Potential community and public health impacts of medically supervised safer smoking facilities for crack cocaine users
Bibliographic record
Abstract
There is growing evidence of the public health and community harms associated with crack cocaine smoking, particularly the risk of blood-borne transmission through non-parenteral routes. In response, community advocates and policy makers in Vancouver, Canada are calling for an exemption from Health Canada to pilot a medically supervised safer smoking facility (SSF) for non-injection drug users (NIDU). Current reluctance on the part of health authorities is likely due to the lack of existing evidence surrounding the extent of related harm and potential uptake of such a facility among NIDUs in this setting. In November 2004, a feasibility study was conducted among 437 crack cocaine smokers. Univariate analyses were conducted to determine associations with willingness to use a SSF and logistic regression was used to adjust for potentially confounding variables (p < 0.05). Variables found to be independently associated with willingness to use a SSF included recent injection drug use (OR = 1.72, 95% CI: 1.09-2.70), having equipment confiscated or broken by police (OR = 1.96, 95% CI: 1.24-2.85), crack bingeing (OR = 2.16, 95% CI: 1.39-3.12), smoking crack in public places (OR = 2.48, 95% CI: 1.65-3.27), borrowing crack pipes (OR = 2.50, 95% CI: 1.86-3.40), and burns/inhaled brillo due to rushing smoke in public places (OR = 4.37, 95% CI: 2.71-8.64). The results suggest a strong potential for a SSF to reduce the health related harms and address concerns of public order and open drug use among crack cocaine smokers should a facility be implemented in this setting.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".