Effet de l’implantation d’une clinique de prescription médicale d’héroïne sur l’environnement communautaire
Bibliographic record
Abstract
Objectives: Heroin-assisted treatment clinics are currently the focus of a social debate. In spite of the clinics’ effectiveness in reducing illicit opiate use, some decision-makers refuse to set them up because of their possible negative impact. These clinics could attract new drug users into the neighbourhood which could lead to an accumulation of injection debris and to a decrease in the sense of security in the community. This study assesses the impact on some elements observed in the surrounding community following the opening of the Montreal heroin-assisted treatment clinic. Methods: Ethnographical walks were taken in order to collect data on the amount of injection debris, street debris and deviant activities. Data were then aggregated on a daily basis and our interrupted time series were analyzed using the segmented regression methodology. Results: Results show that the opening of the clinic was followed by a significant drop in the amount of injection and street debris. This reduction appears proportional to the number of clinic participants. Conclusion: The Montreal heroin-assisted treatment clinic did not produce any negative impact on the surrounding community. In fact, implementation of this kind of clinic was followed by a positive effect on the neighbourhood. In addition to the ethnographical walks, further studies should include surveys in order to estimate the impact of heroin-assisted treatment clinics on residents’ sense of security. Even if there appears to be a positive impact, it is possible that the mere presence of these clinics negatively affects community members’ sense of security. Key words: Heroin-assisted treatment; impact; community; Montreal
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".