My place, your place, or a safer place : the intention among Montreal injecting drug users to use supervised injecting facilities
Bibliographic record
Abstract
BACKGROUND: Supervised injection facilities (SIF), a harm reduction intervention, may reduce several risks of public injection drug use. The prospect of conducting a scientific, multi-site pilot project of these facilities is being explored at federal and local levels in Canada. Experiences with SIF in Europe and Australia indicate that successful outcomes for the community ultimately hinge upon the responsiveness and relevance of the facilities to the needs of their primary target group: people who inject drugs in public places. Consideration of the factors and conditions found to influence a potential user's uptake of SIF, therefore, is imperative. This study sought to assess the acceptability of SIF and to determine factors associated with willingness of injecting drug users (IDU) to use SIF in a city considering their establishment. METHODS: From April 2001 to February 2002, following key informant interviews, a cross-sectional study was conducted among publicly injecting IDU participating in an ongoing HIV surveillance study in Montréal. Univariate and bivariate analyses preceded logistic regression. RESULTS: Participants were 11 key informants and 251 publicly injecting IDU. Key informants generated the Montréal-specific SIF model subsequently presented to IDU. 76% of IDU were willing to use at least one of three proposed SIF sites. Exploratory multivariable models indicated drug-use characteristics and SIF attributes as determinants of outcome: predominant cocaine injection, history of overdose, knowing about SIF, relieving and empowering feelings toward using SIF, and comfort with disclosure of one's injecting drug use. CONCLUSION: User consultations are essential to assess relevance and plan SIF acceptable to IDU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".