A drug use survey among clients of harm reduction sites across British Columbia, Canada, 2012
Bibliographic record
Abstract
BACKGROUND: In British Columbia (BC), understanding of high-risk drug use trends is largely based on survey and cohort study data from two major cities, which may not be representative of persons who use drugs in other regions. Harm reduction stakeholders, representing each of the five geographic health regions in BC, identified a need for data on drug use to inform local and regional harm reduction activities across the province. The aims of this project were to (1) develop a drug use survey that could be feasibly administered at harm reduction (HR) sites across all health regions and (2) assess the data for differences in reported drug use frequencies by region. METHODS: A pilot survey focusing on current drug use was developed with stakeholders and administered among clients at 28 HR supply distribution sites across the province by existing staff and peers. Data were collated and analysed using univariate and bivariate descriptive statistics to assess differences in reported drug use frequencies by geography. A post-survey evaluation was conducted to assess acceptability and feasibility of the survey process for participating sites. RESULTS: Crack cocaine, heroin, and morphine were the most frequently reported drugs with notable regional differences. Polysubstance use was common among respondents (70%) with one region having 81% polysubstance use. Respondents surveyed in or near their region's major centre were more likely to report having used crack cocaine (p < 0.0001) and heroin (p < 0.0001) in the past week as compared to those residing >50 km from the major centre. Participants accessing services >50 km from the regional centre were more likely to have used morphine (p < 0.0001). There was no difference in powder cocaine use by client/site proximity to the regional centre. Participating sites found the survey process acceptable, feasible to administer annually, and useful for responding to client needs. CONCLUSIONS: The survey was a feasible way for harm reduction sites across BC to obtain drug use data from clients who actively use drugs. Drug use frequencies differed substantially by region and community proximity to the regional centre, underlining the need for locally collected data to inform service planning.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".