Peer-Assisted Injection as a Harm Reduction Measure in a Supervised Consumption Service: A Mixed Method Study of Client Experiences
Bibliographic record
Abstract
Abstract Background: Peer assistance is an emerging area of study in injection drug use. When Canada’s first supervised consumption site (SCS) opened in 2003 in Vancouver, BC, clients were prohibited from injecting their peers; only recently has this practice been introduced as a harm reduction measure at these sites. In 2018, Health Canada granted federal exemption to allow peer-assisted injection at certain SCS sites, under the Controlled Drugs and Substances Act. Literature pertaining to peer-assisted injection addresses several topics: interpersonal relationships between the injector and recipient; the roles of ritual and pragmatism; trust and assessment of expertise; and gender relations.Methods: In this explanatory sequential mixed-methods study, participants were interviewed about their experiences in a peer-assisted injection program (PAIP) at one SCS regulated by Health Canada. Quantitative administrative data was used to provide context and to describe the study sample in comparison to all PAIP participants. Results: PAIP clients made up only 17.4% of all SCS clients; however, 71% of all SCS visits and 83% of all overdoses occurred among PAIP clients indicating their high service utilization. The PAIP program was utilized infrequently (0.4% of all SCS visits) but was a valuable service as expressed by Program participants. Participants expressed being moved by compassion to help others inject. While their desire to assist was pragmatic, they felt a significant burden of responsibility for the outcomes. Other prominent factors were social connection, trust, safety, social capital, and reciprocity. Participants also made suggestions for improving the PAIP. Conclusions: These findings reveal the humanity within a cohort of at-risk individuals, often dehumanized at the societal level. Relational equity and mutuality were evident, in contrast to other studies. Regular use of the SCS, and access to its resources, enabled participants to make healthier choices and practise lower-risk injections. At the federal level, there is considerable room to advocate for allowing clients to divide drugs safely within the SCS, and to increase capacity for safer alternatives such as inhalation.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".