A socio-structural approach to preventing injection drug use initiation : rationale for the PRIMER study
Bibliographic record
Abstract
Background Injection drug use remains a primary driver of HIV and HCV-related harms globally. However, there is a gap in efforts to prevent individuals from transitioning into injecting. People who inject drugs (PWID) play a key role in the transition of others into injecting, and while behavioral interventions have been developed to address this phenomenon, socio-structural approaches remain unexplored. To that end, we hypothesize that certain interventions designed to reduce injecting-related risk behaviors may also reduce the risk that PWID expose and introduce others into injecting. Identifying the preventive potential of existing interventions will inform broader efforts to prevent injecting and related harms. Methods The Preventing Injecting by Modifying Existing Responses (PRIMER) study is a multi-country mixed methods study with an aim to investigate whether specific interventions (e.g., opioid substitution therapy, supervised injection facilities, stable housing, incarceration environments) and related factors (e.g., public injecting and gender) influence the likelihood that PWID initiate others into injecting. This study will (1) investigate the PWID participation in injection initiation; (2) identify factors influencing the risk that PWID expose others to or facilitate injection initiation; (3) describe drug scene roles that increase the risk of PWID facilitating injection initiation; and (4) evaluate the impact of structural, social, or biomedical interventions on the risk that PWID facilitate injection initiation. It does so by pooling observational data from cohort studies of PWID in six cities: Vancouver, Canada; San Diego, USA; Tijuana, Mexico; Paris, Marseille, and Bordeaux, France. Results Team members are conducting a prospective, multi-site study of PWID (n = 3050) in North America and France that includes quantitative and qualitative data collection through four separate cohort studies of PWID (San Diego, STAHR II; Tijuana, El Cuete IV; Vancouver, V-DUS; Bordeaux, Marseille, Paris and Strasbourg, COSINUS). Conclusions PRIMER is the largest study of injection initiation to date and the first to investigate structural approaches to preventing injection drug use initiation. Findings have the potential to inform the development and scale up of new and existing interventions to prevent transitions into injecting.
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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 | 0.099 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".