Factors Associated with Frequency of Recent Initiation of Others into Injection Drug Use Among People Who Inject Drugs in Los Angeles and San Francisco, CA, USA, 2016–17
Bibliographic record
Abstract
Objective: Drug injection initiation is often assisted by a person who injects drugs (PWID). How often PWID provide this assistance has not been examined. We examine frequency of injection initiation assistance and factors associated with high (4+) and low frequency (1–3) initiation assistance as compared to no initiation assistance among PWID. Methods: Participants were 979 Californian PWID. PWID were interviewed about providing injection initiation assistance in the last 6 months among other items. Multinomial regression analysis was used to examine factors associated with levels of frequency of injection initiation assistance. Results: Among participants, 132 (14%) had initiated 784 people into injection (mean = 5.94 [standard deviation = 20.13]; median = 2, interquartile range = 1,4) in the last 6 months. PWID engaged in high frequency initiation (26% of sample) assisted 662 new initiates (84% of total). Using multinomial regression analysis with no initiating as the referent group, we found that high frequency initiating was statistically associated with higher injection frequency, having a paying sex partner, taking someone to a shooting gallery, and providing injection assistance. Lower frequency initiation was statistically associated with having a paying sex partner, illegal income source, and providing injection assistance. Conclusion: Differences between high and low frequency initiators were not found. Sex work and assisting with drug injection were linked to initiating others. Individual-level interventions that reduce this behavior among PWID and structural interventions such as safe consumption sites and opioid medication treatments that interrupt the social process of injection initiation should be considered as ways to reduce injection initiations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".