Prevalence and Correlates of Providing and Receiving Assistance With the Transition to Injection Drug Use
Bibliographic record
Abstract
Preventing the transition to injection drug use is an important public health goal, as people who inject drugs (PWID) are at high risk for overdose and acquisition of infectious disease. Initiation into drug injection is primarily a social process, often involving PWID assistance. A better understanding of the epidemiology of this phenomenon would inform interventions to prevent injection initiation and to enhance safety when assistance is provided. We conducted a systematic review of the literature to 1) characterize the prevalence of receiving (among injection-naive persons) and providing (among PWID) help or guidance with the first drug injection and 2) identify correlates associated with these behaviors. Correlates were organized as substance use behaviors, health outcomes (e.g., human immunodeficiency virus infection), or factors describing an individual's social, economic, policy, or physical environment, defined by means of Rhodes' risk environments framework. After screening of 1,164 abstracts, 57 studies were included. The prevalence of receiving assistance with injection initiation (help or guidance at the first injection) ranged 74% to 100% (n = 13 estimates). The prevalence of ever providing assistance with injection initiation varied widely (range, 13%-69%; n = 13 estimates). Injecting norms, sex/gender, and other correlates classified within Rhodes' social risk environment were commonly associated with providing and receiving assistance. Nearly all PWID receive guidance about injecting for the first time, whereas fewer PWID report providing assistance. Substantial clinical and statistical heterogeneity between studies precluded meta-analysis, and thus local-level estimates may be necessary to guide the implementation of future psychosocial and sociostructural interventions. Further, estimates of providing assistance may be downwardly biased because of social desirability factors.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".